725 results about "Therapy planning" patented technology

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

The invention discloses a multi-angle particle therapy system without a rotating rack and a using method thereof, and relates to the technical field of particle therapy equipment, and the treatment system comprises an accelerator, an irradiation system, a control system, a patient supporting system and an imaging system. The patient supporting system comprises a containing and fixing device, a two-dimensional translation system, a rotating system and a multi-degree-of-freedom mechanical positioning system, the containing and fixing device is connected with the two-dimensional translation system, the two-dimensional translation system is connected with the rotating system, and the two-dimensional translation system controls the relative position of a patient and a rotating center; the rotating system rotates a patient to an appointed angle of a treatment plan or cooperates with the imaging system to obtain a three-dimensional image of the patient, and the irradiation system irradiates a tumor target area; the use method comprises the following steps: generating a treatment plan by reconstructing a three-dimensional CT image, and completing irradiation at different angles according to the treatment plan. According to the treatment system, multi-angle irradiation on a patient can be realized under the condition that a rotating rack is not used, and the manufacturing period and cost of the particle treatment system can be reduced.

A method is provided for optimizing a dental treatment plan. In some cases, the method can include receiving patient data comprising one or more progress indicators associated with an dental treatment plan, processing the patient data to determine a level of progression associated with the dental treatment plan based on the one or more progress indicators, modifying the dental treatment plan in response to the determined level of progression, and generating a notification of the modified dental treatment plan. In some cases, modifying the dental treatment plan in response to the determined level of progression includes advancing a patient associated with the patient data to a subsequent stage of the dental treatment plan or retaining the patient within a current stage of the dental treatment plan.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

System and methods are described for developing a delivery plan for a pulsed field ablation device for treating an arrhythmia. A method runs a lesion development simulation based on cardiac characteristics and a treatment plan that specifies a delivery plan and a target location to generate a simulated lesion having lesion characteristics. The method initializes a three-dimensional (3D) mesh representing a heart based on cardiac characteristics. The vertices of the 3D mesh are associated with cardiac tissue characteristics with some of the associated with cardiac tissue characteristics representing an arrhythmia source. The method adjusts the cardiac tissue characteristics of vertices of the 3D mesh to reflect the effect of an ablation resulting in formation of the simulated lesion. The method runs a lesion evaluation simulation based on the 3D mesh with the adjusted cardiac tissue characteristics to determine whether the simulated lesion would be effective at treating the arrhythmia.

Disclosed herein are systems, methods, and devices for an image-based, computer vision approach for anthropometric measurement of a user using an automatically generated three-dimensional model of the user. Also disclosed herein are systems, methods, and devices associated with a telehealth proctoring platform that can be used to remotely proctor, monitor, and manage patients over the course of a medical treatment plan. The telehealth proctoring platform may be used to collect and retrieve various kinds of data associated with a patient, such as at-home diagnostic test data, the anthropometric measurements of the patient, or the generated three-dimensional models in order to remotely monitor and track changes to the body of a patient over time and make dynamic adjustments to the patient's medical treatment plan.

A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.

The invention discloses an ultra-high dose rate radiotherapy plan optimization system based on compensator modulation, and relates to the technical field of radiotherapy. A data acquisition module is used for acquiring medical image data of a patient and receiving a clinical target; the clinical target comprises a target region prescription dose, an organ-endangering limit and a minimum dose rate threshold value required for triggering and maintaining a FLASH effect; and the FLASH biological effect evaluation module is used for calculating FLASH biological effective dose distribution corresponding to the physical dose distribution through an embedded biological effect model based on the input physical dose distribution and dose rate distribution. According to the method, the physical dose is converted into the accurate FLASH biological effective dose through the improved linear quadratic model embedded with the FLASH correction factor, the target tumor killing effect is guaranteed, normal tissue protection is enhanced with the help of the tissue differentiation correction factor, the industrial pain point that the physical dose reaches the standard but the biological effect does not reach the expectation is effectively solved, and the application prospect is wide. And the treatment plan better meets the clinical curative effect and safety core requirements.

The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

Described herein are methods and systems of performing immunotherapy on a subject and / or determining if a subject will be responsive to ablation immunotherapy.

