Systems and methods for generating protocols embodying contrast agent and radiation dose management techniques

Abstract

Systems and methods for generating protocols useful for medical imaging studies. The method may include: receiving information about a subject's patient and information about a subject's imaging study; determining one or more risk factors specific to the subject's patient based on the information about the subject's patient and the information about the subject's imaging study; selecting two or more models, where the two or more models include at least one model for each of at least two aspects of the imaging study of the subject; and applying the two or more models to generate a baseline study protocol for the imaging study of the subject. The baseline research regimen is based at least on information about the subject patient and the one or more risk factors. The baseline study protocol includes parameters of at least two aspects of the subject imaging study.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 374,979, filed on September 8, 2022, entitled “Systems and Methods for Generating Protocols Embodying Contrast and Radiation Dose Management Techniques,” the disclosure of which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates to methods and techniques for selecting or generating study protocols for medical imaging procedures, and methods and techniques for modifying or optimizing standard protocols, initial protocols, or baseline protocols for specific patients and / or procedures in order to reduce risk to the patient, improve image quality, and / or improve workflow efficiency. Also provided are injection systems and fluid injectors that perform a fluid injection procedure for a specific patient according to a selected or modified patient protocol. Background Art

[0004] In many medical diagnostic and therapeutic procedures, a medical practitioner (such as a medical technician) injects one or more medical fluids into a patient. In recent years, a number of medical fluid delivery systems have been developed for pressurized injection of fluids (such as contrast agents (often referred to simply as "contrast agents"), (multiple) flushing agents (such as saline), and other medical fluids) for use in imaging procedures such as angiography, computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), positron emission tomography (PET), and other molecular imaging procedures such as single photon emission tomography (SPECT) or hybrid modalities such as PET / CT, PET / MRI, SPECT / CT, or SPECT / MRI. Typically, these medical fluid delivery systems (such as powered fluid injectors) are designed to deliver fluids via one or more injection protocols. An injection protocol may include one or more injections, each injection including one or more stages to enhance a region of interest in a patient's body during diagnostic imaging. Examples of powered fluid injectors capable of delivering such fluids via a user-programmable multi-stage injection protocol include StellantCT Infusion System and MRXperion MR Infusion System, both provided by Bayer HealthCare LLC.

[0005] There are several known risks and patient safety issues associated with imaging procedures involving the delivery of contrast media and ionizing radiation to a patient. For example, patient safety issues for contrast media injections may include one or more of the following: (i) prevention, detection, and minimization of extravasated material; (ii) minimization of acute adverse events in each of contrast naive patients and patients with known atopy or documented acute adverse events due to contrast media injection; (iii) prevention of contrast media-induced nephrotoxicity and / or post-contrast renal injury; and / or (iv) management of patients to prevent thyroid disorders, such as thyrotoxicosis (TX).

[0006] Extravasation can be an uncommon but significant problem in contrast-enhanced medical imaging procedures, especially during nuclear medicine imaging procedures or when large amounts of contrast media are involved. Extravasation occurs when contrast media to be delivered to the central circulation via peripheral vascular access instead enters peripheral tissues (e.g., when contrast media escapes the vascular lumen during injection and infiltrates into interstitial tissues, etc.). The incidence of intravenous contrast media extravasation is generally reported to be less than 1% and is not directly related to the injection flow rate. However, some patients with extravasation may remain asymptomatic, while other patients may report swelling, tightness, tingling, or burning, and may exhibit edema, erythema, or tenderness at the injection site. Serious complications of extravasation include compartment syndrome, skin ulcers, and / or tissue necrosis.

[0007] Acute adverse events depend on the substance used. The incidence of acute adverse events with low-osmolar iodinated contrast agents is approximately 0.2%-0.7%, and the incidence of severe acute reactions is approximately 0.04%. The incidence of acute adverse events with gadolinium-based contrast agents (GBCAs) is low, occurring approximately once in every 10,000-40,000 injections. Most reactions are mild and transient, with skin reactions being the most common. Severe, life-threatening anaphylactic-like reactions to gadolinium-based contrast agents (GBCAs) are rare. Risk factors for acute adverse events with contrast agents may include previous reactions to iodinated contrast agents, severe allergies and reactions to medications and / or foods, a history of asthma, bronchospasm and / or atopy, a history of heart or kidney disease, etc.

[0008] Contrast-induced nephrotoxicity can be defined as "a sudden deterioration in renal function (e.g., acute kidney injury, etc.) following recent intravascular administration of contrast media in the absence of another nephrotoxic event." Risk factors for contrast-induced nephrotoxicity can include hypertension, proteinuria, gout, and / or previous renal surgery or pre-existing chronic kidney disease (CKD). In patients with normal, stable renal function, the risk of contrast-induced nephrotoxicity is considered low. Similarly, post-contrast acute kidney injury is a general term used to indicate a sudden deterioration in renal function within 48 hours of intravascular administration of iodine-based contrast media.

[0009] In the case of iodinated contrast media application (which reflects the majority of contrast media use), patients with untreated Graves' disease and / or multinodular goiter and thyroid autonomy, the elderly, and patients living in areas where dietary iodine deficiency is common may increase the risk of thyrotoxicosis through excessive iodine absorption. In addition, the use of iodinated contrast media before any planned radioactive iodine imaging or treatment can reduce radioactive iodine uptake. Regarding the risk of contrast agent atoms or molecules, what is relevant is the total dose, such as milligrams of iodine or gadolinium, rather than the milliliters of fluid. The total milliliters (ml) of fluid delivered may pose a risk of fluid overload for some impaired patients. Examples include dialysis patients or patients with congestive heart failure. In these patients, the goal is to minimize the total amount of fluid given to the patient, including contrast media and saline flushing solutions.

[0010] For other imaging procedures, there is a risk if the imaging procedure cannot be performed in a timely manner. For example, if a patient presents with symptoms of a stroke, quickly determining the presence, type (hemorrhagic vs. ischemic), and extent of the stroke may have a significant impact on the patient's ability to survive, i.e., "time is brain." In this case, the priority is to obtain adequate diagnostic images as quickly as possible, and to use a protocol that can be set up and executed efficiently and quickly, despite potential trade-offs in contrast agent dose used, image quality, spatial resolution, or other parameters. An additional risk is that the patient undergoes an imaging study, receives contrast agent and in some cases a radiation dose, and the study is non-diagnostic for one of many reasons, such as patient movement, poor timing between injection and study, the procedure itself is too complex for the technician to perform it correctly, the imaging system cannot perform the selected protocol as desired, and many other reasons.

[0011] Developed schemes for imaging procedures in order to mitigate risk, avoid patient discomfort, improve image quality and optimize workflow efficiency and cost. Typically, schemes are developed by analyzing clinical trial data of past medical imaging procedures to identify possible changes to existing procedures that will improve compliance with guidelines and standards for patient safety. In order to evaluate the efficacy of the proposed changes in medical technology, technology and standards, technicians (such as doctors) must perform procedures, use new technologies and / or test new standards on multiple patients in multiple multi-site clinical trials. Naturally, those trials must include a control group for appropriately evaluating medical technology, technology or standards. After the clinical trial, the technician or doctor usually describes and publishes his or her findings in a suitable medical journal. In addition, the doctor can present their findings to peers at medical conferences. As can be easily understood, this process may usually take several years. In addition, the huge scale of this business usually means only pursuing the most worthwhile medical technology, technology and / or establishing the most beneficial standard efforts. In addition, the huge costs associated with research prohibit most technicians and doctors from testing any technology or equipment or establishing new standards without the help of large companies and research organizations, which have sufficient financial resources to fund these activities.

[0012] The medical injection scheme that can be used to deliver drugs, therapeutic agents, imaging agents, contrast agents or another composition to patients can be identified, tested and finally adopted using the testing, publishing and presentation process described herein. For example, when performing a diagnostic assessment involving the use of a medical injector in combination with a scanning device (such as a CT (computed tomography) or MRI (magnetic resonance imaging) scanner), it may be a widely accepted practice to inject contrast agents into the patient at a standard rate, which is sufficient to provide images of sufficient quality for diagnostic assessment for all or substantially all patients without exposing standard patients to unreasonable risk levels or patient discomfort. Examples can be found in the American College of Radiology Contrast Agents Manual. Standard injection rates are generally determined or established using clinical trials and test methods described herein. It is worth noting that a widely accepted "standard rate" will be expected to provide sufficient levels of enhancement for most or all patients, while at the same time, not causing risk or causing a reasonably low level of risk for any patient.

[0013] Some physicians may adjust accepted standard protocols to take into account the capabilities of new equipment, or otherwise adapt protocols to changing circumstances to improve image quality and / or reduce risk to patients. However, other practitioners may continue to use established protocols (e.g., acceptable contrast agent flow rates) despite technological advances simply because the flow rates fall within the standards established for a particular diagnostic technique. As described above, any effort to modify or establish new standards may be time consuming and expensive. Therefore, in some cases, existing standards used by physicians and technicians may not take into account recent changes in technology or understanding of patient risks associated with medical imaging procedures.

[0014] Various systems and methods are known in the art that can algorithmically generate a solution for fluid injection (e.g., a fluid injection solution for an angiography procedure) based on input provided by a user or detected by a system sensor. In particular, such a solution generation system typically receives various inputs, such as patient information, desired enhancements, etc., and generates outputs in the form of parameters that can form part or all of a study solution. In some cases, these algorithms can be used to update standard solutions to take into account changes in technology or differences between specific types of medical equipment. However, existing models are typically based on a small number of input values ​​and may not take into account many sources of risk or patient discomfort, as well as many ways to improve image quality and / or workflow. Therefore, there is a need in the art for more robust and complete methods and techniques for modifying or optimizing medical imaging solutions that address the unique risks of specific patients and confirm that images of sufficient quality can be obtained for patients. The techniques, methods, and systems disclosed herein are provided to address these problems. Summary of the invention

[0015] In some non-limiting aspects of the present disclosure, a system for generating a protocol that can be used for a medical imaging study is provided. The system includes one or more processors and a non-transitory computer-readable medium having instructions stored thereon. The instructions, when executed by the one or more processors, will cause the system to: receive information about a subject patient and information about a subject imaging study from one or more data sources; determine one or more risk factors specific to the subject patient based on the information about the subject patient and the information about the subject imaging study; select two or more models, wherein the two or more models include at least one model for each of at least two aspects of the subject imaging study; and apply the two or more models to generate a baseline study protocol for the subject imaging study, wherein the baseline study protocol is based at least on information about the subject patient and one or more risk factors, and wherein the baseline study protocol includes parameters for at least two aspects of the subject imaging study.

[0016] In another non-limiting aspect of the present disclosure, a method for generating a protocol that can be used for a medical imaging study is provided. The method includes: receiving information about a subject patient and information about the subject imaging study from one or more data sources; determining one or more risk factors specific to the subject patient based on the information about the subject patient and the information about the subject imaging study; selecting two or more models, wherein the two or more models include at least one model for each of at least two aspects of the subject imaging study; and applying the two or more models to generate a baseline study protocol for the subject imaging study, wherein the baseline study protocol is based at least on information about the subject patient and one or more risk factors, and wherein the baseline study protocol includes parameters for at least two aspects of the subject imaging study.

[0017] In another non-limiting aspect of the present disclosure, a method for generating a protocol that can be used for a medical imaging study is provided. The method includes: receiving information about a subject patient and a subject imaging study from one or more data sources; generating a baseline study protocol based on the information about the subject patient and the subject imaging study; determining one or more risk factors specific to the subject patient based on the information about the subject patient and the subject imaging study; and modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient. Modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient includes: performing an iterative process to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors, and generating a modified study protocol that will provide images of sufficient diagnostic quality.

[0018] In another non-limiting aspect of the present disclosure, a system for generating a protocol that can be used for a medical imaging study is provided. The system includes one or more processors and a non-transitory computer-readable medium having instructions stored thereon. The instructions, when executed by the one or more processors, will cause the system to: receive information about a subject patient and a subject imaging study from one or more data sources; generate a baseline study protocol based on the information about the subject patient and the subject imaging study; determine one or more risk factors specific to the subject patient based on the information about the subject patient and the subject imaging study; and modify the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient. Modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient includes: performing an iterative process to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors, and generating a modified study protocol that will provide images of sufficient diagnostic quality.

[0019] Various aspects of the present disclosure may be further characterized by one or more of the following clauses:

[0020] Item 1. A system for generating a protocol that can be used for a medical imaging study, comprising: one or more processors; a non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by the one or more processors, will cause the system to: receive information about a subject patient and information about an imaging study of the subject from one or more data sources; determine one or more risk factors specific to the subject patient based on the information about the subject patient and the information about the imaging study of the subject; select two or more models, wherein the two or more models include at least one model for each of at least two aspects of the imaging study of the subject; and apply the two or more models to generate a baseline study protocol for the imaging study of the subject, wherein the baseline study protocol is based at least on the information about the subject patient and the one or more risk factors, and wherein the baseline study protocol includes parameters for at least two aspects of the imaging study of the subject.

[0021] Clause 2. A system according to clause 1, wherein the baseline study plan includes a contrast agent injection plan and an image acquisition plan, the contrast agent injection plan includes at least a total contrast agent dose and a maximum flow rate, and the image acquisition plan includes at least scanning parameters, scanning duration, timing parameters for coordinating with contrast agent injection, and one or more image reconstruction algorithms.

[0022] Clause 3. A system according to clause 1 or 2, wherein the two or more models comprise multiple models of an aspect of an imaging procedure, wherein the two or more models are configured to operate in parallel to transform the same or similar inputs.

[0023] Clause 4. A system according to any of the above clauses, wherein the two or more models are applied in a sequence, wherein the sequence is determined based on a desired optimization of at least one or more patient characteristics and the one or more risk factors.

[0024] Clause 5. The system of clause 4, wherein the system is configured to allow a user to accept or change the order based on the user's knowledge or preferences.

[0025] Clause 6. A system according to any of the above clauses, wherein the instructions, when executed by the one or more processors, will also cause the system to: perform one or more iterative loops through at least one of the two or more models to optimize one or more of the parameters of the baseline study protocol.

[0026] Clause 7. A system according to clause 6, wherein the instructions, when executed by the one or more processors, will further cause the system to: if none of the iterative loops provide optimized results, present the results of one or more of the iterative loops to a user in a selectable format.

[0027] Clause 8. The system according to any of the above clauses further comprises a user interface in which the expected parameters of the baseline study protocol are displayed for user confirmation or further adjustment.

[0028] Clause 9. The system of clause 8, wherein the user interface provides one or more selectable user interface elements that allow an operator to adjust the one or more risk factors specific to the subject patient.

[0029] Clause 10. The system of Clause 9, wherein at least one of the one or more selectable user interface elements is in the form of a slider that is adjustable by a user.

[0030] Clause 11. The system of clause 10, wherein the user interface is a graphical user interface display screen, and the one or more user interface elements are adjustable by the user touching the graphical user interface display screen.

[0031] Clause 12. A system according to any of the preceding clauses, wherein at least one of the two or more models relates to at least one of a fluid injection aspect of the subject imaging study and an image creation aspect of the subject imaging study.

[0032] Clause 13. The system of clause 12, wherein at least two of the two or more models relate to at least one of a fluid injection aspect of the subject imaging study and an image creation aspect of the subject imaging study.

[0033] Clause 14. A system according to clause 13, wherein a first model of the two or more models relates to fluid injection aspects of the subject imaging study and a second model of the two or more models relates to image creation aspects of the subject imaging study.

[0034] Clause 15. A system according to any of the above clauses, wherein the parameters include at least one of: total contrast agent volume, maximum flow rate, contrast agent delivery rate, average flow rate, contrast agent temperature, contrast agent viscosity, contrast agent concentration, IV access location, scanning area, potential applied to the X-ray tube, maximum current applied to the X-ray tube, scan speed, scan duration, radiation dose, signal-to-noise ratio, contrast agent / noise ratio, or spatial / resolution ratio.

[0035] Clause 16. A system according to any of the above clauses, wherein the information about the subject patient includes at least one of the following: height, weight, body mass index, cardiac output, gender, age, race, chest width, chest circumference, medications taken, underlying medical conditions, physical abilities, vital signs, pregnancy / expected pregnancy, genetic predisposition of the subject patient, allergies, results of previous imaging studies on the subject patient, and known radiation sensitivity of the subject patient.

[0036] Clause 17. A system according to any of the above clauses, wherein the one or more data sources include at least one of: an electronic medical record system including the patient's electronic medical record (EMR), an electronic health record (EHR) system, a patient procedure tracking system, a radiology analysis system (RAS), a digital pathology system (DPS), a picture archiving and communication system (PACS), a hospital data system, a hospital purchase order system including orders for studies to be performed on the subject patient, a database including previous scan results for the patient, a database including previous scan results for one or more other patients, or a government guideline database of acceptable radiation doses and contrast agent dose levels.

[0037] Clause 18. A system according to any of the above clauses, wherein the information about the subject imaging study includes information about a fluid injector associated with the subject imaging study, wherein the information about the fluid injector includes information from a test injection or patency check using saline, information about the capabilities and tolerances of the fluid injector, and / or the presence of an external sensor for monitoring injections performed by the fluid injector.

[0038] Clause 19. The system of any of the preceding clauses, wherein the one or more risk factors are related to at least one of contrast agent dose, radiation dose, extravasation risk, patient discomfort, anaphylactic shock risk, and image quality.

[0039] Clause 20. A system according to clause 6, wherein the one or more iterative loops optimize one or more parameters of the baseline study protocol by applying an algorithm that minimizes or maximizes selected parameter values, an algorithm for ensuring that certain parameters are within a target or threshold range, or a weighting function to the parameter values ​​of the baseline study protocol.

[0040] Item 21. A method for generating a protocol that can be used for a medical imaging study, comprising: receiving information about a subject patient and information about the subject imaging study from one or more data sources; determining one or more risk factors specific to the subject patient based on the information about the subject patient and the information about the subject imaging study; selecting two or more models, wherein the two or more models include at least one model for each of at least two aspects of the subject imaging study; and applying the two or more models to generate a baseline study protocol for the subject imaging study, wherein the baseline study protocol is based at least on the information about the subject patient and the one or more risk factors, and wherein the baseline study protocol includes parameters for at least two aspects of the subject imaging study.

[0041] Clause 22. A method according to Clause 21, wherein the baseline study plan includes a contrast agent injection plan and an image acquisition plan, the contrast agent injection plan includes at least a total contrast agent dose and a maximum flow rate, and the image acquisition plan includes at least scanning parameters, scanning duration, timing parameters for coordinating with contrast agent injection, and one or more image reconstruction algorithms.

[0042] Clause 23. The method of clause 21 or 22, wherein the two or more models comprise multiple models of an aspect of an imaging procedure, wherein the two or more models are configured to operate in parallel to transform the same or similar inputs.

[0043] Clause 24. The method of any one of clauses 21-23, wherein the two or more models are applied in a sequence, wherein the sequence is determined based on a desired optimization of at least one or more patient characteristics and the one or more risk factors.

[0044] Clause 25. The method of clause 24, wherein a user accepts or changes the order based on the user's knowledge or preferences.

[0045] Clause 26. The method of any one of clauses 21-25, further comprising: performing one or more iterative cycles through at least one of the two or more models to optimize one or more of the parameters of the baseline study protocol.

[0046] Clause 27. The method of clause 26, further comprising: presenting the results of one or more of the iterative loops to a user in a selectable format if none of the iterative loops provide an optimized result.

[0047] Clause 28. The method according to any one of clauses 21-27 further comprises: displaying expected parameters of the baseline study protocol on a user interface for user confirmation or further adjustment.

[0048] Clause 29. The method of any of clauses 21-28, wherein at least two of the two or more models relate to at least one of a fluid injection aspect of the subject imaging study and an image creation aspect of the subject imaging study.

[0049] Clause 30. The method of Clause 29, wherein at least two of the two or more models relate to at least one of the fluid injection aspect of the subject imaging study and the image creation aspect of the subject imaging study.

[0050] Clause 31. The method of clause 30, wherein a first model of the two or more models relates to fluid injection aspects of the subject imaging study and a second model of the two or more models relates to image creation aspects of the subject imaging study.

[0051] Clause 32. The method of any one of Clauses 21-31, further comprising: applying the baseline study protocol to perform the subject imaging study on the subject patient.

[0052] Clause 33. A method according to any one of clauses 21-32, wherein the parameters include at least one of: total contrast agent volume, maximum flow rate, contrast agent delivery rate, average flow rate, contrast agent temperature, contrast agent viscosity, contrast agent concentration, IV access location, scanning area, potential applied to the X-ray tube, maximum current applied to the X-ray tube, scan speed, scan duration, radiation dose, signal-to-noise ratio, contrast agent / noise ratio, or spatial / resolution ratio.

[0053] Clause 34. A method according to any one of clauses 21-33, wherein the information about the subject patient includes at least one of the following: height, weight, body mass index, cardiac output, gender, age, race, chest width, chest circumference, medications taken, underlying medical conditions, physical abilities, vital signs, pregnancy / expected pregnancy, genetic predisposition of the subject patient, allergies, results of previous imaging studies on the subject patient, and known radiation sensitivity of the subject patient.

[0054] Clause 35. A method according to any one of clauses 21-34, wherein the one or more data sources include at least one of: an electronic medical record system including the patient's electronic medical record (EMR), an electronic health record (EHR) system, a patient procedure tracking system, a radiology analysis system (RAS), a digital pathology system (DPS), a picture archiving and communication system (PACS), a hospital data system, a hospital purchase order system including orders for studies to be performed on the subject patient, a database including previous scan results for the patient, a database including previous scan results for one or more other patients, or a government guideline database of acceptable radiation doses and contrast agent dose levels.

[0055] Clause 36. A method according to any of clauses 21-35, wherein the information about the subject imaging study includes information about a fluid injector associated with the subject imaging study, wherein the information about the fluid injector includes information from a test injection or patency check using saline, information about the capabilities and tolerances of the fluid injector, and / or the presence of an external sensor for monitoring injections performed by the fluid injector.

[0056] Clause 37. The method of any of clauses 21-36, wherein the one or more risk factors are related to at least one of contrast agent dose, radiation dose, extravasation risk, patient discomfort, anaphylactic shock risk, or image quality.

[0057] Clause 38. A method according to Clause 26, wherein the one or more iterative loops optimize one or more parameters of the baseline study protocol by applying an algorithm that minimizes or maximizes selected parameter values, an algorithm for ensuring that certain parameters are within a target or threshold range, or a weighting function to the parameter values ​​of the baseline study protocol.

[0058] Clause 39. A method for generating a protocol that can be used for a medical imaging study, comprising: receiving information about a subject patient and a subject imaging study from one or more data sources; generating a baseline study protocol based on the information about the subject patient and the subject imaging study; determining one or more risk factors specific to the subject patient based on the information about the subject patient and the subject imaging study; and modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient, wherein modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient comprises: performing an iterative process to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors, and generating a modified study protocol that will provide images of sufficient diagnostic quality.

[0059] Clause 40. A method according to clause 39, wherein the baseline and modified study protocols include parameter values ​​for at least one of the following: total contrast agent volume, maximum flow rate, contrast agent delivery rate, average flow rate, contrast agent temperature, contrast agent viscosity, contrast agent concentration, IV access location, scanning area, potential applied to the X-ray tube, maximum current applied to the X-ray tube, scan speed, scan duration, radiation dose, signal-to-noise ratio, contrast agent / noise ratio, or spatial / resolution ratio.

[0060] Clause 41. A method according to clause 39 or 40, wherein the medical imaging study comprises at least one of computed tomography (CT) imaging, magnetic resonance (MR) imaging, nuclear medicine, PET, SPECT, ultrasound, thermal imaging, infrared (IR) imaging, or a combination thereof.

[0061] Clause 42. The method of clause 41, wherein the combination comprises a thermal / IR study; a PET / CT imaging study; a PET / MR imaging study; a SPECT / CT elastography study or a study comprising optical and X-ray imaging.

[0062] Clause 43. A method according to any one of clauses 39-42, wherein the information about the subject patient includes at least one of: height, weight, body mass index, cardiac output, gender, age, race, chest width, chest circumference, medications taken, underlying medical conditions, physical abilities, vital signs, pregnancy / expected pregnancy, genetic predisposition of the subject patient, allergies, results of previous imaging studies on the subject patient, or known radiation sensitivity of the subject patient.

[0063] Clause 44. The method of any one of clauses 39-43, wherein the information about the subject patient includes the subject patient's radiation sensitivity and the subject patient's age.

[0064] Clause 45. A method according to any of clauses 39-44, wherein the information about the subject patient includes information representing at least one of vascular access location, vein size, vein fragility, IV gauge, a specific distance within the body through the subject patient's vascular system, arterial perfusion, or parenchyma.

[0065] Clause 46. The method of any of clauses 39-45, wherein the information about the subject patient comprises at least one physiological waveform of the subject patient.

[0066] Clause 47. The method of Clause 46, wherein the at least one physiological waveform comprises an ECG waveform.

[0067] Clause 48. The method of any one of clauses 39-47, wherein the one or more data sources comprises a caregiver's assessment of the subject patient.

[0068] Clause 49. A method according to any one of clauses 39-48, wherein the one or more data sources include at least one of: an electronic medical record system including the patient's electronic medical record (EMR), an electronic health record (EHR) system, a patient procedure tracking system, a radiology analysis system (RAS), a digital pathology system (DPS), a picture archiving and communication system (PACS), a subject patient, a hospital data system, a hospital purchase order system including orders for studies to be performed on the subject patient, a database including previous scan results for the patient, a database including previous scan results for one or more other patients, or a government guideline database of acceptable radiation doses and contrast agent dose levels.

[0069] Clause 50. The method of any one of clauses 39-49 further comprises performing a scout scan on the subject patient to determine study-specific information, the study-specific information comprising at least one of a length of a scanned area, a scan time, a length of a plateau phase, and / or a bolus enhancement time, and wherein the information about the subject patient comprises the study-specific information determined by the scout scan.

[0070] Clause 51. A method according to any of clauses 39-50, wherein the information about the subject imaging study includes information about a fluid injector associated with the subject imaging study, wherein the information about the fluid injector includes information from a test injection or patency check using saline, information about the capabilities and tolerances of the fluid injector, and / or the presence of an external sensor for monitoring injections performed by the fluid injector.

[0071] Clause 52. The method of any one of clauses 39-51, wherein some or all of the information about the subject patient is determined based on a question-and-answer session with the subject patient.

[0072] Clause 53. A method according to any of clauses 39-52, wherein generating a baseline study protocol comprises applying a plurality of input parameters determined from information received about a subject patient and an imaging study of the subject to at least one model, wherein the at least one model uses at least one of an algorithm, an attenuation and noise model, or a patient fitting and Monte Carlo simulation model to determine output parameters for the baseline study protocol.

[0073] Clause 54. The method of clause 53, wherein the output parameters for the baseline study protocol include at least one of: an injection parameter; a radiation dose output; or contrast agent dose information.

[0074] Clause 55. The method of clause 53, wherein generating the baseline study protocol comprises applying a plurality of input parameters to a first model and applying output parameters from the first model to a second model to generate additional output parameters for the baseline study protocol.

[0075] Clause 56. A method according to any of clauses 39-55, wherein the baseline study protocol is based on parameter values ​​determined from at least one of: a comparison of the results of a single model completed at one time, a single model completed at one time selected by a user from a plurality of available models, multiple models completed sequentially, or multiple models completed together.

[0076] Clause 57. The method of any of clauses 39-56, wherein the one or more risk factors are related to at least one of contrast agent dose, radiation dose, extravasation risk, patient discomfort, anaphylactic shock risk, or image quality.

[0077] Clause 58. The method of Clause 57, wherein the radiation dose comprises a peak skin dose, an organ dose, a breast dose (for female patients), an effective dose, or a cumulative dose.

[0078] Clause 59. A method according to any one of clauses 39-58, wherein the determination of one or more risk factors specific to the subject patient is based on artificial intelligence using a model trained based on clinical data of patient outcomes and image quality.

[0079] Clause 60. A method according to any of clauses 39-59, wherein the iterative process for optimizing one or more parameters of the baseline study protocol includes an algorithm for minimizing or maximizing selected parameter values, an algorithm for ensuring that certain parameters are within a target or threshold range, or a weighting function applied to the parameter values ​​of the baseline study protocol.

[0080] Clause 61. A method according to any of clauses 39-60, wherein the iterative process of optimizing one or more parameters of the baseline study protocol is based on a comparison between predicted values ​​of dose from past scans and actual radiation dose from past scans, any atypical events from previous scans of the subject patient, any atypical events from previous scans of a particular type, and / or optimization scores from previous scans.

[0081] Clause 62. The method of any of Clauses 39-61, wherein modifying the baseline study protocol comprises adjusting a dose volume based at least in part on a measured gadolinium retention value of the subject patient.

[0082] Clause 63. The method of any of Clauses 39-62, wherein modifying the baseline study protocol comprises at least one of reducing the iodine concentration of the contrast agent, reducing the radiation dose, or changing the IV specifications of the medical imaging study.

[0083] Clause 64. A method according to any one of clauses 39-63, wherein performing the iterative process includes optimizing a first parameter related to one of radiation dose, contrast agent dose, or extravasation risk, and after optimizing the first parameter, optimizing a second parameter related to another of radiation dose, contrast agent dose, or extravasation risk.

[0084] Clause 65. The method of clause 64, wherein, after optimizing the second parameter, confirming that a modified study protocol including the first parameter and the second parameter provides images of sufficient diagnostic quality.

[0085] Clause 66. The method of any of clauses 39-65, wherein determining whether the modified study protocol provides images of sufficient diagnostic quality is based on determining whether image differentiation is sufficient based on at least one of tissue differentiation or Hounsfield units.

[0086] Clause 67. The method of clause 66, wherein the determination of whether the image is of sufficient diagnostic quality is based on quantification of residual and noise errors in the image generated by the modified study protocol.

[0087] Clause 68. A method according to any of clauses 39-67, wherein the generated modified study protocol includes a protocol that is different from the baseline study protocol and is performed on the same type of scanner as the baseline study protocol, using a different scanner and associated equipment to perform the modified study protocol, performing a different modality or test compared to the baseline study protocol, and / or modifying one or more of the following fluid delivery parameters: concentration, flow rate, duration, iodine delivery rate, gadolinium delivery rate, dose volume, phase sequence, flow rate rise time, phase change time, or injection delay compared to the baseline study protocol.

[0088] Clause 69. The method of any of clauses 39-68, wherein the modified study protocol includes recommendations to increase subject patient sedation, hydration to reduce motion, and / or change IV position to an optimal IV position.

[0089] Clause 70. A system for generating a protocol that can be used for medical imaging studies, comprising: one or more processors; a non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by the one or more processors, will cause the system to perform a method as described in any of Clauses 39-69.

[0090] Clause 71. The system of Clause 70, further comprising a user interface providing one or more selectable user interface elements that allow an operator to adjust the one or more risk factors specific to the subject patient.

[0091] Clause 72. The system of clause 71, wherein at least one of the one or more selectable user interface elements is in the form of a slider bar adjustable by an operator.

[0092] Clause 73. The system of clause 72, wherein the user interface is a graphical user interface display screen, and wherein the one or more user interface elements can be adjusted by the operator's touch on the graphical user interface display screen.

[0093] Clause 74. A fluid injector system for administering at least one fluid to a patient in a generated patient-specific regimen, the fluid injector system comprising: a control device operably associated with at least one drive component for pressurizing the at least one fluid into the patient through at least one disposable component; and the control device comprising at least one processor, the at least one processor being programmed or configured to enable programming of the patient-specific regimen, according to which the at least one drive component pressurizes the at least one fluid into the patient through the at least one disposable component so as to achieve enhancement of at least one region of interest thereof during a scan duration of a diagnostic imaging procedure, wherein, in order to generate the patient-specific regimen, the control device is also programmed or configured to enable programming of the patient-specific regimen, according to which the at least one drive component pressurizes the at least one fluid into the patient through the at least one disposable component Configured to: receive information about the patient and the imaging study to be performed from one or more data sources; generate a baseline study protocol based on the information about the patient and the imaging study to be performed; determine one or more risk factors specific to the patient based on the information about the patient and the imaging study to be performed; and modify the baseline study protocol to address at least one of the one or more risk factors specific to the patient, thereby providing the patient-specific protocol, wherein modifying the baseline study protocol to address at least one of the one or more risk factors specific to the patient includes: performing an iterative process to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors, and generating a modified study protocol that will provide images of sufficient diagnostic quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1A is a schematic diagram illustrating features of a model for generating a protocol for a medical imaging procedure according to one aspect of the present disclosure;

[0095] Figure 1Bis a schematic diagram illustrating features of another exemplary model for generating a protocol for a medical imaging procedure according to one aspect of the present disclosure;

[0096] Figure 1C is a schematic diagram illustrating features of another exemplary model for generating a protocol for a medical imaging procedure according to one aspect of the present disclosure;

[0097] Figure 1D is a schematic diagram illustrating features of another exemplary model for generating a protocol for a medical imaging procedure according to one aspect of the present disclosure;

[0098] Figure 2A is a schematic diagram illustrating an exemplary model for generating a protocol for a medical imaging procedure, which includes sequentially using two models to generate output parameters for a study protocol;

[0099] Figure 2B is a schematic diagram of another exemplary model according to one aspect of the present disclosure, the exemplary model obtaining output parameters of a study protocol based on multiple models that are executed sequentially to generate a desired output;

[0100] FIG. 3A to FIG. 3C is a schematic diagram illustrating trade-offs for optimizing a protocol for CT imaging studies according to one aspect of the present disclosure;

[0101] Figure 3D and Figure 3E is a schematic diagram illustrating trade-off considerations for optimizing a protocol for MR imaging studies according to one aspect of the present disclosure;

[0102] Figure 3F and Figure 3G is a schematic diagram illustrating trade-off considerations for optimizing a protocol for a nuclear medicine imaging study according to one aspect of the present disclosure;

[0103] Figure 3H shows a series of schematic graphs illustrating response curves resulting from increases in the value of the independent variable;

[0104] Figure 4A and Figure 4B is a flow chart illustrating a method for generating an optimized patient-specific protocol for a medical imaging study according to an aspect of the present disclosure;

[0105] Figure 5A and 5B is an example optimized table for modifying a baseline study regimen for a particular patient according to one aspect of the present disclosure;

[0106] Figure 5C and 5Dis a schematic diagram of a user interface that may be used by a system operator to adjust risk factors considered in generating a modified or optimized patient-specific study protocol according to one aspect of the present disclosure;

[0107] Fig. 6A is a flow chart illustrating a method for generating an optimized patient-specific protocol for a medical imaging study of a first patient according to one aspect of the present disclosure;

[0108] Figure 6B is a flow chart illustrating a method for generating an optimized patient-specific protocol for a medical imaging study of a second patient according to one aspect of the present disclosure;

[0109] Figure 6C is a flow chart illustrating a method for generating an optimized patient-specific protocol for a medical imaging study of a third patient according to one aspect of the present disclosure;

[0110] Fig.6D is a flow chart illustrating a method for generating an optimized patient-specific protocol for a medical imaging study of a fourth patient according to one aspect of the present disclosure;

[0111] Figure 7 is a schematic diagram of a powered fluid injector system for delivering fluid to a patient following a patient-specific regimen generated by the methods and techniques disclosed herein;

[0112] Fig. 8A is a perspective view of another example of a power fluid injector system according to one aspect of the present disclosure;

[0113] Figure 8B yes Fig. 8A A perspective view of a power fluid injector system of FIG. 1 with an access panel in an open position;

[0114] Fig. 9 yes Fig. 8A A schematic diagram of various fluid paths within a power fluid injector system;

[0115] Fig.10 is a schematic diagram of a use environment of a fluid injector system including a scanning room and a control room according to one aspect of the present disclosure;

[0116] Fig.11 is a schematic diagram showing electronic components of a fluid injector system according to one aspect of the present disclosure; and

[0117] Fig.12 is a schematic diagram of an image creation system according to one aspect of the present disclosure. DETAILED DESCRIPTION

[0118] The diagrams generally illustrate preferred and non-limiting examples or aspects of the systems and methods of the present disclosure. Although various examples or aspects of the present device are described, they should not be interpreted as limiting the present invention in any way. In addition, modifications, concepts and applications of the examples or aspects of the present disclosure will be interpreted by those skilled in the art as being covered by the illustrations and descriptions herein but not limited to the illustrations and descriptions herein.