The invention discloses a multi-modal image registration processing method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: calculating a mutual information loss value after registration through a mutual information loss method, optimizing a CNN model and UNet model parameters in combination with the total loss generated by the fusion of global and local deformation fields, and obtaining a registered sCT image. A CNN model and a UNet model are combined to generate global and local deformation fields, overall rigid transformation and local nonlinear deformation are effectively captured, the spatial alignment precision of sCT and reference CT is improved, model parameters are optimized through mutual information loss and total loss, the intensity distribution consistency is ensured, feature weights and treatment plan parameters are automatically adjusted through registration quality feedback, and the accuracy of the treatment plan is improved. The coverage precision and efficiency of the radiotherapy plan are remarkably improved, automatic and high-precision image registration and treatment plan optimization are realized, and the reliability and practicability of clinical image processing are enhanced.

An aspect includes examining a dental defect in an oral cavity, performing a minimally invasive procedure to detect or remove a portion of the dental defect, scanning the oral cavity including a residual tooth substance remaining after the performing, to obtain an intraoral scan, computing one or more parameters of the dental defect or residual tooth substance, and generating a number of treatment plans for one or more restoration materials based on a triangulation of the dental defect and an enforcement of constraints of the one or more restoration materials. Each treatment plan includes a geometry of a restoration option, and an optimal restoration option is selected from the plurality of treatment plans.

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

In one embodiment, a computer-implemented system includes an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan. The treatment plan includes a high-intensity interval training (HIIT) session. A processing device is configured to initiate, using the electromechanical machine, the HIIT session, receive, via one or more sensors, one or more measurements pertaining to the electromechanical machine, determine whether the one or more measurements exceed one or more of one or more corresponding thresholds, and in response to determining that the one or more measurements exceed one or more of the one or more corresponding thresholds, modify the treatment plan to cause operation of the electromechanical machine to be modified.

Devices, systems, and methods for predicting palatal geometry are provided. In some embodiments, a system for predicting an anatomy of a patient's palate includes one or more processors and a memory operably coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations including accessing a first digital representation including an initial geometry of soft tissue corresponding to a patient's palate, determining an initial geometry of hard tissue of the patient's palate, predicting a change in the hard tissue during a treatment, using the predicted change in the hard tissue to predict a change in the soft tissue during the treatment, and outputting a second digital representation including a predicted geometry of the patient's palate at a future treatment stage of a treatment plan, based on the predicted change in the soft tissue.

Embodiments described herein provide for implementing a language model in a use-case for Tumor Board meetings for radiotherapy treatment planning (RTTP) efforts and treatment planning assistance, which requires high-levels of accuracy and / or precision in the outputs generated by the LLM and presented to members of the Tumor Board participating in a Tumor Board discussion, which may include live meetings or asynchronous online discussions. Tumor Board Application (TBA) software collects from discussions of a Tumor Board meeting to train the LLM on predicting outputs that contribute information about the patient, proposed RTTP, or aspects of the patient treatment. An AI agent participates in the Tumor Board discussion to ingest the inputs of the members of the Tumor Board and output the responsive text produced by the LLM, thereby allowing the LLM-powered AI-agent to interact with Tumor Board discussions.

A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a sexual performance health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the sexual performance health issue, enable the user to perform an exercise at different exertion levels. While the user uses a treatment apparatus to perform the first treatment plan for the user, the computing device receives cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user, and transmits the cardiovascular data. A machine learning model is used to generate a second treatment plan that modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. The method includes receiving the second treatment plan.

An automated clinical decision support method for treatment re-planning that enhances a CBCT scan using information from an initial CT scan of the patient to create a synthetic CT scan; adapts contours of organ(s) and tumour(s) from the initial CT scan to align with the synthetic CT scan; and calculates a delivered radiation dose from the synthetic CT scan; compares these against an original treatment plan to detect deviations exceeding preset thresholds, and triggers alerts with assigned priority levels for a re-plan decision.

The invention discloses a radiotherapy plan generation method and system based on a large model. The method comprises the steps that medical images such as CT, MRI and PET-CT are acquired; constructing a plan generation model which comprises a feature extraction module, a sparse attention module and a large language model; the feature extraction module is used for extracting morphological features of tumors and organs at risk from the medical image to obtain image features; the sparse attention module is used for dynamically fusing the image features and the text data and generating interactive feature vectors; the large language model is used for generating radiotherapy plan parameters according to the fused feature vectors; and inputting the actual medical image into the plan generation model, and outputting radiotherapy plan parameters. The system comprises an image acquisition unit, a model construction unit and an application unit. According to the invention, the consistency and the accuracy of automatically generating the radiation treatment plan are improved. The method can be widely applied to the field of treatment plan design.