[0119] The following description is provided to enable those skilled in the art to make and use the described examples or aspects intended for performing the present disclosure. However, various modifications, equivalents, variations and alternatives will still be apparent to those skilled in the art. Any and all such modifications, variations, equivalents and alternatives are intended to fall within the spirit and scope of the present disclosure.

[0120] For the purposes of the description below, the terms "upper", "lower", "right", "left", "vertical", "horizontal", "top", "bottom", "lateral", "longitudinal" and their derivatives shall refer to the present disclosure as it is oriented in the accompanying drawings. When used with respect to a dosing line, the term "proximal" refers to the portion of the dosing line closest to the power fluid injector. When used with respect to a dosing line, the term "distal" refers to the portion of the dosing line closest to the injection site of the patient. When used with respect to a dosing line or a syringe of a power fluid injector, the term "axial" refers to the direction along a longitudinal axis extending between the proximal end and the distal end of the syringe or dosing line.

[0121] As used herein, the term “at least one of…” is synonymous with “one or more of…”. For example, the phrase “at least one of A, B, and C” means any one of A, B, and C, or any combination of any two or more of A, B, and C. For example, “at least one of A, B, and C” includes one or more of A alone; or one or more of B alone; or one or more of C alone; or one or more of A and one or more of B; or one or more of A and one or more of C; or one or more of B and one or more of C; or one or more of A, B, and C. Similarly, as used herein, the term “at least two of…” is synonymous with “two or more of…”. For example, the phrase “at least two of D, E, and F” means any combination of any two or more of D, E, and F. For example, “at least two of D, E, and F” includes one or more of D and one or more of E; or one or more of D and one or more of F; or one or more of E and one or more of F; or one or more of all of D, E, and F.

[0122] It should also be understood that the specific devices and processes shown in the drawings and described in the following specification are merely exemplary aspects of the present disclosure. Therefore, specific dimensions and other physical characteristics related to the examples disclosed herein are not to be considered limiting.

[0123] With reference to the accompanying drawings, the present disclosure relates to techniques and methods for generating fluid injection study plans and / or imaging study plans for medical imaging procedures. In particular, the systems and / or methods disclosed herein can be used to generate program plans (e.g., parameter sets that can be used by injectors, scanners, or other modalities to complete imaging procedures). Desirably, the generated or optimized patient plans provide at least acceptable ("good enough") imaging results (e.g., diagnostic images) for each patient each time. As described in detail herein, the generated or optimized study plans can be based on data from many sources, including patient-specific data, as well as data collected from previous imaging procedures, data from other patients and / or patient populations. Many mathematical models for determining or modifying plans can be used to analyze input data to produce optimized patient-specific plans. In addition, the study plan optimization methods and techniques disclosed herein should be easy to implement, have minimal work for technicians, and have minimal cost / harm to patients and healthcare systems.

[0124] As used herein, "protocol" or "study protocol" can refer to the sequence of events that occur during an imaging procedure, as well as multiple input parameters of medical equipment (such as fluid injectors and image scanners) used during the imaging procedure. For example, a study protocol may include settings or parameters related to the contrast agent being used, such as total contrast agent volume, flow rate (e.g., contrast agent delivery rate) and / or contrast agent dose temperature, which is related to the viscosity of the contrast agent. The parameters of the injection protocol can also be related to the radiation dose or scanner settings, such as current (e.g., the current through the X-ray tube in milliamperes (mA)) or the potential applied to the X-ray tube, which can be measured as kilovolt peak (kVp). Other scanning parameters can include scan speed, scan duration, beam width, slice width and radiation dose. The parameters of the imaging procedure can also be related to the image quality of the image obtained during the study or procedure. For example, the parameters of the imaging protocol can include the signal-to-noise (S / N) ratio, contrast-to-noise (C / N) ratio and / or spatial resolution of the image captured according to the protocol. Other relevant protocol parameters may include maximum injection rate (eg, to address extravasation risk), injection site (eg, venous or arterial), bolus shape, contrast medium temperature, contrast medium viscosity, contrast medium concentration, image resolution, etc.

[0125] " Scheme " for the imaging procedure being performed can include one or more selected values ​​of these parameters, and any other parameters that are considered to be relevant to the fluid delivery and imaging procedure being performed. As described in further detail herein, a study scheme can be generated for a normal or average patient or a moderate number of patients across the normal distribution of the patient. This normal or average patient scheme (also referred to herein as initial or baseline scheme) is expected to be sufficient to provide an image of reasonable quality for the average patient, without exposing the patient to unreasonable risks from radiation, contrast agents or any other previously described risk sources. For example, in a speech at the annual meeting of the Radiological Society of North America in 2016, Dominik Fleischman suggested that 64-layer CT angiography (CTA) use a scan time of 10 seconds, an injection duration of 18 seconds, and a contrast agent flow rate and volume adjusted for patient weight. Patients less than 55kg receive 72mL, patients 55-65kg receive 81mL, patients 66-85kg receive 90mL, patients 86-95kg receive 99mL, and patients>95kg receive 108mL. Based on the tomographic images (CT scout images) of each patient, the length of the scan is adjusted for each patient. This is an example of an appropriate number of initial schemes. The methods and techniques disclosed herein are provided to optimize or customize the generated or initial schemes for specific patients based on, for example, an assessment of patient-specific risks to improve image quality and / or reduce the risk of the patient. Optimization or customization can be based on the specific features of the patient, the medical device being used, and / or the procedure being performed. Therefore, in summary, the systems and methods described herein are intended to provide a mechanism by which an operator can optimize certain research parameters so that the research scheme is suitable for a specific patient and any risk factors that may exist in a specific patient are addressed. In addition, although the following examples and many embodiments focus on contrast agent-related studies, such as CT and MRI, the methods and systems disclosed herein for using models and iterating around multiple models can also be applied to non-contrast agent studies, including studies in which radiation dose, image quality, motion risk, and claustrophobia are still applicable.

[0126] As an example, parameters related to contrast agent delivery to patients can be adjusted to improve patient safety and the effectiveness of scanners for specific imaging procedures. For example, conventional fluid injectors usually eject fluid at a high rate to ensure that sufficient fluid doses are delivered to patients within a reasonable period of time. However, for some patients and procedures, the injection rate of contrast agents may not need to be as high as the rate initially envisioned. For example, slight changes in scanning technology, patient demographics, patient physiology, or other program measurements can allow for the use of less contrast agents for some patients and procedures to achieve optimal results. In addition, the sensitivity of scanners for specific diagnoses may have improved (and is likely to have improved) because of the development of (multiple) standards (e.g., standard protocols for normal patients) associated with their use. As another example, a model patient for whom a specific standard (e.g., a specific standard or baseline protocol) is developed may vary slightly in height or weight from a patient who is now undergoing an imaging procedure. Therefore, the contrast agent injection rate and / or volume can be adjusted to take into account these minor differences between the patient and the scanner device.

[0127] The present inventors have recognized many benefits of reducing contrast agent dosage. For example, modifying the contrast agent injection rate and / or contrast agent volume can reduce the cost of the procedure by reducing the amount of contrast agent injected into the patient. Reducing the contrast agent dosage reduces the risk that the patient will have an adverse reaction to the contrast agent. In addition, due to the increased sensitivity of some modern scanners, such scanners can perform optimally at lower injection rates than current standard protocols envision. In this case, scanner performance may actually be hindered by following the standard protocol of injecting contrast agent at conventional high flow rates. Many practitioners may also be unaware of the improvements achieved by other practitioners who have successfully achieved optimal research results using protocols that deviate from once accepted practice or more closely tailored to the actual research being conducted. Therefore, adjustments to standard injection protocols and parameter values ​​are beneficial to improving patient outcomes.

[0128] In some examples, the methods and systems of the present disclosure are intended to be used as a "guidance system" in the form of an overall image creation system model that relies on two or more sub-models of various aspects or sub-systems of the overall imaging system, such as an injector and an imaging device, to generate a patient-specific solution. In this case, the systems and methods disclosed herein may include multiple sub-models of the same or overlapping aspects of the image creation system based on different levels of detail, variables, or experimental results. The image creation system may also involve patients, technicians, and optional radiologists as sources of data and / or feedback. In addition, the performance of the overall image creation system model can be simulated based on user-guided input and existing or missing input data to converge to one or more solutions that are optimized or patient-specific. Due to the high probability of missing data and incorrect model directions (e.g., when the model cannot be explicitly inverted, the variable to be optimized is the model input), the overall performance can be iterated to converge to a recommended, modified, or adjusted (multiple) study plan.

[0129] Thus, in some examples, the systems and methods of the present disclosure obtain modified or customized protocols through an iterative optimization approach. For example, the methods and systems disclosed herein can apply patient and study data (e.g., patient demographics, patient study history, information about study type), risk assessments (e.g., extravasation risk, radiation risk, contrast agent risk), and protocol calculation guidance (e.g., existing protocol algorithms and methods) to an iterative process in order to generate a study protocol that can produce diagnostic quality images with sufficient image contrast (e.g., tissue differentiation, Hounsfield units, etc.) and sufficiently low noise (e.g., detector noise) while avoiding or minimizing patient-specific risk factors. Specifically, an object of the present disclosure is to provide a system and method for performing various optimizations in a system and / or algorithm to allow automatic trade-offs between optimization tasks in order to simplify and speed up the operation of imaging procedures.

[0130] In some examples, the methods and systems of the present disclosure rely on independent or uncontrollable inputs (e.g., inputs that do not change during a procedure). Such inputs may include, for example, patient information, procedural information, disease screening information, equipment information, etc. Examples of patient information may include patient physical or anthropometric information (e.g., height, weight, lean body mass, gender, age, race, chest width, chest diameter, body mass index, body surface area, waist-to-hip ratio, metal or other implants, pregnancy, etc.). Patient physiological information (e.g., allergies, cardiac output, ejection fraction, blood pressure, blood flow velocity, pulse rate, flow rate capacity due to vein size, respiratory rate, glucose level, estimated glomerular filtration rate, serum creatinine level, coagulation status, blood oxygen saturation, vascular access position), patient history (e.g., results from previous imaging examinations, previously received contrast agents and radiation doses, occupational radiation dose estimates, claustrophobia, history of uncooperative behavior) and operator assessment of the patient (e.g., breath holding ability, ability to follow instructions, need for sedation, critical / stable / ambulatory status, fast / non-fast). Examples of procedure information may include procedure type (first pass, arterial phase, parenchymal phase, venous phase, cardiac, abdominal, neurological, peripheral, computed tomography angiography (CTA) vs. conventional CT, lung cancer vs. pulmonary embolism, target tissue to be imaged, presence of contrast agent, emergency vs. routine, etc.), scan volume, procedure objectives (e.g., screening vs. diagnosis, evaluation for treatment, monitoring follow-up, biopsy, etc.), procedure history (e.g., previous predicted vs. actual radiation dose for this scan for this patient or this study, any atypical events from previous scans of this type or this patient, benchmark comparables / data, prospective or retrospective gating, etc.), planned scan type (e.g., modality, full scan details, length and duration covered, kVp, multispectral imaging, multimodal imaging scout scan), and contrast agent bolus data (e.g., flow and pressure for estimating impedance, bolus curves, regions of interest, phantom data). Examples of device information may include sensor availability, device type, device quality, device age, and / or cost of using the device. The sources of independent input are not necessarily limited to the data types disclosed herein. Other non-limiting sources of information or input for optimizing or customizing a patient's regimen may be, for example, a paper order from the patient's caregiver, a verbal (Q&A) assessment by the patient, written instructions for the procedure being performed, facility rules and guidelines for the procedure, hospital information systems, or the patient's chart and lab work.

[0131] Continuing with reference to the drawings, the present disclosure also relates to a contrast agent injector system and a controller configured to cause the system's fluid injector to discharge fluid (e.g., contrast agent or saline) at a predetermined or calculated rate and / or pressure according to a baseline and / or customized regimen generated by the methods and techniques disclosed herein. Hardware associated with the system may include, for example, one or more scanning devices (e.g., CT (computed tomography) or MRI (magnetic resonance imaging) scanners), one or more medical injectors (e.g., contrast agent injectors, pharmacological stress agent injectors), one or more databases for storing the information discussed herein and generated regimens, and one or more processors (e.g., computers) configured to execute programming instructions stored in a non-transitory computer medium to perform the tasks and functions described herein.

[0132] Baseline or routine regimen determination

[0133] The methods and techniques disclosed herein generally begin with generating or providing a standard or baseline protocol for a normal patient (e.g., a patient of average height, weight, age, etc.) and / or a normal patient of a specified gender, age, and / or general size (e.g., small, medium, or large). In some examples, a normal or conventional protocol may be a model that is known (e.g., based on long-term and widespread use) to provide a reasonable radiation dose and obtain images of reasonable quality for otherwise healthy patients. The model may use standard patient parameters as inputs to the model, including patient size, weight, and gender. Based on such common or general inputs, a protocol may be generated that should desirably work for any patient to provide a reasonable image (e.g., an image of sufficient quality to be used for diagnosis or to serve as a record of the patient's current condition). Figures 1A to 1D is a schematic diagram showing several of these conventional models used to generate a baseline protocol for an imaging procedure being performed.

[0134] The model has one or more model inputs as input and provides one or more model outputs. Some of the model inputs may be about the imaging study. The model outputs include aspects of the injector and / or scanner protocol and / or information about what is expected to happen, such as the contrast agent dose to be used or the radiation dose expected to be received by the patient. For example, the current P3T algorithm outputs the injector protocol and timing information to synchronize between the injector and the scanner, and the current SiemensCARE kV model outputs CT scan parameters.

[0135] Some models are not invertible, meaning that an inverse model cannot be created, and therefore the original model input cannot be determined from the original model output. An example of this is the Ty Bae model for contrast agent flow through the body, which produces as output time-varying concentrations of contrast agent concentrations in various body parts given a specific contrast agent injection bolus as input. If the goal is to determine an injection bolus for a specific patient to achieve a specific organ image enhancement, this cannot be easily done because all parameters of the model are unknown for the specific patient.

[0136] One way around this is to create a reasonable model based on the patient's height, weight, and cardiac status (if known). The model is given a reasonable bolus as input, and the output is calculated. By continuously adjusting the bolus length and recalculating the output, the desired length of image enhancement can be achieved. This is discussed in U.S. Patent Application Publication No. 2019 / 0012932A1, entitled "Simulator, Injection Device Or Imaging System Provided With Simulator, And Simulation Program," which is incorporated herein by reference.

[0137] Another approach is to perform at least some of the iterations in advance, for example to create a data set or "dictionary" or "library" of a large but finite number of input combinations and their resulting output sets. This approach has been done for MRI signal evolution as part of a process known as magnetic resonance fingerprinting (MRF), as described in U.S. Patent No. 8,723,518, entitled "Nuclear Magnetic Resonance (NMR) Fingerprinting," and U.S. Patent Application Publication No. 2014 / 0167754A1, entitled "Magnetic Resonance Fingerprinting (MRF) With Echo Splitting," both of which are incorporated herein by reference. Then, for a given desired output set, it can be compared to the data set in the dictionary, for example using orthogonal matching pursuit (OMP), and when the closest match is found, the associated input set provides parameters to be used for bolus design and / or scan design, optionally interpolated between the closest dictionary entries, and optionally subsequently confirmed by the model using the interpolated parameters. Optionally, further minor adjustments can be made through iteration, although this may not be necessary or worth the time or effort.

[0138] Each model described herein covers certain aspects of the entire imaging procedure. Historically, separate models were associated with injector devices and scanning devices, which are two aspects of the imaging procedure. Therefore, the systems and methods described herein optionally use multiple models to optimize the entire imaging procedure. It is expected that new models will be developed that can cover more or less aspects than those described herein. For example, within the imaging aspects for CT scanning, there are CT scan design, radiation dose estimation, and image quality estimation, which are usually different models, but can be covered as a single model or a series of connected or interoperable models. There can also be specific models for specific studies, such as the case of Bayer Healthcare LLC's P3T algorithm or Siemens' FAST CARE technology, as listed in https: / / www.siemens-healthineers.com / it / computed-tomography / technologies-innovations / fast-care. These more inclusive models can be used as part of the present disclosure, with the potential benefits of faster protocol development, more customization and precision of the patient's protocol, and reduced overall risk to the patient. Optional additional aspects of the imaging procedure may include patient handling and care before, during, and after, including optimizing risk mitigation suggested by the workstation, overall workflow (e.g., patient and machine scheduling), and post-processing of data acquired by the scanning device before, during, and after. These are relevant as it relates to optimizing the overall protocol for patients with a specific risk profile.

[0139] In other aspects of the invention, more complex or inclusive models can be constructed. For example, a CARE kV model can be developed that includes both low contrast dose and normal contrast dose, where the low contrast dose provides, for example, a lower kVp, increases iodine sensitivity at the expense of higher mA, and thus provides a slightly higher radiation dose. By having these multiple models that include multiple risk factors as inputs, the amount of iterations required to achieve a satisfactory optimized imaging protocol can be reduced.

[0140] One method of building a more inclusive model is to record the optimized scheme created, thereby establishing a database of patients and optimized imaging schemes. The database preferably also captures actual results, such as the time course of image quality or enhancement, and compares them with the results predicted by the (multiple) models used. When the database is established, when a new patient arrives, the database can be scanned first to check for matches. If there is such a match, the scheme can be used for the current patient. Alternatively, with the increase in computer power and the reduction in storage costs, it will be possible to model a wide range of patients over a period of time and calculate their optimized schemes and store them in a large database. Then, when a new patient arrives, the database can be queried to obtain the closest matching scheme for its imaging scheme. Therefore, the modeling process does not have to be completed in real time for each patient, but can be completed in advance in whole or in part to simplify the modeling process required to find the optimized scheme. In some systems, optimization can include database lookups in the database of optimized imaging schemes.

[0141] For example, Figure 1A A schematic diagram showing at a high level the CT dosimetry system of Bayer HealthCare LLC, known as Software system that provides CT dose recommendations for a variety of studies, including cardiac, pulmonary angiography, and abdominal. Figure 1A As shown, at box 110, the P3T system receives the following inputs: patient information (e.g., patient weight and height), contrast agent information (e.g., concentration), and desired imaging characteristics (e.g., Hounsfield units). At box 112, the model uses an algorithm to generate outputs for injection. Specifically, as shown in box 114, the output parameters may include volume, flow rate, contrast agent concentration, and scan delay. Further information on software systems may be found in US Pat. Nos. 7,925,330, 8,428,694, and 10,166,326, which are incorporated herein by reference.

[0142] Figure 1BAnother model for determining the regimen is shown, referred to as the Cincinnati Hospital Model. As shown in box 116, the inputs to the model include patient information (e.g., patient height, weight, and gender) and a numerical value representing the desired image quality. The model applies an attenuation and noise model (shown in box 118) to generate output parameter values ​​of radiation dose (kilovolt peak (kVp) and milliamperes (mA)), which are shown in box 120. Further information about the Cincinnati Hospital Model can be found in U.S. Patent Application Publication No. 2014 / 0270053A1, entitled "Method for Consistent and Verifiable Optimization of Computed Tomography (CT) Radiation Dose," which is incorporated herein by reference.

[0143] Figure 1C 1 is a schematic diagram showing high level features of another model referred to by Siemens as the CARE kV system. As shown in block 122, the CARE kV system receives input in the form of patient information from a tomographic image and an input value for desired or required image quality. The image quality value may be a value for image differentiation or Hounsfield units. Other values ​​for image quality may include a desired signal-to-noise ratio or contrast-to-noise ratio. At block 124, the attenuation model is applied to the input values, which generates scan parameters including values ​​for kVp and maximum mA, as shown in block 126. Further information about the CARE kV system can be found in the document entitled “How to Scan with CARE kV,” available online at https: / / cdn0.scrvt.com / 39b415fb07de4d9656c7b516d8e2d907 / 1800000000073220 / c2ab5e6cbb6e / CT_How_to_reduce_dose_CARE_kV_final_1800000000073220.pdf, which is incorporated herein by reference.

[0144] Figure 1D It is shown for Schematic diagram of the solution for enterprise application, The enterprise application is a radiation dose management platform from Bayer HealthCare LLC. As shown in box 128, the model uses inputs including patient information (e.g., from a tomographic image) and the expected CTD / vol of the area to be scanned. At box 130, a patient fit for the input information is identified by a Monte Carlo model or simulation. At box 132, the model provides an output value of the effective dose to the patient and / or the effective dose to the patient's organ. Further information about the Radimetrics radiation dose management platform can be found in U.S. Patent No. 10,438,348 assigned to Bayer Healthcare LLC, which is incorporated herein by reference. Radimetrics is designed as a dose calculation system typically used after a CT scan so that the patient's organ dose can be determined and recorded. In the present invention, it can be used as one of two or more models in the iterative optimization system described herein.

[0145] In some examples, only one model may be used to determine an initial or baseline regimen. For example, the system may be configured to use only one of these models or any other known model to generate an initial or baseline regimen for a patient. As described in further detail herein, the initial or baseline regimen may then be adjusted, modified, or optimized to provide a patient-specific regimen. In other examples, the system may be configured to receive a user selection as to which model to use. For example, a user may select an initial model to be used to determine baseline regimen parameters by, for example, selecting a particular model from a user interface screen that lists multiple available models. In other examples, the system or method may be configured to use multiple different models to determine different regimens or output values. A system operator (or the system itself) may view the outputs generated by different models and select which model and / or generated output to use as an initial or baseline regimen, where the selection depends on the various risks of concern to the patient. In other examples, such as Figure 2A and Figure 2B As shown, multiple models can be used together to generate output values ​​or parameters for an initial or baseline scenario. The models discussed herein are not intended to be limiting, and additional models can be developed as knowledge and / or technology improve. The model can also be customized according to the policies and / or guidelines of the specific institution (e.g., hospital, healthcare system, or country) using the model. A database can be used to store the model, and the database can reside in the cloud.

[0146] In particular, if Figure 2A and Figure 2B As shown, some models can also be used in sequence or in combination to provide output for an initial or baseline scenario. Figure 2A As shown, the CARE KV model (from Figure 1C) can be used to determine the output of kVp and maximum mA, as shown in block 126. The values ​​of kVp and maximum mA can then be used as the P3T model (from Figure 1A As previously described, the P3T model may then be used to determine output parameters including injection parameters, contrast agent information, and scan delay, as shown in block 114 .

[0147] Figure 2B is a schematic diagram showing a more complex arrangement, in which the CARE kV model (from Figure 1C ), Radimetrics model (from Figure 1D )、P3T model( Figure 1A ) and other models such as risk models for extravasation, renal injury, radiation, sensitivity (box 136) and / or American College of Radiology (ACR) guidelines and / or image quality modeling (box 138). Advantageously, different models can use separate patient information measurements (e.g., the radiation model uses tomographic images, while the P3T model uses patient height and weight), optionally combined with outputs from one or more of the other models to provide a more complete indication of the patient's size and physical characteristics. Patient functional information (such as cardiac output) can also be used as an input to one or more models. As Figure 2B As shown, the output from the CARE kV model (block 126) relating kVp and maximum mA can be used as the Radimetrics model ( Figure 1D Box 128 in ) and the P3T model ( Figure 1A The model may be iteratively executed while taking into account a risk model (block 136) and / or an image quality model (block 138) to consider, for example, tradeoffs between radiation dose, contrast agent (e.g., iodine) dose, and / or image quality. As shown in block 114, outputs from different iterations of the P3T model, such as injection parameters, contrast agent parameters, and scan delay parameters, may be used for an initial or baseline protocol.

[0148] Other models that may also be suitable for use with the methods and systems of the present disclosure have also attempted to provide quantitative analysis of the injection process during CT angiography (CTA) to improve and predict arterial enhancement. For example, Bae and colleagues developed a pharmacokinetic (PK) model of contrast agent behavior and solved a system of coupled differential equations with the goal of finding the driving function that causes the most uniform arterial enhancement. See KTBae, JPHeiken, and JABrink, "Aortic and HepaticContrast Medium Enhancement at CT. Part I. Prediction with a Computer Model," Radiology, Vol. 207, pp. 647-55 (1998); KTBae, "Peak contrast enhancement in CT and MR angiography: when does it occurrent and why? Pharmacokinetic study in aporcine model,” Radiology, vol. 227, pp. 809-16 (2003); KTBae et al., “MultiphasicInjection Method for Uniform Longed Vascular Enhancement at CT Angiography: Pharmacokinetic Analysis and Experimental Porcine Method," Radiology, Vol. 216, pp. 872-880 (2000); U.S. Pat. Nos. 5,583,902, 5,687,208, 6,055,985, 6,470,889 and 6,635,030, the disclosures of which are incorporated herein by reference. The inverse solution of a set of differential equations for the simplified compartment model proposed by Bae et al. showed that an exponentially decreasing flow rate of contrast agent can result in optimal / constant enhancement in CT imaging procedures. However, the injection curve calculated by the inverse solution of the PK model is a curve that is not easily achieved by most CT power injectors without major modifications.

[0149] In another approach, Fleischmann and colleagues treated the cardiovascular physiology and contrast agent dynamics as a "black box" and determined its impulse response by forcing the system with a short bolus (approximately a unit pulse) of contrast agent. In this approach, one performs a Fourier transform on the impulse response and manipulates the transfer function estimate to determine an estimate of the injection trajectory that is more optimized than previously practiced. See D. Fleischmann and K. Hittmair, "Mathematical analysis of arterial enhancement and optimization of bolus geometry for CT angiography using the discrete Fourier transform", J Comput Assist Tomogr, vol. 23, pp. 474-84 (1999), the disclosure of which is incorporated herein by reference.

[0150] Monophasic administration of contrast agents (typically, 100 to 150 mL of contrast agent at one flow rate) results in an inhomogeneous enhancement curve. See, e.g., D. Fleischmann and K. Hittmair, supra; and KTBae, "Peak contrast enhancement in CT and MR angiography: when does it occurrent and why? Pharmacokinetic study in a porcine model," Radiology, vol. 227, pp. 809-16 (2003), the disclosure of which is incorporated herein by reference. Fleischmann and Hittmair therefore proposed a scheme that attempted to adjust the administration of contrast agents to a biphasic infusion customized for individual patients, with the goal of optimizing imaging of the aorta. The fundamental difficulty in controlling the presentation of CT contrast agents is the rapid diffusion of hyperosmotic drugs from the central blood compartment. In addition, the contrast agent mixes with and is diluted by blood that does not contain contrast agent.

[0151] Fleischmann provides for the injection of a small bolus, a test bolus (16 ml of contrast at 4 ml / s) of contrast prior to the diagnostic scan. A dynamic enhancement scan is performed on the vessel of interest. The resulting processed scan data (test scan) is interpreted as the impulse response of the patient / contrast system. Fleischmann derives the Fourier transform of the patient transfer function by dividing the Fourier transform of the test scan by the Fourier transform of the test injection. Assuming that the system is a linear time-invariant (LTI) system and that the desired output time domain signal is known (a flat diagnostic scan at a predetermined enhancement level), Fleischmann derives the input time signal by dividing the frequency domain representation of the desired output by the frequency domain representation of the patient transfer function. Because the method of Fleischmann et al. computes an input signal that is not practically achievable due to injection system limitations (e.g., flow rate limitations), the computed continuous time signal must be truncated and approximated.

[0152] Improve or optimize the solution

[0153] As previously mentioned, the methods and techniques of the present disclosure are provided to modify, customize and / or adjust a model from a current and / or approved model (such as Figures 1A to 1D , Figure 2A and Figure 2B In particular, the inventors contemplate that a baseline or initial protocol may be generated based on the patient's Figure 3A The protocol modification and / or optimization is achieved by considering the following main criteria shown in: radiation dose or scan time 350, contrast agent dose 352, and image quality 354, depending on the imaging modality(s) to be considered. The evaluation of the relationship between these three main criteria can be referred to as the trade-off triangle for contrast agent image acquisition. As imaging equipment improvements occur, such as improvements in resolution, sensitivity, speed, and other improvements that bring the whole to a new level, these improvements are used to improve one or more of the trade-off parameters. Figure 3B-3G is a diagram visually depicting the relationship between and considerations associated with the three major criteria for different imaging procedures (eg, a trade-off triangle). The methods and techniques described herein take these relationships and considerations into account in order to modify or optimize an initial or baseline regimen to provide a patient-specific regimen.

[0154] More specifically, the systems and methods of the present disclosure rely, at least in part, on a hierarchy of optimizations to better serve the patient and his or her specific risk factors. Figure 3B-3G Indicates CT( Figure 3B and 3C )、MRI( Figure 3D and 3E ) and Nuclear Medicine ( Figure 3F and 3G), each of which is a modality to which the concepts described herein may be applied. Decisions about the parameters to be optimized and the order in which to optimize these parameters may be made, for example, by a person(s) (e.g., referring physician, technician, radiologist, patient) or automatically by a system (e.g., by using artificial intelligence (AI), machine learning, or a recommendation engine utilizing patient data). In addition to patient demographics and other data, optimization decisions may be based on, for example, medical / professional association guidelines, government guidelines, patient preferences, manufacturer / drug package inserts and instructions for use (e.g., for contrast agent optimization), and other types of industry literature.

[0155] like Figure 3B-3G As shown, the parameters for consideration and / or trade-offs (listed in box 356) may include or be related to the scanner itself (e.g., the type of scanner, scanner capabilities, maximum and minimum injection pressures, etc.). Other features considered may include reconstruction algorithms, CT imager properties, such as photon counting CT, scanner bed speed, image source, and / or scanner sensitivity and field strength. Figure 3D As shown, trade-offs may also include or be related to the contrast agent atoms used (e.g., I, Gd, Mn, Fe, W, etc.), imaging of different nuclei, radiopharmaceutical activity levels, surface coil limitations, scanning schemes used, field strength / aperture size, or MRI fingerprinting.

[0156] Figure 3B and Figure 3C is a schematic diagram illustrating the trade-offs for CT imaging procedures utilizing iodine. Figure 3B and 3C As shown, considerations related to reducing radiation and contrast agent dose risks (e.g., radiation dose exposure to organs) enclosed by box 358 may include possible allergic reactions, iodine volume and / or concentration, risk of kidney injury, and / or risk of adverse events, such as risk of extravasation. Figure 3B and Figure 3C As shown, the risk of extravasation may be mitigated by adjusting parameters including contrast volume, contrast temperature, contrast flow rate, iodine concentration, bolus shape, or amplitude and duration of the plateau, as shown in block 360. The risk of extravasation may also be mitigated by taking into account patient functional information, such as patient cardiac output, or other patient characteristics, such as age, venous status / fragility, IV access location, or the patient's ability to follow instructions and not move during contrast administration.

[0157] Considerations for improving image quality (e.g., related to the risk that image quality will be insufficient for diagnosis) enclosed within box 362 may include adjustments to scan parameters, temporal resolution (e.g., considering single time point, dual time point, and / or multiple time point curves), scan time acquisition duration, start time and bolus shape / duration, bolus image contrast (in HU units), desired spatial resolution, acquisition slice thickness, contrast signal-to-noise ratio or background contrast requirements, duration of the procedure, and / or size of the imaged region. Considerations for image quality may also include considerations related to the risk of motion degrading the image, as shown in box 364. Motion may be caused by, for example, cardiac motion, respiration, peristalsis, seizures, the patient's inability to control body movement due to anxiety or inability to follow instructions, adverse patient reactions to contrast agents (e.g., sensations of hot flashes, metallic taste, nausea, increased heart rate, pain), and / or any other patient body or limb movement. Motion degradation may be mitigated by changing timing accuracy requirements or respiration requirements or scanning protocols. Patient compliance may also be improved by taking actions to improve and / or remind the patient of the importance of remaining still and breathing appropriately during image acquisition, or by sedative or anxiolytic medications to mitigate motor decline. For example, image timing or duration may be increased or extended for patients who have a good ability to remain still and / or hold their breath for a reasonable period of time. Image timing or duration may be decreased or shortened for patients who do not remain still even when asked to do so.

[0158] Figure 3D and 3E Tradeoff features or considerations for MR imaging procedures using gadolinium-based contrast agents / GCBA (mgGd / ml) are shown, as shown in box 366. Unique considerations related to radiation dose risk of gadolinium (enclosed in box 356) can include gadolinium dose concentration volume considerations (e.g., related to the risk of gadolinium deposition in the brain or the risk of nephrogenic systemic fibrosis (NSF) due to poor renal function), as well as the risk of allergic reactions, adverse events, and / or the risk of extravasation. As in the previous examples, the risk of extravasation can be mitigated by, for example, adjusting the contrast agent bolus shape, Gd delivery rate, amplitude or duration of the plateau phase, injection site, contrast agent volume, and / or contrast agent flow rate.

[0159] Figure 3F and 3G The trade-off features for nuclear medicine (e.g., PET and SPECT) imaging procedures are shown. Figure 3F and 3GAs shown, image trade-off considerations (enclosed in box 356) can include requirements for the scanner itself, reconstruction algorithms, and / or different radiopharmaceuticals. Contrast and / or radiation dose risk mitigation features (enclosed in box 358) can include modifications that use less radiation or lower contrast agent doses, as well as mitigate the risk of allergic reactions, other adverse events, or extravasation risk. As in the previous example, the risk of extravasation can be mitigated or addressed by, for example, adjusting contrast agent volume, contrast agent flow rate (e.g., active delivery rate), contrast agent concentration, bolus shape, and / or amplitude / duration of the plateau phase, as shown in box 360. Radiation risk can also be mitigated by considering patient functional characteristics (e.g., cardiac output).

[0160] Considerations for improving image quality (block 362) may include considering whether contrast agent concentration changes during the scan cause contrast agent artifacts. Scan parameters, temporal resolution, start time bolus shape / duration, acquisition duration, spatial resolution, radioactivity concentration per voxel, contrast signal-to-noise ratio, background contrast, and / or duration of the procedure can also be adjusted or optimized for improving image quality. As previously described and as shown in block 364, the risk of motion degrading the image may also be considered to improve image quality.

[0161] Figures 3A-3G The representation of the trade-off triangle in shows that modifying certain characteristics or parameters can produce an injection regimen that is more beneficial to a particular patient than a standard or baseline regimen. However, in many cases, modifying one variable does not result in a linear improvement in another parameter, such as a parameter of image quality. Furthermore, a change in one parameter may affect multiple other parameters, sometimes in conflicting ways or in opposite directions. Figure 3H The idea that changes to variables lead to different and / or nonlinear improvements is visually illustrated in Figure 3H Represents a series of graphs or charts illustrating the concept of a "goodness" scale as it relates to different variables. Figure 3H The basic concept illustrated in the graph of is that changes in the value of an independent variable can have different trade-offs on the “goodness” of a particular dependent or output variable, and these changes can be, but are typically not, linear. For example, increasing the contrast agent dose (the independent variable) can increase the “goodness” of the resulting image quality, but this may not be a linear relationship. In fact, having too much contrast agent in CT may result in streak or star artifacts, hide calcifications in vessels, or actually reduce the MR signal in MR. Conversely, any benefit to image quality conferred by an increase in contrast agent dose is likely to reach a maximum. Once the maximum “goodness” of image quality is reached, further increases in contrast agent dose do not provide any further improvement in image quality. In other examples, such as Figure 3HAs shown in other graphs in , the improvement in Goodness can be a step function. For example, increasing the independent variable may result in no change in Goodness until a threshold is reached. Once the threshold is reached, Goodness can increase dramatically and then level off, creating a graph with a step function or waveform. Figure 3H Many other possible "goodness" responses to increasing values ​​of the independent variable are shown in the graph of .