The invention relates to the field of radiotherapy plan design, and discloses a radiotherapy parameter intelligent recommendation system based on disease species, target area volume and dose characteristics, and the system comprises the steps: constructing a response surface model of machine physical parameters and plan quality indexes, generating a Pareto optimal solution set through a non-dominated sorting genetic algorithm, and building an optimization rule base; extracting anatomical and geometric features of historical cases, and training a CART decision tree to establish a mapping relation between the features and clinical scenes; analyzing to-be-planned case features, matching a target scene through the decision tree, and retrieving an optimal machine physical parameter combination in the rule base according to the user preference weight. According to the method, the width, the screw pitch and the modulation factor of the collimator can be automatically recommended, the problem that manual trial and error efficiency is low and depends on experience is solved, and balance between treatment plan quality and machine execution efficiency is achieved.

A medical imaging system including an optical identifier camera for imaging a medical device, a medical device packaging, a patient, or a technician. The system further includes an imaging probe for imaging of a target location, and a console with optical identifier logic to extract identifier markers or characteristics from the image of the medical device, packaging, patient or technician. A parameter logic determines the medical imaging parameters based on the identifiers or characteristics, and a medical imaging logic captures medical images of the medical device. The system enables efficient and accurate imaging of medical devices on the specific patient, by a specific technician, enhancing diagnostic capabilities and treatment planning in medical parameters.

A small animal imaging and radiotherapy (RT) irradiator device uses a circular array x-ray source to scan an anatomy of an animal, delivers conventional and ultra-high dose rate of radiation to the treatment target. The multipixel x-ray source includes a ring of focal spots capable of irradiating x-ray beams from around fifty beam angles. The x-ray beams are collimated with a collimator designed in treatment planning.

The invention relates to the field of medical image processing, in particular to a medical image segmentation method based on improved U-Net. According to the method, an improved U-Net network structure and an innovative training strategy are introduced, and an sE attention module and multi-scale expansion convolution are introduced into each layer of an encoder, so that semantic information of a multi-scale defect target is effectively captured. An additive attention mechanism is fused in each layer of the encoder to enhance the global spatial perception capability of decoder features while optimizing the expression of detail information. According to the method, an accurate coronary artery segmentation result can be provided for a doctor, and disease diagnosis and formulation of a treatment plan are facilitated.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.

The invention provides a BNCT treatment plan optimization method. The method comprises the steps of obtaining a candidate set of BNCT treatment plans; determining a plurality of quality indexes of the BNCT treatment plan and corresponding index standard values, and determining an index scoring function based on the quality indexes and the corresponding index standard values; obtaining index weights of the plurality of quality indexes, and obtaining a plan scoring function based on the index weights and the index scoring function; and obtaining a score regression model based on the plan scoring function, and iteratively calculating a score maximum value in the candidate set based on the score regression model and the plan scoring function to obtain a corresponding optimal BNCT treatment plan. According to the method and the device, the BNCT treatment plan with the highest score can be selected with fewer computing resources by setting the plan scoring function, so that accurate and efficient BNCT treatment is realized.

The invention relates to the technical field of tumor image analysis, in particular to a tumor CT image segmentation processing method and system, and the method comprises the following steps: adjusting image processing parameters based on a patient tumor CT image, carrying out the enhancement processing of the image, extracting various image feature information of a medical image, and generating an image enhancement processing result. According to the method, the image is enhanced by adjusting the contrast and brightness of the image, so that the visual quality of the image is improved, a clearer basis is provided for subsequent edge detection, the accuracy of edge detection is improved, the tumor region is segmented in combination with texture and color information, and the geometric features of the tumor are extracted; according to the method, the understanding of the tumor morphology is enhanced, the construction of a three-dimensional model and the calculation of the tumor volume are realized, the method is crucial to the stage evaluation of diseases and the formulation of treatment plans, and the data retrieval efficiency is improved by integrating tumor volume estimation data and key patient information and optimizing the classification and storage process of images.

Systems and methods for evaluating and presenting robustness of a radiotherapy treatment plan for use in radiotherapy are discussed. An exemplary system includes a processor to generate, in a radiation simulation in accordance with the treatment plan under evaluation, dose distributions at an anatomical structure under a nominal condition and one or more artificially imposed uncertainty conditions, determine a dose distribution characteristic for the anatomical structure using the received dose distributions, and generate a robustness indicator of the treatment plan. The dose distributions may be determined at a target structure and one or more structures at risk, and presented graphically in a three-dimensional dose-volume-structure space. An output circuit can output the dose distribution characteristic or the robustness indicator to a user or a treatment planning system.