[0162] Solution Optimization Method

[0163] The present disclosure describes methods and techniques for modifying an initial or baseline protocol based on patient-specific and other input data. As previously described, a study protocol may include a set of parameter values ​​for a medical injection and imaging procedure, such as those associated with injector parameters, contrast agent-related parameters, and / or radiation dose-related parameters. For example, a generated study protocol may include one or more of the following parameters: total contrast agent volume, maximum flow rate, contrast agent delivery rate, average flow rate, contrast agent temperature, contrast agent viscosity, contrast agent concentration, IV access location (e.g., location between shoulder and wrist, peripheral venous access point on leg or elsewhere on body, central catheter, artery or vein), scan area, potential application of X-ray tube (kVp), maximum current applied to X-ray tube (mA), scan speed, scan duration, radiation dose, signal-to-noise (S / N) ratio, contrast / noise (C / N) ratio, or spatial / resolution ratio.

[0164] When developing a patient-specific protocol, the methods and techniques described herein consider multiple factors, including, for example, contrast agent dose, radiation dose, time required for image acquisition, and image quality considerations and risks. In particular, the inventors recognize that some risk factors may be particularly relevant or important for certain patients, while less important for other patients. Therefore, the techniques and methods disclosed herein prioritize certain risks based on the unique characteristics of the patient and / or the procedure being performed. Once risks are identified and prioritized, the initial or baseline study protocol can be modified to take into account unique patient-specific risks. Figure 4A and Figure 4B Depicted is a flow chart illustrating an exemplary method for developing a patient-specific protocol using an iterative process that begins with an initial or baseline protocol for one aspect of the study and iteratively modifies the protocol to reach or converge toward an optimized or patient-specific protocol, which is then used to select and optimize one or more protocols for other aspects of the imaging study. Figures 6A-6D Methods are shown for performing and procedures to be performed (e.g., CT, MR, or nuclear medicine) for a specific patient (e.g., a young patient, an elderly patient, and / or a patient with an underlying disease condition), resulting in a generated customized or optimized protocol for the specific patient.

[0165] like Figure 4A and Figure 4B As shown, the method for developing a patient-specific protocol for a medical imaging study includes receiving information about a subject patient and an imaging study to be performed from one or more data sources at step 410. The received information may include patient information, such as anthropometric indicators of the patient, including height and weight, and the patient's gender, age, and similar identifying information. The information about the patient may also include information about the patient's condition, such as an underlying medical condition. The patient information may be obtained from the patient's medical records and by asking the patient questions and recording the results.

[0166] More specifically, in some examples, the received patient information can relate to one or more of: height, weight, body mass index, gender, age (e.g., child, adolescent, young adult, adult, elderly), race, chest width, chest circumference, medications taken (e.g., beta blockers), underlying medical conditions (e.g., renal damage, thyroid damage, presence of metallic or non-metallic implants, history of kidney stones, cancer, expected or known vascular tumors), physical abilities (e.g., ability to hold breath for a certain duration or remain still), vital signs (e.g., heart rate, resting heart rate, blood pressure, blood glucose, cardiac output, ejection fraction, blood flow velocity, oxygen saturation, serum creatinine level, estimated glomerular filtration rate (eGFR)), pregnancy / expected pregnancy, genetic susceptibility of the subject patient, such as risk of cancer, allergies (contraindications or limitations), previous imaging examinations of the subject patient, or known radiation sensitivity of the subject patient.

[0167] Information about the imaging study may include information about the type of imaging procedure being performed. For example, the imaging procedure may include computed tomography (CT) imaging, magnetic resonance (MR) imaging, nuclear medicine, PET, SPECT, ultrasound, thermal imaging, infrared (IR) imaging, or a combination thereof. A combined imaging procedure may include, for example, a thermal / IR study; a PET / CT imaging study; a PET / MR imaging study; a SPECT / CT, MR / elastography study; or a study including optical and X-ray imaging.

[0168] Data sources for patient information and / or information about the study can be obtained from many sources, including electronic records, databases, and / or information entered by a system operator or user. For example, the data source may include an operator's assessment of the patient and medical equipment that can be used for the study to be performed (e.g., assessing the patient's breath-holding ability, ability to follow instructions, sedation requirements, flow rate capabilities, intravenous IV specifications, etc.). Data sources for patient and program information may also include electronic medical records (EMR) systems, hospital data systems (e.g., hospital information systems (HIS), radiology information systems (RIS), patient program tracking systems), radiology analysis systems (RAS), laboratory information systems (LIS), digital pathology systems (DPS), picture archiving and communication systems (PACS), electronic health records (EHR) systems (such as EPIC), including hospital purchase order systems for orders for studies to be performed on subject patients (which may include paper prescription orders), including databases of previous scan results for patients, or government guideline databases (e.g., databases with standards for acceptable radiation doses and contrast agent dose levels for specific procedures). Patient and program information may also be obtained from other databases and records maintained, for example, by medical facilities, medical device manufacturers, or government organizations. In addition, patient and procedure information may be obtained from the patient, such as through written or verbal inquiries, patient charts, wearable devices, patient screening forms, electronic medical records (including those provided by the patient), etc.

[0169] In some examples, at step 412, the method may also include performing a scout scan on the subject patient. The scout scan or tomographic image may be used to determine study specific information including, for example, the length of the scanned area, the scan time, the length of the plateau phase, and / or the boost time of the bolus. The study specific information may be used with other patient information to generate initial or baseline protocol values.

[0170] At step 414, the method also includes generating a baseline study protocol based on information about the subject patient and the subject imaging study. As previously described, currently available or conventional models (such as those previously described) may be used. Software Model or Figures 1A-2B In some examples, the system may include only a single model (e.g., Figures 1A-1D ), which is used for all baseline solution determinations. As previously described, the method may also include allowing a user to select a particular model from a set of possible models to be used for calculating the initial or baseline solution. In other examples, such as Figure 2A and Figure 2B As shown, multiple models may be used sequentially or according to an iterative process to calculate or determine outputs or parameters of an initial or baseline solution.

[0171] At step 416, the method further includes determining one or more risk factors specific or unique to the subject patient based on information about the subject patient and the subject imaging study. Specific risk factors may include, for example, risks associated with radiation dose, contrast agent dose, risk of extravasation, risk of adverse events, and / or risk of reduced image quality. For example, Fig. 6A As described in further detail in , it may be particularly desirable to limit radiation doses to young patients, and in particular to reduce radiation doses to the breasts of young women. Figure 6B As described in further detail in , for patients with limited renal function, limiting the contrast dose may be important even if other risk factors need to be increased (e.g., radiation dose may need to be increased to compensate for the reduced intravenous contrast dose). Figure 6C As described in further detail in , patients undergoing chemotherapy may have an increased risk of extravasation, which means that reducing the flow rate and / or contrast viscosity may be particularly important for such patients. In other examples, for patients who cannot remain still or have difficulty holding their breath, it may be important to reduce the scan duration so that the image is not affected by patient movement.

[0172] In some examples, determining risk factors can include prioritizing which risk factors should be evaluated or considered first. For example, as previously described, radiation dose may be particularly important for young patients. In this case, the regimen can be optimized first to reduce radiation dose. After optimizing to reduce radiation dose, other models or optimization processes can be applied to the regimen to address issues related to contrast agent dose and / or image quality. In contrast, for patients with reduced renal function, the method can include prioritizing minimization of contrast agent dose. After the contrast agent dose is minimized, the method can include further modifying the regimen to address lower priority risks for specific patients related to radiation dose and image quality.

[0173] Once the risk factors have been determined and / or the priorities of the risk factors have been selected, the method further includes calculating scan parameters for the scan using an initial or baseline protocol to determine values ​​for the highest priority risk factors at step 418. For example, when radiation dose is of particular concern, the radiation dose value may be determined and compared to guidelines or acceptable values ​​for a particular patient.

[0174] At step 420, the method further includes modifying the baseline study protocol based on and / or addressing the determined highest priority risk factors for the particular or subject patient. In particular, this initial modification can be based on a comparison between the calculated values ​​of the risk factors and acceptable values ​​for the parameters provided in the literature. The modification can be an iterative process to optimize the parameters of the baseline study protocol to minimize the particular risk factor under consideration and generate a modified study protocol that will provide images of sufficient diagnostic quality.

[0175] In some examples, to modify or adjust the radiation dose, the method can include addressing radiation dose risk factors by reducing the radiation dose by a small amount (e.g., X%) by reducing the kVp and / or maximum mA to ensure that the resulting radiation dose is within the guidelines for the particular patient. The radiation dose can then be recalculated using the new scan parameters to determine whether the incremental change produces the desired result. If the desired result is not achieved, the radiation dose can be reduced by additional successive small changes to the scan parameters until the desired result is achieved. The resulting reduction in radiation increases noise in the image, while any reduction in KVp increases sensitivity to contrast agents, thereby enabling the use of smaller contrast agents.

[0176] Once the desired result is achieved, as shown in step 422, the method next includes addressing another of the risk factors. For example, the method can include determining whether the Hounsfield Unit (HU) value and noise in the image created according to the modified protocol will have acceptable quality. Reducing kVp increases sensitivity to contrast agents, while reducing kVp and mA generally increases image noise, which depends on many factors. If the image has acceptable quality, the modified protocol is acceptable for the imaging procedure used for the patient. If it is determined that the HU value and / or noise hinder the acquisition of an image of acceptable quality, it may be necessary to adjust parameters, such as increasing the contrast agent dose to improve the image. In other examples, it may be necessary to change the scanner or the procedure being performed in order to obtain an acceptable image while controlling or reducing the radiation dose and contrast agent dose for a particular patient. In this example, increasing the contrast agent dose is a reasonable first step.

[0177] Once the desired result is achieved, as shown in step 424, the method next includes addressing another of the risk factors. For example, for young patients, lower priority risk factors may be associated with contrast agent dose and / or extravasation risk. Therefore, the method can compare the contrast agent dose with the amount allowed or recommended for the patient (a healthy young person in this example). If it is within the allowed amount, no adjustment is made. If it is too high, the method may include iteratively reducing the contrast agent dose by a small amount, such as reducing the iodine dose by a predetermined percentage, until the desired result is achieved. Once the desired result is achieved, the protocol may be updated to include the determined or derived contrast agent dose.

[0178] At step 426, once it is determined that an image of acceptable quality can be obtained using the modified protocol, the method includes scanning and injecting according to the modified protocol. If the image quality is unacceptable, the system can repeat any of steps 420, 422, and / or 424. If repeating one or more of these steps does not produce acceptable image quality, the system can notify the user of this situation via a user interface, an example of which is shown in FIG. Figure 5DThe user can accept the system's recommendations, or can change one or more of the objectives and request the system to repeat the optimization process.

[0179] Methods for generating modified protocols for imaging procedures rely on the ability to prioritize which outputs, tradeoffs, and risk factors are most important for a particular patient. Determining which risk factors should be prioritized and / or modified most important can be based on user judgment, user preferences, practice guidelines or recommendations, and / or clinical evaluation of patient outcomes of previous imaging procedures. Any or all of these can be included in a lookup table or similar database. Patient information can be used as an input to a lookup table or database to provide values ​​associated with which risk factors should be prioritized or modified.

[0180] Figure 5A and 5B An exemplary lookup table including optimizations for CT studies is shown. Figure 5A , the information in the columns labeled "Patient Type / Disease," "Diagnostic Indication / Area," and "Condition,(s) Question, Issue" represent examples of independent or uncontrolled data that may be used as input for obtaining information from the lookup table. The information in the column entitled "Patient Specific Procedure Goals" represents examples of patient risk assessments as discussed above, and informs the appropriate optimizations that should be performed in order to address the independent or uncontrolled data associated with the patient and / or study. The remaining data shows the types of injection / scan parameters that may be optimized in order to achieve each patient-specific procedure goal, and whether each specific parameter may need to be increased or decreased from the baseline regimen in order to address each risk assessment.

[0181] More specifically, Figure 5A and 5B The table lists some scenarios (such as Figure 5A ), where certain parameters of the study protocol should be prioritized or modified (e.g., increased or decreased), given greater weight, or otherwise prioritized relative to other parameters to obtain improved results. The scenarios listed are for a CT diagnostic imaging procedure. However, one skilled in the art can prepare similar tables for other scans and / or procedures based on clinical data (e.g., image quality results from previous scans) or mathematical models for medical imaging procedures.

[0182] like Figure 5B As shown, the parameters (enclosed by box 510) increase or decrease in different scenes. The increase or decrease amount of each parameter in one scene of the scene is enclosed in box 512 (in Figure 5BSpecifically, different parameters may be assigned positive values ​​(e.g., 0.5 or 1) for parameters that should be increased relative to a baseline value in a particular scenario, and / or may be assigned negative values ​​(-0.5 or -1) for parameters that should be decreased relative to a baseline value in a particular scenario. For example, a parameter assigned a value of 0.5 may be initially increased by 5%, a parameter assigned a value of 1 may be initially increased by 10%, a parameter assigned a value of -0.5 may be initially decreased by 5%, and a parameter assigned a value of 1 may be initially decreased by 10%. Parameters that are not assigned a value may remain unchanged from the value of the initial or baseline scenario. As previously described, the use of Figures 1A-2B Currently available algorithms and models (e.g., Software, CARE Bolus, etc.) to calculate the baseline value. Other parameter assessments, weighting schemes, and assessment methods are also possible.

[0183] In some examples, scenarios can be based on patient type / disease state (e.g., male, female, pediatric, adult, elderly, pregnant or possibly pregnant, cancer, diabetes, chronic kidney disease, cardiovascular health risk, prior adverse events, obesity, etc.). Scenarios can also take into account the procedure to be performed or the area to be scanned. Patient-specific procedural goals can also be taken into account (e.g., scenarios that need to minimize risk of extravasation, reduce radiation dose, need to increase or maximize speed of diagnosis, etc.).

[0184] In one scenario, Figure 5A As shown in the first row of the table of , a cardiovascular (CTA) scan is being performed on a pediatric patient who has an increased risk of extravasation due to small veins. In this scenario, it is desirable to minimize the risk of extravasation. Figure 5B As shown, the creator of the table determined that this risk could be mitigated by reducing contrast agent parameters including total volume and flow rate (e.g., contrast agent delivery rate). In addition, the IV access location is moved along the arm toward the body so that larger veins can be accessed. Scan parameters can be modified to increase scan speed and reduce scan duration. Conversely, the temperature of the contrast agent, which reduces viscosity, can be increased to body temperature because reducing viscosity through temperature reduces the risk of extravasation. In addition, reducing the radiation dose via a lower kVp reduces the radiation risk, but may result in increased image noise and therefore reduced image quality. Other regimen parameters can be maintained at normal or baseline levels. Figure 5A and 5B Many other scenario modifications are described in the table below.

[0185] In some examples, a system operator or user may also wish to provide input regarding which risk factors or patient characteristics should be considered when optimizing or modifying an initial or baseline regimen. For example, a system operator (e.g., a technician or physician) may identify certain characteristics of a patient that should be addressed or considered based on an assessment of the patient. To allow the system operator to provide input regarding such risk factors, the system may include an input option that allows the operator to identify risk factors of particular concern for the patient. Figure 5C is a user interface (eg, a graphical user interface display screen) that provides an exemplary interface that may be provided to an operator for entering input related to risk factors of particular concern. Figure 5C As shown, the user interface includes user interface elements, which in this example are in the form of multiple bars 550 and slider controls 552. However, other user interface elements that allow input to be provided by the user, such as buttons, virtual knobs or dials, etc., can be used. Each bar represents a specific risk area of ​​the patient that the system operator may wish to address. For example, bar 550 can be related to the risk of motion-down images, the risk of image quality being insufficient for diagnosis, the risk of radiation damage to the patient, the risk of kidney damage, the risk of extravasation, the risk of allergic reactions, and the risk of other adverse events. In alternative aspects of the present invention, other risks discussed herein can be similarly represented by sliders. The user interface can also include a bar 554 that provides an expected / required output of image quality. The user interface allows the system operator to move the slider control 552 along the bar 550 to adjust which risk factors are considered to be the most important. In a non-limiting embodiment, the user interface is a touch display screen, and the user interface elements can be adjusted by touching the screen.

[0186] In some examples, the slider controls 552 may be initially positioned based on an analysis of the patient information and procedure information obtained by the system. The initial position of the slider controls 552 may be indicated by dashed lines 556 so that the operator may return one or more of the slider controls 552 to the initial position and / or recall the initial position if the slider controls 552 are moved or moved, as described below. For example, as previously described, for young patients, the risk of radiation harm may be most important. In this case, the slider control 552 on the bar 550 for radiation hazard may be near the top of the bar 550, as shown in FIG. Figure 5C. The system operator can move the slider control 552 from its initial position (shown by dashed line 556) based on which risk factors he or she determines are important for a particular patient. For example, the system operator can move the slider control 552 for radiation hazard downward. In response, the user interface can automatically move other slider controls upward for risk factors, which can increase as radiation dose increases. For example, the slider control 552 for the output image quality bar 554 can automatically move upward because the image quality can be improved if it is determined that the need to mitigate the risk of radiation hazard is lower. In a similar manner, if the system operator moves the slider control 552 of the output image quality bar 554 downward, indicating that a lower quality image may be acceptable for a particular imaging study, the slider control 552 of the bar associated with radiation risk or contrast dose risk can be moved upward, indicating that the radiation and / or contrast dose can be reduced. The system can use Figure 5C The slider control 552 position information input by the user interface is used to determine the parameter value of the optimized or modified solution using the methods and techniques disclosed in this article.

[0187] like Figure 5D As shown, the user interface 560 can display additional information about the plan recommendation, including the model sequence used to optimize various aspects of the plan (such as contrast, radiation dose, scanning and quality). These models can be selected or changed by the operator. The user interface 560 can also provide the resulting plan output. The exemplary user interface 560 or control panel (e.g., a graphical user interface display screen) provides additional features for the entire plan generation and optimization system. It can be composed of various parts or sub-panels. Figure 5C The optimization sliders referenced in are in subpanel 562. Key 564 can explain to the user the functions of the various sliders and sliding indicators. Subpanel 566 contains data about the patient collected from various sources, such as electronic medical records (EMR), hospital information systems (HIS), radiology information systems (RIS), patient-entered data, or conversations or observations with the patient, which are then manually entered by the operator. "Next" subpanel 568 indicates (e.g., with boxes) the actions that the operator needs to take to indicate that they have been completed.

[0188] The Risk Reduction Actions subpanel 570 indicates optional steps that can help further reduce patient risk. These are actions outside of the injector or scanner protocol itself, but would be useful for the operator or technician to perform. Examples include selecting a different catheter gauge or IV location, providing preventive pretreatment with medication (e.g., Benadryl) to patients with a history of or potential for allergic reactions, providing antianxiety medication (e.g., Valium) to patients with claustrophobia, or performing a pretreatment infusion (e.g., saline or bicarbonate solution) to reduce the occurrence of contrast-induced nephropathy in patients with poor renal function, as discussed in U.S. Pat. No. 9,421,330, entitled "Mitigation of Contrast Induced Nephropathy," the contents of which are incorporated herein by reference.

[0189] The protocol recommendation sub-panel 572 indicates which models are being used during the protocol optimization process and the order in which the optimization occurs. When the user clicks on the model, a pop-up window 574 opens, which allows the user to select one or more models to be used for this aspect of the entire imaging modeling and optimization process. Button 576 takes the user to the scanner control panel, and button 578 takes the user to the injector control panel. Button 580 will execute the protocol by loading the appropriate sub-protocol into the injector, scanner, and any other equipment involved. The scan itself can be started from this user interface, the injector user interface, or the scanner user interface, depending on the timing between the two, the exact setup of the hardware, and national regulations regarding what medical professionals must initiate the use of what medical equipment.

[0190] As mentioned earlier, Figures 6A-6D is a flow chart illustrating a specific embodiment of a method for generating a protocol of the present disclosure for a specific type of patient and procedure to be performed. Fig. 6A, existing data about the patient and the type of study (e.g., independent or uncontrolled data) indicates that the patient is a young female undergoing a lung study, as shown in box 610. The patient has good renal function, and the study type can include one or more of first pass, arterial, venous, parenchymal, or late enhancement. As shown in box 612, the system can also query other patient data sources, including previous patient studies. As shown in box 614, the system can also perform a scout scan to determine the length of the scan area, the scan time, the length of the plateau phase, the enhancement time of the bolus, and any other relevant scan information. Based on the available information from multiple sources, at box 616, the system calculates a patient risk assessment to determine what controllable scan / injection parameters (e.g., image acquisition scheme, radiation dose, contrast agent dose) should be optimized and the order of optimization (e.g., priority). For this particular patient (young, good renal function), the patient risk assessment can indicate that the process should first try to optimize radiation exposure (radiation dose), and a radiation exposure guide (e.g., target or threshold) can be assigned to be used later in the process. The assigned guideline value for that particular patient can be selected from guidelines preloaded into the system, determined by artificial intelligence (AI) and / or machine learning algorithms, and / or manually entered by an operator. The assigned guideline can be based on empirical data reported in, for example, professional literature or contrast agent package insert materials.

[0191] In some examples, the patient risk assessment also identifies other or secondary scan / injection parameters that should be optimized after the radiation dose. For example, the contrast agent dose can be optimized to limit the patient's risk of extravasation when the volume or flow rate of the contrast agent reaches or exceeds a certain level. To optimize the contrast agent dose, the system assigns a contrast agent dose guideline (e.g., Figure 3H A target, threshold, or goodness function) may be used later in the process. Contrast media dosage guidelines may be determined in a manner similar to that described above. An example of such guidance for contrast media dosage that may be obtained from industry literature may be found in the American College of Radiology's Handbook of Contrast Media at http: / / www.acr.org / - / media / acr / files / clinical-resources / contrast_media.pdf.

[0192] As previously described, calculation of the optimal study protocol begins at block 618, where a baseline protocol for a model patient shares certain patient demographics (e.g., size, weight, etc.) of the subject patient. Such a baseline protocol may include both scan parameters (e.g., kVp and mA) and injection parameters (e.g., mL, flow rate) that are capable of achieving diagnostic image quality in the model patient. The baseline protocol may be determined according to any known method, including using Figures 1A-2BAs previously described, the baseline regimen is expected to provide adequate enhancement at a reasonable radiation dose for otherwise healthy patients of the same size, weight, and gender as the subject patient.

[0193] At box 620, the baseline protocol can be optimized by analyzing the baseline protocol in view of the patient risk assessment discussed above. In this particular example, the analysis involves determining the radiation dose to be delivered according to the baseline protocol based on the scan / injection parameters established for the baseline protocol. The system can then compare this radiation dose value with the radiation exposure guideline value assigned earlier in the process. If the calculated radiation dose expected to be delivered using the baseline protocol exceeds the radiation exposure guideline value or the risk value selected by the user, the system modifies the scan parameters of the baseline protocol, as shown in box 622, to reduce the radiation dose by a certain amount so as to be at or below the radiation exposure guideline. This can be done, for example, by changing kVp, scan length, region of interest, radiation direction (e.g., posterior or anterior vs. full circle), etc. As part of this step, the system can also modify other parameters, including injection parameters, as needed to achieve a potentially acceptable imaging protocol. It can also be considered whether the modified study protocol can achieve an enhancement level sufficient to present an acceptable image. Alternatively or additionally, the system can suggest operator actions: center the patient and use bismuth shielding for the breast if this will significantly reduce the dose. Additional example recommendations can be found in Application of Different methods for Reducing Radiation Dose to Breast during MDCT, PMCID: PMC6280114; PMID: 30568923, available online: https: / / www.ncbi.nlm.nih.gov / pmc / articles / PMC6280114 / #:~:text=PMCID%3A%20PMC6280114-,PMID%3A%2030568923,-Application%20of%20Different.

[0194] Once the scan parameters have been adjusted, the system can again determine the radiation dose to be delivered in the first modified protocol, as indicated by arrow 624. After this calculation is completed, at box 626, the system can again compare the radiation dose to the radiation exposure guideline value. If the radiation dose of the first modified protocol remains above the radiation exposure guideline value, the system can again modify the scan parameters to attempt to reduce the radiation dose so that it is at or below the radiation exposure guideline value, thereby repeating certain of the above steps. However, if the recalculated radiation dose is equal to or below the radiation exposure guideline value, the system proceeds to the next step, which involves evaluating one or more secondary risk factors, in this case, whether the contrast agent dose is above the guideline or the risk of extravasation, as shown in box 628. In this particular example, both the contrast agent volume and flow rate, which are directly related to the risk of extravasation, are handled by a single model, so they are optimized in a single step.

[0195] In this particular example, the secondary risk factor assessment involves analyzing whether the contrast agent dose to be delivered in the first modified scheme will be equal to or lower than the contrast agent dose guideline and the risk of extravasation is equal to or lower than its guideline. In order to perform this assessment, the system can compare the contrast agent dose parameters of the first modified scheme with the contrast agent dose guideline values ​​assigned above. If the contrast agent dose parameters of the first modified scheme are higher than the contrast agent dose guideline values, or the flow rate is high enough so that the risk of extravasation will be considered unacceptably high, another iteration can be performed with slightly reduced image enhancement (Houhans units) or image quality through step 622. Then, the system can modify the injection parameters of the first modified scheme to reduce the contrast agent dose and the risk of extravasation. Once the injection parameters are adjusted, the system can calculate the scan and injection parameters required to achieve the diagnostic enhancement value again, and can determine and evaluate the radiation dose to be delivered in the second modified scheme again, thereby iterating the above steps with the modified values. On the other hand, if the contrast agent dose is equal to or lower than the contrast agent dose guide value, the system can proceed to the next step, which involves calculating the image quality (e.g., Hounsfield units, noise, etc.) achievable with the updated scheme, as shown in box 630. In this step, the system can confirm that the application of the study scheme will allow images of sufficient quality for diagnostic purposes. If the system determines in box 632 that an acceptable image is achievable, the scheme can be used to perform the study on the subject patient, as shown in box 634. However, if the system determines that an acceptable image is not achievable, the system can perform further iterations by adjusting (usually increasing) the contrast agent dose and / or radiation dose and performing the above steps again. Alternatively, the system can suggest the use of a different scanner and / or modality. If multiple iterations have been performed without reaching an acceptable optimized or modified patient-specific scheme, the alternative may be particularly suitable. Alternatively, the system can notify the user of the situation, and the user can continue the study because the user believes that the given patient travel time, cost, and other factors and related trade-offs that have not been modeled by the system will be sufficient.

[0196] Figure 6B 6 is a flow chart illustrating an iterative process for determining a patient-specific protocol for another patient. In this example, existing data regarding the patient and study type indicate that the patient is an elderly patient undergoing a head and neck study, as shown in block 636. The patient has limited renal function, and the study type may include one or more of first pass, arterial, venous, parenchymal, late enhancement. Therefore, the study is Fig. 6A The difference in the studies in the is that the patients were older men (as opposed to younger women) undergoing head and neck studies (as opposed to lung studies). These two patient groups have different "importances" or weights placed on the various risks that occur.

[0197] and Fig. 6ASimilar to the example in , the system utilizes the available data to calculate a patient risk assessment to determine what controllable scan / injection parameters (e.g., radiation dose, contrast agent dose) should be optimized and the order of optimization (e.g., priority), as shown in block 638. For this particular patient (elderly, limited renal function), the patient risk assessment indicates that the process should first attempt to optimize / limit contrast agent dose, and the system assigns contrast agent dose guidelines (e.g., values) to be used later in the process. As described above, the contrast agent dose can be optimized in the same manner as described above. Fig. 6A Contrast media dosing guidelines are determined in a similar manner. The guidelines may be preloaded into the system, determined by the AI, and / or manually entered by the operator, and may be based on, for example, empirical data reported in the professional literature or in contrast media package insert materials. An example of such guidance on contrast media dosing that may be obtained from industry literature is available in the American College of Radiology's Handbook of Contrast Media at http: / / www.acr.org / - / media / ACR / files / clinical-resources / contrast_media.pdf.

[0198] exist Figure 6B In the example of , the patient risk assessment also identifies radiation dose as a second scan / injection parameter that should be optimized. The system assigns a radiation dose guideline (e.g., a value) for use later in the process (e.g., for use in a radiation dose comparison step 640). The radiation dose guideline can be determined in a similar manner as discussed above.

[0199] and Fig. 6A Similar to the example in , at box 642, a scout scan is performed on the patient to determine the length of the scan area (in cm), the scan time, the length of the plateau phase (useful), and the boost time of the bolus. Fig. 6A Similar to the example in , calculation of the optimal study protocol can begin with a baseline protocol for a model patient that shares certain patient demographics (e.g., size, weight, etc.) of the subject patient, as shown in block 644. Such a baseline protocol can include both scan parameters (e.g., kVp and mA) and injection parameters (e.g., mL, flow rate) that are capable of achieving diagnostic image quality in the model patient. The baseline protocol can be determined according to any known method, including those described above, such as Software, CARE Bolus, e.g. Figures 1A-2B The baseline regimen is expected to provide adequate enhancement at a reasonable radiation dose for otherwise healthy patients of the same size, weight, and gender as the subject patients.

[0200] The baseline regimen can then be optimized by analyzing the baseline regimen in light of the patient risk assessment discussed above. In this example, this involves first comparing the contrast agent dose of the baseline regimen with the contrast agent dose guideline value assigned to this particular patient earlier in the process, as shown in box 645. If the calculated contrast agent dose of the baseline regimen exceeds the contrast agent dose guideline value, the system modifies the injection parameters of the baseline regimen to reduce the contrast agent dose by a certain amount so as to be at or below the contrast agent dose guideline. This can be done, for example, by changing the flow rate, iodine concentration and / or injection duration, as will be understood by those skilled in the art. As part of this step 647, the system can also modify other parameters, including scanning parameters, as needed to achieve a potentially acceptable study regimen. For example, kVp can be reduced to increase the ratio of Hounsfield units per (milligrams of iodine per milliliter), so that less contrast agent can be used, where the increase in mA is to reduce image noise, so that fewer Hounsfield enhancement units are required, thereby further reducing the contrast agent dose, but at the expense of increasing the radiation dose. It can be considered whether the modified study regimen can achieve an enhancement level sufficient to present an acceptable image.

[0201] Once the injection parameters are adjusted, the system can again compare the contrast agent dose to the contrast agent dose guideline value, as shown in block 646. If the contrast agent dose of this first modified protocol remains above the contrast agent dose guideline value, the system can again modify the injection parameters and / or contrast agent dose parameters to attempt to reduce the contrast agent dose so that it is at or below the contrast agent dose guideline value. However, if the recalculated contrast agent dose is equal to or below the contrast agent dose guideline value, the system proceeds to the next step, which involves an assessment of a secondary risk factor, in this case, radiation dose, as shown in block 640.

[0202] In this example, the secondary risk factor assessment involves analyzing whether the radiation dose to be delivered in the first modified scheme will be at or below the radiation dose guidelines. To perform this assessment, the system compares the radiation dose parameters of the first modified scheme with the radiation dose guideline values ​​assigned above, as shown in box 640. If the radiation dose parameters of the first modified scheme are higher than the radiation dose guideline values, the system can modify the scanning parameters of the first modified scheme to reduce the radiation dose. Once the scanning parameters are adjusted, the system can calculate the scanning and injection parameters required to achieve the diagnostic enhancement value again, as shown in box 645, and can determine and evaluate the contrast agent dose to be delivered in the second modified scheme again, thereby iterating the above steps with the modified values. On the other hand, if the radiation dose is at or below the radiation dose guideline value, the system can proceed to the next step, which involves calculating the image quality (e.g., Hounsfield units, noise, etc.) achievable with the updated scheme, as shown in box 648. In this step, the system can confirm that the application of the study scheme will allow images of sufficient quality for diagnostic purposes. If the system determines that an acceptable image is achievable, as indicated at block 632, the protocol may be used to perform a study on a subject patient, such as Figure 6B 633 in FIG. 64. However, if the system determines that an acceptable image is not achievable, the system may perform further iterations by adjusting (typically increasing) the contrast agent dose and / or radiation dose and performing the above steps again. Alternatively, the system may suggest using a different scanner and / or modality. This alternative may be particularly appropriate if multiple iterations have been performed without reaching an acceptable solution.

[0203] Figure 6C is a flow chart of a method for developing a study protocol according to an iterative process for another exemplary patient. In this example, existing data regarding the patient and study type indicate that the patient is an elderly person undergoing a head and neck study, as shown in block 650. The patient has previously received intravenous (IV) chemotherapy, and the study type may be one or more of first pass, arterial, venous, and parenchymal. Thus, the imaging procedure is similar to Fig. 6A and 6B The example in FIG. 65 differs in that the patient is an elderly person undergoing IV chemotherapy. Chemotherapy damages veins and is therefore a significant risk factor for extravasation. At step 652, a scout scan may also be performed to determine scan information such as the length of the scanned area, the scan time, an estimate of the length of the desired plateau phase, and the boost time of the bolus.

[0204] and Fig. 6A and 6BSimilar to the example in, the system uses available data of patient information and / or available data obtained from the scout scan to calculate a patient risk assessment, as shown in box 654, to determine what controllable scan / injection parameters (e.g., radiation dose, contrast agent dose) should be optimized and the order of optimization (e.g., priority). For this particular patient (elderly, IV chemotherapy), the patient risk assessment indicates that the patient has a high risk of extravasation. Therefore, the process first attempts to optimize / limit parameters consistent with the risk of extravasation (e.g., contrast agent dose, contrast agent viscosity and flow rate) by assigning an extravasation guide (e.g., values ​​that can be based on contrast agent dose, viscosity and flow rate) to the patient for use later in the process. The extravasation guide can be pre-loaded into the system, determined by artificial intelligence (AI) or machine learning, and / or manually entered by the operator, and can be based on, for example, empirical data reported in professional literature or contrast agent packaging insert materials, instructions for use (IFU) and / or medication orders. The patient risk assessment can also identify radiation dose as a second scan / injection parameter that should be optimized. The system assigns a radiation dose guide (e.g., value) to be used later in the process. Radiation dose guidelines may be determined in a similar manner as discussed above.

[0205] and Fig. 6A and Figure 6B Similar to the example in , calculation of the optimal study protocol may begin with a baseline protocol for a model patient that shares certain patient demographics (e.g., size, weight, etc.) of the subject patient, as shown in block 656. Such a baseline protocol may include both scan parameters (e.g., kVp and mA) and injection parameters (e.g., mL, flow rate) that are capable of achieving diagnostic image quality in the model patient. As in the previous example, the baseline protocol may be determined according to any known method, including, for example, Software, CARE Bolus, etc. Figures 1A-2B The generated baseline regimen is expected to provide adequate enhancement at a reasonable radiation dose for otherwise healthy patients of the same size, weight, and gender as the subject patients.

[0206] The optimization of the baseline protocol can then be performed by analyzing the baseline protocol in view of the patient risk assessment discussed above. As shown in box 658, this involves first comparing the extravasation risk of the baseline protocol with the extravasation guideline value assigned earlier in the process. If the extravasation risk of the baseline protocol exceeds the extravasation guideline value, the system modifies the injection parameters of the baseline protocol to reduce the contrast agent dose, viscosity and / or flow rate by a certain amount so as to make the extravasation risk equal to or below the extravasation guideline. In some examples, the system can also modify other parameters as needed, including scanning parameters, to achieve a potentially acceptable study protocol. It can be considered whether the modified study protocol can achieve an enhancement level sufficient to present an acceptable image.

[0207] Once the injection parameters have been adjusted, the system can again compare the extravasation risk to the extravasation guideline value (box 658). If the extravasation risk of the first modified regimen remains unacceptably high, the system can again modify the injection parameters in an attempt to reduce the extravasation risk so that it is equal to or below the extravasation guideline value, thereby repeating some of the above steps. However, if the recalculated extravasation risk is acceptable, the system proceeds to the next steps 660-664, which involve an assessment of secondary risk factors, which in this case are image quality and radiation dose. After some reasonable number of adjustment cycles, if the risk is still unacceptable, the system can alert the operator to the situation and request guidance or intervention. Alternatively, the system can proceed with a risk greater than expected and Figure 5C and 5D The achievable results are indicated on the user interface shown in .

[0208] In this example, as shown in box 664, the secondary risk factor assessment initially involves an analysis of whether the image quality to be achieved in the first modified protocol will be at or below the image quality guidelines. To perform this assessment, the system can compare the image quality of the first modified protocol with the image quality guideline values ​​assigned above. If the image quality parameters of the first modified protocol are above the image quality guideline values, the system can modify the scanning parameters of the first modified protocol to increase the image quality. Once the scanning parameters are adjusted, the system can again calculate the scanning and injection parameters required to achieve the diagnostic enhancement value, and can again determine and assess the risk of extravasation in the second modified protocol, thereby iterating the above steps with the modified values. On the other hand, if the image quality is at or below the image quality guideline values, the system can proceed to the next step shown by box 660, which involves calculating the radiation dose achievable using the updated protocol. In this step, the system can confirm that the application of the study protocol will allow a sufficiently low radiation dose. If the system determines that an acceptable radiation dose is achievable, the protocol can be used to perform the study on the subject patient, as shown in box 668. However, if the system determines that an acceptable radiation dose is not achievable, the system may perform further iterations by adjusting (typically increasing) the contrast agent dose or reducing image quality and performing the above steps again. Alternatively, the system may suggest using a different scanner and / or modality. This alternative approach may be particularly appropriate if multiple iterations have been performed without reaching an acceptable solution.

[0209] Fig.6D6 is a flow chart of a method for developing a study protocol according to an iterative process for another exemplary patient that is assumed to be MRI compatible. In this example, as shown in block 670, existing data regarding the patient and study type indicate that the patient is an elderly person undergoing a head and neck study. The patient has limited renal function. The study type may be one or more of a first-pass, arterial, venous, parenchymal, or late enhancement MR study. Thus, the imaging procedure is consistent with Figures 6A-6C The example in FIG. 67 differs in that the study is MRI rather than CT, so the primary tradeoff is the duration of the scan rather than the radiation dose, although the SAR (specific absorption rate) and total energy deposited in the patient do need to be limited. There are no known long-term hazards of MRI procedures. At step 672, a scout scan may also be performed to determine scan information such as the length of the scanned area, the scan duration, the length of the plateau phase, and the boost time of the bolus.

[0210] and Figures 6A-6C Similar to the example in, the system utilizes available data of patient information and / or available data obtained from the reconnaissance scan to calculate a patient risk assessment, as shown in box 674. The risk assessment can be based on, for example, patient characteristics and needs (e.g., age and renal function). For example, the risk assessment can consider whether the risks including extravasation, contrast agent dose, SAR, patient movement and / or patient claustrophobia are the most important and / or whether they should be addressed for a particular patient. As in the previous example, at step 674, the risk assessment determines what controllable scan / injection parameters (e.g., SAR, contrast agent dose, scan time) should be optimized and the order of optimization (e.g., priority). For this particular patient (elderly, limited renal function, MRI compatible), the patient risk assessment indicates that the risk from the MR contrast agent dose should be optimized or mitigated, which can be achieved by reducing the gadolinium dose by a predetermined amount (e.g., 2%, 5% or 10%) to reach the guideline value for the contrast agent dose. Guideline values ​​for MR contrast agent doses can be based on, for example, clinical evidence (e.g., similar procedures performed on similar patients), protocols developed by professional societies, literature, manufacturers (e.g., contrast agent package inserts), and / or derived through artificial intelligence. As described below, other risk parameters that can be optimized after MR contrast agent doses can include scan duration and specific absorption rate (SAR) for radiation dose. Risk parameters can also relate to image quality (e.g., adjusting the signal-to-noise ratio to improve image quality).

[0211] and Figures 6A-6CSimilar to the example in , calculation of the optimal study protocol may begin with a baseline protocol for a model patient that shares certain patient demographics (e.g., size, weight, etc.) of the subject patient, as shown in block 676. Such a baseline protocol may include scan parameters (e.g., SAR and scan duration) and injection parameters (e.g., mL, flow rate) that enable diagnostic image quality in the model patient. As in the previous example, the baseline protocol may be determined according to any known method, including, for example, software, myExam Companion from Siemens MR (https: / / www.siemens-healthineers.com / en-us / magnetic-resonance-imaging / technologies-and-innovations / my-exam-companion), etc. Figures 1A-2B The generated baseline protocol is expected to provide adequate enhancement at reasonable SAR and scan duration for otherwise healthy patients of the same size, weight, and gender as the subject patient.

[0212] The baseline scheme can then be optimized by analyzing the baseline scheme in view of the patient risk assessment discussed above. As shown in box 678, this involves first comparing the MR contrast agent dose value of the baseline scheme with the value provided by the contrast agent dose guide assigned earlier in the process. If the risk of the baseline scheme exceeds the amount allowed by the MR contrast agent dose guide, the system modifies the injection parameters of the baseline scheme to reduce the contrast agent dose. For example, as previously described, the dose of gadolinium-based contrast agents can be iteratively reduced by a predetermined amount to comply with the guidelines. In some examples, AI tools can be used to reduce the dose by a larger amount, such as 10% to 90%. In other examples, modifying the contrast agent dose can also include using imaging agents with higher relaxation rates, using different studies with different pulse sequences that provide different sensitivities to contrast agents, skipping contrast agents and using different studies, recommending different imaging modalities, or applying special precautions to specific patients before or after treatment (e.g., dialysis). In some examples, the system can also modify other parameters, including scanning parameters, as needed to achieve a potentially acceptable study scheme. It can be considered whether the modified study scheme can achieve an enhancement level sufficient to present an acceptable image.

[0213] Once the injection parameters have been adjusted, the system may again compare the contrast dose value to the risk value for the contrast dose (block 678). If the contrast dose risk of the first modified regimen remains unacceptably high, the system may again modify the injection parameters to attempt to reduce the contrast dose risk so that it is at or below the guideline value, thereby repeating some of the above steps. However, if the recalculated contrast dose risk is acceptable, the system proceeds to the next step 680, which involves an evaluation of secondary risk factors, which in this case are scan duration and SAR.

[0214] In this example, as shown in box 680, the secondary risk factor assessment involves analyzing whether parameters such as scan duration and SAR in the first modified protocol will be at or below the guidelines. To make this assessment, the system can compare the scan duration and SAR parameters of the first modified protocol with the scan duration and SAR guideline values ​​assigned above. If the radiation dose parameters of the first modified protocol are above the radiation dose guideline values, the system can modify the scan parameters of the first modified protocol to reduce the radiation dose. In some examples, the radiation dose can also be reduced or mitigated by using better scanners, applying AI to optimize radiation dose, or utilizing other modalities.

[0215] Once the scan parameters are adjusted, the system can again calculate the scan and injection parameters required to achieve the diagnostic enhancement value, and can again determine and evaluate the contrast agent dose and scan duration and SAR risk in the second modified protocol, thereby iterating the above steps with the modified values. On the other hand, if the scan duration and SAR are equal to or lower than the scan duration and SAR guideline values, the system can proceed to the next step shown by box 682, which involves calculating the image quality (e.g., signal and signal-to-noise ratio, noise, etc.) achievable using the updated protocol. In this step, the system can confirm that the application of the study protocol will allow images of sufficient quality for diagnostic purposes. If the system determines that acceptable images are achievable, the protocol can be used to perform the study on the subject patient, as shown in box 684. After performing the study, the patient can be monitored and / or dialysis can be performed to address concerns about the effect of the contrast agent dose on the patient's renal function.

[0216] If the system determines that an acceptable image is not achievable, the system may perform further iterations by adjusting the scan pulse sequence and performing the above steps again. Alternatively, the system may suggest using a different scanner and / or modality. This alternative may be particularly appropriate if multiple iterations have been performed without reaching an acceptable solution.

[0217] In each of the examples above, the system develops a study protocol through a method in which patient risks are addressed in a sequential and (if necessary) iterative process by identifying and prioritizing risks and then adjusting appropriate study parameters to reduce risks to acceptable thresholds. Although not explicitly listed in any of the above examples, the system can use the results of a test bolus as described above and can suggest that a test bolus can be performed to provide additional information for better optimization. For example, the system can recommend the performance of a test bolus as described in U.S. Patent Application Publication No. 2022 / 0133982A1, entitled “System And Methods For Delivering A Test Bolus For Medical Imaging,” which is incorporated herein by reference. In addition to addressing risks to patients, image quality is also considered part of the iterative process to ensure that the study protocol can achieve images of sufficient quality so that they are useful from a diagnostic perspective. The risk of a poor or non-diagnostic study is a risk to the patient.

[0218] Fluid Injector System

[0219] The modified protocols generated using the systems and methods of the present disclosure can be used to obtain images using fluid injectors and image scanner devices and systems known in the art, such as Figure 8A-11 As shown. For example, once the modified scheme is created and verified, the scheme can be downloaded and stored on the device memory of the fluid injector and / or scanner. Once the modified scheme is saved to the memory of the injector and / or scanner, the system can continue to perform their corresponding functions to obtain images according to the modified scheme. In some examples, the modified scheme is generated away from the fluid injector and scanner devices. For example, computer software and / or computer systems for generating and optimizing the modified scheme can be stored on a cloud server or another remote computer device. The modified scheme can be downloaded and configured to work with the fluid injector device when ready for use. In other examples, the software for generating the initial or baseline scheme and the modified scheme can be integrated with the memory of the fluid injector or scanner and / or stored on the memory of the fluid injector or scanner. When the fluid injector is activated, the software can be configured to process the available data to generate the modified scheme, and once the final modified scheme is created, the operating parameters of the fluid injector and scanner are adjusted according to the modified scheme.

[0220] Figure 7-11Features of exemplary fluid injectors and fluid injector systems are shown in FIG, which may be suitable for use with the methods and techniques for protocol generation described herein. In addition, although the following description is primarily directed to a CT injection system, it is also recognized that the techniques and methods disclosed herein may be applied to a variety of other injection systems. Examples of such injection systems include those provided by Bayer HealthCare LLC. Stellant and Stellant FLEX CT injection system, MRXperion MR injection system, Mark 7 Arterial Infusion System and Centargo CT Infusion System.

[0221] Figure 7 A schematic diagram of an exemplary fluid injector system 700 having at least one reservoir (such as a syringe 732) connected to a fluid path kit is shown. The fluid path kit can be a single-use disposable kit (SUDS) 790. At least one syringe 732 can be configured to be filled with at least one fluid F, such as a contrast agent, a saline solution, or any desired medical fluid. In particular, the contrast agent can be a contrast agent dose selected according to a modified regimen for a specific patient as described above. The SUDS 790 can be used to deliver at least one fluid F from at least one syringe 732 to the patient. At least one syringe 732 can be pre-filled, or it can have the ability to be filled with at least one fluid F. At least one syringe 732 can be, for example, a rolling diaphragm syringe, a bottle, or a collapsible bag.

[0222] The system 700 also includes a fluid injector 701, such as an automatic or powered fluid injector, configured to deliver fluid F from a syringe 732 to a patient. For example, the injector 701 can be configured to drive a plunger 744 of a syringe 732 with a drive member 703, such as a piston, to deliver fluid F from the syringe 732 via the fluid path kit 790 at an injection speed consistent with a modified regimen generated for the patient. At least one drive member 703 can be reciprocally operable to selectively fill at least one syringe 732 or deliver fluid F from at least one syringe 732. In some examples or aspects, the injector 701 can be configured to releasably receive a syringe 732. The injector 701 can be a multi-syringe injector, in which several syringes can be oriented side by side or in another spatial relationship and are individually actuated by corresponding pistons associated with the injector 701.

[0223] The flow of fluid from at least one syringe 732 can be regulated by a fluid control module or controller 723, which is configured to operate various valves, stopcocks, and flow regulation structures to adjust the delivery of at least one fluid F to the patient based on the infusion parameters (e.g., infusion flow rate, duration, and total infusion volume) of the modified regimen being implemented.

[0224] exist Fig. 8A and 8B Another exemplary fluid injector system 700 is shown in FIG. 7 , which may be adapted to perform injections and be used in conjunction with a scanner to obtain images according to a modified approach as described herein. Unlike the previous examples, a syringe or fluid reservoir 732 is positioned in the injector 701, as shown in FIG. Figure 8B As shown. An exemplary fluid injector system 700 includes a powered fluid injector 701 connected to a fluid delivery kit that is intended to be associated with the injector device 701 to deliver fluid under pressure from one or more single-dose or multi-dose containers and a fluid path kit into a patient. The fluid injector 701 includes an injector housing 702 having opposing lateral sides 704, a distal or upper end 706, and a proximal or lower end 708. The housing 702 encloses various mechanical drive components, electrical and power components required to drive the mechanical drive components, and a reciprocally movable drive member (such as a drive member 703 (in the case of a fluid injector system 700) associated with the fluid injector system 700) . Figure 7 and Fig. 9 ))) of the control components of the operation, such as electronic memory and electronic control device (hereinafter referred to as (multiple) electronic control devices). Such a drive member 703 can be reciprocated via an electromechanical drive component, such as a ball screw shaft driven by a motor, a voice coil actuator, a rack and pinion drive, a linear motor, etc.

[0225] The fluid injector system 700 may further include at least one bulk fluid connector 718 for connecting to at least one bulk fluid source 720. Alternatively, the fluid source may be a single-dose vial rather than a bulk source. In some examples or aspects, a plurality of bulk fluid connectors 718 may be provided. For example, Fig. 8A and 8BAs shown, three body fluid connectors 718 can be provided side by side or in other arrangements. In some examples, at least one body fluid connector 718 can be a spike that is configured to be removably connected to at least one body fluid source 720, such as a vial, bottle, or bag. At least one body fluid connector 718 can have a reusable or non-reusable interface with each new body fluid source 720. At least one body fluid source 720 can be configured to receive a medical fluid, such as saline, imaging contrast solution, or other medical fluid, for delivery to the fluid injector system 700. The housing 702 can have at least one support member 722 for supporting at least one body fluid source 720 when the at least one body fluid source 720 is connected to the fluid injector system 700.

[0226] refer to Figure 8B , the fluid injector system 700 may further include disposable components for delivering fluid to a patient. For example, the fluid injector system 700 may include a multiple use disposable set or MUDS 730 positioned inside the housing 702. Examples and features of a MUDS are described in detail in International Application No. WO 2016 / 112163, entitled “Multiple Fluid Delivery System with Multi-Use Disposable Set and Features Thereof,” the disclosure of which is incorporated herein by reference.

[0227] In some examples, the MUDS 730 can include one or more syringes or pumps 732 connected to and / or in fluid communication with the body fluid source(s) 720 via the MUDS fluid pathways 734. In some examples, the number of syringes or fluid reservoirs 732 can correspond to the number of body fluid sources 720. For example, the MUDS 730 can include three syringes / fluid reservoirs 732 arranged side by side such that each syringe / reservoir 732 can be fluidly connected to one or more of the body fluid sources 720.

[0228] The MUDS 730 can be removably connected within the housing 702 of the fluid injector system 700 for delivering one or more fluids from one or more body fluid sources 720 to the patient. As described in further detail herein, the fluid injector system 700 can also include a sensor for identifying when the MUDS 730 is connected to and / or removed from the fluid injector system 700.

[0229] The MUDS 730 can be configured to deliver fluid to a fluid pathway kit, such as a SUDS 790, as described herein. To establish fluid communication between the MUDS 730 and the SUDS 790, the fluid injector system 700 can further include at least one slot or connection port 728 for releasably connecting a single-use connector or disposable kit (such as a SUDS 790) to the MUDS 730.

[0230] The SUDS 790 may include, for example, a connector configured to be received by the connection port 728. The connection port 728 may include a sensor for identifying when the SUDS 790 is connected to the port 728. The SUDS 790 may also include a patient line for delivering fluid from the MUDS 730 to the patient. An exemplary SUDS 790 is described and illustrated in International Application Publication No. WO 2015 / 106107, entitled “Single-Use Disposable Set Connector,” which is incorporated herein by reference.

[0231] Fig. 9 A schematic diagram of the components of a MUDS 730 enclosed within a housing 702 of a fluid injector system 700 is shown. Fig. 9 As shown, a syringe plunger 744 is disposed within each syringe 732 and reciprocates within the syringe 732 due to movement of a drive member 703 associated with the fluid injector system 700. Each syringe 732 is in fluid communication with a valve 736, which provides fluid communication with a manifold 748 and a body fluid connector 718. The manifold 748 can also provide support for the syringe / fluid reservoir 732 so that the syringe / reservoir 732 can be handled as a single integral structure. The manifold 748 can be in fluid communication with a first end of a MUDS fluid path 734 via the valve 736 and / or the syringe / reservoir 732, and the first end of the MUDS fluid path 734 connects each syringe / reservoir 732 to a corresponding body fluid source 720. The opposite second end of the MUDS fluid path 734 can be connected to a corresponding body fluid connector 718, which is configured to be fluidly connected to the body fluid source 720.

[0232] In some examples, when not connected to the patient's catheter (e.g., before fluid injection), the patient line 752 of the SUDS 790 can be connected to a waste reservoir 756 on the fluid injector system 700. The waste reservoir 756 is ideally separated from the syringe / fluid reservoir 732 to prevent contamination. In some examples, the waste reservoir 756 is configured to receive waste fluid discharged from the syringe / reservoir 732 during, for example, a priming operation. The waste reservoir 756 can be removed from the housing 702 so that the contents of the waste reservoir 756 are discarded. In other examples, the waste reservoir 756 can have a discharge port (not shown) for emptying the contents of the waste reservoir 756 without removing the waste reservoir 756 from the housing 702. In some examples, the waste reservoir 756 is provided as a separate component from the MUDS 730.

[0233] Exemplary fluid injection systems also include those disclosed in the following patents: US Pat. No. 6,643,537; US Pat. No. 7,094,216; US Pat. No. 7,556,619; US Pat. No. 8,337,456; US Pat. No. 8,147,464; and US Pat. No. 8,540,698, the disclosures of each of which are incorporated herein by reference.

[0234] refer to Fig.10 , the fluid injector system 700 can be configured for use in an environment 810 including a scanning room 812 and a control room 814. For example, the fluid injector system 700 can be a bifurcated system in which some functions, processing, and control operations are performed by equipment located in the scanning room 812, and other functions, processes, and control operations are performed by equipment, processors, and displays located in the control room 814. In particular, processes associated with determining a modified protocol can be executed on a controller and other processors in the control room 814. Once the modified protocol is determined, the modified protocol can be provided to a computing device in the scanning room 812, which can be configured to control the fluid injector and image scanner to obtain images according to the modified protocol. In some examples, the devices in different rooms 812, 814 can communicate via a wired or wireless computer network 816.

[0235] like Fig.10As shown, the fluid injector 701 and associated control device 723 are located in the scanning room 812. The control device 723 can be configured to provide a scanning room user interface 718 for controlling the injector 701 from the scanning room 812. The scanning room user interface 718 can be located on the fluid injector 701 and can include, for example, a display 920 on the injector housing 702, such as a touch screen display and associated buttons for operating the injector 701. A user, such as a medical technician, can view injection parameters, such as parameters of a modified protocol, and perform other actions to prepare for an injection. The scanning room user interface 718 can also provide feedback to the user, such as feedback notifying the user when the injector 701 is primed and ready to begin executing the injection protocol.

[0236] In some examples, the control room 814 can be a shielded control room outside the scanning room 812. From the control room 814, a user (e.g., a medical technician) can monitor the fluid injector system 700 in a safe and convenient location during an injection protocol. The control room 814 may include a computer device, such as a computer terminal, that includes one or more controllers or processors for controlling the operation of the fluid injector system 700 and the fluid injector 701 from the control room 814. The computer device or terminal in the control room 814 can provide a control room user interface 820 that allows a user to input instructions to the system 700 and the injector 701 and receive feedback from the fluid injector system 700. The feedback can include information about the progress of the injection protocol, and, for example, a confirmation when the injection protocol is completed. The feedback can be provided on a visual display in the control room 814.

[0237] Some processes related to the generation of baseline and / or modified protocol studies can be performed by a computer device or terminal in the control room 814. For example, the computer terminal in the control room 814 can be configured to receive information about the patient and / or the results of the scout scan. The computer terminal can be configured to generate a baseline protocol or a modified protocol based on the received information. Once the protocol is generated, the computer terminal can be configured to cause the fluid injector 701 and other medical devices to execute the protocol.

[0238] refer to Fig.11 , the electrical components of the fluid injector system 700 are shown and described in detail. As previously discussed, some electrical components and processing circuits may be located in the scanning room 812. Other electrical components and processing circuits of the fluid injector system 700 may be located in or accessible from the control room 814.

[0239] In some examples, the fluid injector system 700 includes at least one control device, such as a computer processor 910, which can be configured to generate or modify a protocol using the methods described herein. In some examples, the control device or processor 910 is a controller 723 ( Figure 7 In other examples, such as Fig.11 As shown, the control device or processor 910 can be a separate processing component that is remote from and in communication with the controller 723 of the injector 701. For example, the processor 910 can be a component of a control terminal 914 located in the control room 814 or at another location remote from the injector 701. The computer terminal 914 can be configured to control the fluid injector system 700. In other examples, the processor 910 can be a component of a general purpose computer device (such as a computer tablet, smart phone, or laptop computer) that communicates with the controller 723 of the injector 701 and is configured to receive information therefrom.

[0240] like Fig.11 As shown, the control terminal 914 may include a processor 910, a visual display 920, a system memory 918 for storing information about injector protocols, and one or more input devices 912 for inputting information about the injection protocol being executed. The at least one processor 910 of the fluid injector system 700 is configured to receive or determine information about the injection procedure being executed by the fluid injector system 700. For example, the information may include the time at which the injection was performed.

[0241] Fig.12 1 is an example image creation system 1010 according to certain non-limiting embodiments of the present disclosure. System 1010 includes an injector device 1012, a scanning or imaging device 1014, and an optimization computer or engine 1016. The injector device 1012 in this example also includes a patient monitoring device, such as a patient monitoring system as described in International Application Publication No. WO2021 / 222771A1 entitled "System, Device, And Method for Safeguarding Wellbeing of Patients for Fluid Injection", which is incorporated herein by reference. Such a patient monitoring device can collect information about the patient 1002 before and / or during the imaging procedure. The optimization computer or engine 1016 can use this information in the optimization process of the present disclosure, including recommendations for risk reduction actions. Fig.12The imaging device 1014 in this example of further includes an imager camera 1018 for monitoring the patient 1002. The imaging device 1014 may further include an ECG monitor (not shown) or other sensors (not shown) for monitoring the patient 1002. Similarly, these sensors can collect information before and / or during the imaging procedure. Such information (e.g., heart rate, tendency to move, or respiratory rate) is most conveniently and effectively collected by (multiple) such sensors before the procedure, and can be used by the optimization computer 1016 in the optimization process of the present disclosure to adjust risks and / or schemes, including suggestions for risk reduction actions. For example, a patient 1002 who moves significantly before the study has a higher risk of poor image quality caused by movement. The optimization computer 1016 function can be performed by a suitable computer as part of the injector 1012, the imaging device 1014, or on an external or cloud computer.

[0242] Each of the models described herein covers certain aspects of the overall imaging protocol. It is expected that new models will be developed that may cover more or fewer aspects than those described herein. For example, CT scan planning, radiation dose estimation, and image quality estimation may be covered as a single model or a series of connected or interrelated models. There may also be specific models for specific studies, such as the CT Scanning Design Model from Bayer Healthcare LLC. protocols or Siemens' FAST CAREtechnology, as listed at http: / / www.siemens-healthineers.com / it / computed-tomography / technologies-innovations / fast-care. These more inclusive models can be used as part of the present disclosure, with the potential benefits of faster protocol development, more customization and precision of the protocol to the patient, and reduced overall risk to the patient.

[0243] Although the present disclosure has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred aspects, it should be understood that such details are only for this purpose, and the present disclosure is not limited to the disclosed aspects, but on the contrary, is intended to cover modifications and equivalent arrangements. For example, it should be understood that the present disclosure contemplates that, to the extent possible, one or more features of any aspect may be combined with one or more features of any other aspect.

Claims

1. A system for generating a protocol that can be used for medical imaging research, comprising: one or more processors; A non-transitory computer readable medium having stored thereon instructions, wherein the instructions, when executed by the one or more processors, cause the system to: receiving information about a subject patient and information about an imaging study of the subject from one or more data sources; determining one or more risk factors specific to the subject patient based on the information about the subject patient and the information about the subject imaging study; selecting two or more models, wherein the two or more models include at least one model for each of at least two aspects of an imaging study of the subject; and The two or more models are applied to generate a baseline study protocol for an imaging study of the subject, wherein the baseline study protocol is based at least on information about the subject patient and the one or more risk factors, and wherein the baseline study protocol includes parameters for at least two aspects of the imaging study of the subject.

2. The system according to claim 1, wherein: The baseline study plan includes a contrast agent injection plan and an image acquisition plan, wherein the contrast agent injection plan includes at least a total contrast agent dose and a maximum flow rate, and the image acquisition plan includes at least scanning parameters, scanning duration, timing parameters for coordinating with contrast agent injection, and one or more image reconstruction algorithms.

3. The system according to claim 1, wherein: The two or more models include a plurality of models of an aspect of an imaging procedure, wherein the two or more models are configured to operate in parallel to transform the same or similar inputs.

4. The system according to claim 1, wherein: The two or more models are applied in a sequence, wherein the sequence is determined based on a desired optimization of at least one or more patient characteristics and the one or more risk factors.

5. The system according to claim 4, wherein: The system is configured to allow a user to accept or change the order based on the user's knowledge or preferences.

6. The system of claim 1, wherein the instructions, when executed by the one or more processors, will further cause the system to: One or more iterative cycles are performed through at least one of the two or more models to optimize one or more of the parameters of the baseline study protocol.

7. The system of claim 6, wherein the instructions, when executed by the one or more processors, will further cause the system to: If none of the iterative loops provide an optimized result, the results of one or more of the iterative loops are presented to a user in a selectable format.

8. The system of claim 1, further comprising a user interface wherein expected parameters of the baseline study protocol are displayed for user confirmation or further adjustment.

9. The system according to claim 8, wherein: The user interface provides one or more selectable user interface elements that allow an operator to adjust the one or more risk factors specific to the subject patient.

10. The system of claim 9, wherein at least one of the one or more selectable user interface elements is in the form of a slider that is adjustable by a user.

11. The system according to claim 10, wherein: The user interface is a graphical user interface display screen, and the one or more user interface elements are adjustable by the user touching the graphical user interface display screen.

12. The system of claim 1, wherein: At least one of the two or more models relates to at least one of a fluid injection aspect of an imaging study of the subject and an image creation aspect of the imaging study of the subject.

13. The system according to claim 12, wherein: At least two of the two or more models relate to at least one of a fluid injection aspect of an imaging study of the subject and an image creation aspect of the imaging study of the subject.

14. The system according to claim 13, wherein: A first model of the two or more models relates to fluid injection aspects of an imaging study of the subject, and a second model of the two or more models relates to image creation aspects of the imaging study of the subject.

15. The system of claim 1, wherein: The parameters include at least one of: total contrast agent volume, maximum flow rate, contrast agent delivery rate, average flow rate, contrast agent temperature, contrast agent viscosity, contrast agent concentration, IV access location, scanning area, potential applied to the X-ray tube, maximum current applied to the X-ray tube, scan speed, scan duration, radiation dose, signal-to-noise ratio, contrast agent / noise ratio or spatial / resolution ratio.

16. The system of claim 1, wherein: The information about the subject patient includes at least one of the following: height, weight, body mass index, cardiac output, gender, age, race, chest width, chest circumference, medications taken, underlying medical conditions, physical abilities, vital signs, pregnancy / expected pregnancy, genetic predisposition of the subject patient, allergies, results of previous imaging examinations for the subject patient, and known radiation sensitivity of the subject patient.

17. The system of claim 1, wherein: The one or more data sources include at least one of: an electronic medical record system including an electronic medical record (EMR) of the patient, an electronic health record (EHR) system, a patient procedure tracking system, a radiology analysis system (RAS), a digital pathology system (DPS), a picture archiving and communication system (PACS), a hospital data system, a hospital purchase order system including orders for studies to be performed on the subject patient, a database including previous scan results for the patient, a database including previous scan results for one or more other patients, or a government guideline database of acceptable radiation doses and contrast agent dose levels.

18. The system of claim 1, wherein: The information about the subject imaging study includes information about a fluid injector associated with the subject imaging study, wherein the information about the fluid injector includes information from a test injection or patency check using saline, information about the capabilities and tolerances of the fluid injector, and / or the presence of an external sensor for monitoring injections performed by the fluid injector.

19. The system of claim 1, wherein: The one or more risk factors are related to at least one of contrast agent dose, radiation dose, extravasation risk, patient discomfort, anaphylactic shock risk, and image quality.

20. The system of claim 6, wherein: The one or more iterative loops optimize one or more parameters of the baseline study protocol by applying an algorithm that minimizes or maximizes selected parameter values, an algorithm for ensuring certain parameters are within a target or threshold range, or a weighting function to the parameter values ​​of the baseline study protocol.

21. A method for generating a protocol that can be used in a medical imaging study, comprising: receiving information about a subject patient and information about an imaging study of the subject from one or more data sources; determining one or more risk factors specific to the subject patient based on the information about the subject patient and the information about the subject imaging study; selecting two or more models, wherein the two or more models include at least one model for each of at least two aspects of an imaging study of the subject; and The two or more models are applied to generate a baseline study protocol for an imaging study of the subject, wherein the baseline study protocol is based at least on information about the subject patient and the one or more risk factors, and wherein the baseline study protocol includes parameters for at least two aspects of the imaging study of the subject.

22. The method according to claim 21, wherein: The baseline study plan includes a contrast agent injection plan and an image acquisition plan, wherein the contrast agent injection plan includes at least a total contrast agent dose and a maximum flow rate, and the image acquisition plan includes at least scanning parameters, scanning duration, timing parameters for coordinating with contrast agent injection, and one or more image reconstruction algorithms.

23. The method according to claim 21, wherein: The two or more models include a plurality of models of an aspect of an imaging procedure, wherein the two or more models are configured to operate in parallel to transform the same or similar inputs.

24. The method according to claim 21, wherein: The two or more models are applied in a sequence, wherein the sequence is determined based on a desired optimization of at least one or more patient characteristics and the one or more risk factors.

25. The method of claim 24, wherein a user accepts or changes the order based on the user's knowledge or preferences.

26. The method of claim 21, further comprising: One or more iterative cycles are performed through at least one of the two or more models to optimize one or more of the parameters of the baseline study protocol.

27. The method according to claim 26, further comprising: If none of the iterative loops provide an optimized result, the results of one or more of the iterative loops are presented to a user in a selectable format.

28. The method of claim 21, further comprising: The expected parameters of the baseline study protocol are displayed on a user interface for user confirmation or further adjustment.

29. The method according to claim 21, wherein: At least two of the two or more models relate to at least one of a fluid injection aspect of an imaging study of the subject and an image creation aspect of the imaging study of the subject.

30. The method of claim 29, wherein: At least two of the two or more models relate to at least one of the fluid injection aspect of the subject imaging study and the image creation aspect of the subject imaging study.

31. The method according to claim 30, wherein: A first model of the two or more models relates to fluid injection aspects of an imaging study of the subject, and a second model of the two or more models relates to image creation aspects of the imaging study of the subject.

32. The method of claim 21, further comprising: The baseline study protocol is applied to perform the subject imaging study on the subject patient.

33. The method of claim 21, wherein: The parameters include at least one of: total contrast agent volume, maximum flow rate, contrast agent delivery rate, average flow rate, contrast agent temperature, contrast agent viscosity, contrast agent concentration, IV access location, scanning area, potential applied to the X-ray tube, maximum current applied to the X-ray tube, scan speed, scan duration, radiation dose, signal-to-noise ratio, contrast agent / noise ratio or spatial / resolution ratio.

34. The method of claim 21, wherein: The information about the subject patient includes at least one of the following: height, weight, body mass index, cardiac output, gender, age, race, chest width, chest circumference, medications taken, underlying medical conditions, physical abilities, vital signs, pregnancy / expected pregnancy, genetic predisposition of the subject patient, allergies, results of previous imaging examinations for the subject patient, and known radiation sensitivity of the subject patient.

35. The method of claim 21, wherein: The one or more data sources include at least one of: an electronic medical record system including an electronic medical record (EMR) of the patient, an electronic health record (EHR) system, a patient procedure tracking system, a radiology analysis system (RAS), a digital pathology system (DPS), a picture archiving and communication system (PACS), a hospital data system, a hospital purchase order system including orders for studies to be performed on the subject patient, a database including previous scan results for the patient, a database including previous scan results for one or more other patients, or a government guideline database of acceptable radiation doses and contrast agent dose levels.

36. The method of claim 21, wherein: The information about the subject imaging study includes information about a fluid injector associated with the subject imaging study, wherein the information about the fluid injector includes information from a test injection or patency check using saline, information about the capabilities and tolerances of the fluid injector, and / or the presence of an external sensor for monitoring injections performed by the fluid injector.

37. The method of claim 21, wherein: The one or more risk factors are related to at least one of contrast agent dose, radiation dose, extravasation risk, patient discomfort, anaphylactic shock risk, or image quality.

38. The method of claim 26, wherein: The one or more iterative loops optimize one or more parameters of the baseline study protocol by applying an algorithm that minimizes or maximizes selected parameter values, an algorithm for ensuring certain parameters are within a target or threshold range, or a weighting function to the parameter values ​​of the baseline study protocol.

39. A method for generating a protocol that can be used in a medical imaging study, comprising: receiving information about a subject patient and an imaging study of the subject from one or more data sources; generating a baseline study protocol based on information about the subject's patients and the subject's imaging studies; determining one or more risk factors specific to the subject patient based on information about the subject patient and imaging studies of the subject; and modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient, Wherein modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient comprises: An iterative process is performed to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors and generate a modified study protocol that will provide images of sufficient diagnostic quality.

40. A system for generating a protocol that can be used in a medical imaging study, comprising: one or more processors; A non-transitory computer readable medium having stored thereon instructions, wherein the instructions, when executed by the one or more processors, cause the system to: receiving information about a subject patient and an imaging study of the subject from one or more data sources; generating a baseline study protocol based on information about the subject's patients and the subject's imaging studies; determining one or more risk factors specific to the subject patient based on information about the subject patient and imaging studies of the subject; and modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient; Wherein modifying the baseline study protocol to address at least one of the one or more risk factors specific to the subject patient comprises: performing an iterative process to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors and generate a modified study protocol that will provide images of sufficient diagnostic quality.

41. A fluid injector system for administering at least one fluid to a patient in a generated patient-specific regimen, the fluid injector system comprising: a control device operably associated with the at least one drive component for pressurizing the at least one fluid into the patient through the at least one disposable component; and the control device comprising at least one processor programmed or configured to enable programming of the patient-specific protocol according to which the at least one drive component pressurizes the at least one fluid into the patient through the at least one disposable component to achieve enhancement of at least one region of interest thereof for a scan duration of a diagnostic imaging procedure, Wherein, in order to generate the patient-specific protocol, the control device is further programmed or configured to: receiving information about the patient and an imaging study to be performed from one or more data sources; generating a baseline study protocol based on information about the patient and the imaging study to be performed; determining one or more risk factors specific to the patient based on information about the patient and the imaging study to be performed; and modifying said baseline study protocol to address at least one of said one or more risk factors specific to said patient to provide said patient-specific protocol, Wherein modifying the baseline study protocol to address at least one of the one or more risk factors specific to the patient comprises: performing an iterative process to optimize one or more parameters of the baseline study protocol to minimize at least one of the one or more risk factors and generate a modified study protocol that will provide images of sufficient diagnostic quality.

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