Methods and apparatus for radioablation therapy

Abstract

Systems and methods for radioablation treatment planning are disclosed. In some examples, a computing device provides a user interface for display that allows a medical professional to define a target region of a patient for treatment. The user interface can allow the medical professional to select a treatment region using an interactive target map generated for the patient. The computing device also receives image data of the patient from an imaging system, such as image data that identifies a 3D volume of a scan structure of the patient. The computing device can generate a 3D image of the scan structure for display based on the received image data, and can superimpose a target region map on the 3D image that the medical professional can manipulate to define a target region of the patient for treatment. Once defined, the computing device can transmit the defined target region to a treatment system for treatment of the patient.

Description

Technical Field

[0001] The aspects of this disclosure generally relate to medical diagnostic and treatment systems, and more specifically, to systems for providing radioablation diagnostic, treatment planning, and delivery for diagnosing and treating conditions such as arrhythmias. Background Technology

[0002] Various techniques can be used to capture or image a patient's metabolic, electrical, and anatomical information. For example, positron emission tomography (PET) is a metabolic imaging technique that produces tomographic images representing the distribution of positron-emitting isotopes within the body. Computed tomography (CT) and magnetic resonance imaging (MRI) are anatomical imaging techniques that use X-rays and magnetic fields, respectively, to create images. Images from these exemplary techniques can be combined to generate composite anatomical and functional images. For instance, software systems such as Velocity™ software from Varian Medical Systems use image fusion processes to deform and / or register images, combining different types of images to produce composite images.

[0003] In cardiac radioablation, medical professionals work together to diagnose arrhythmias, identify ablation areas, specify radiation therapy, and create a radioablation treatment plan. Typically, each of these different medical professionals has complementary medical training and therefore specializes in different aspects of treatment progression. For example, an electrophysiologist may identify one or more areas or targets in a patient's heart based on the patient's anatomy and electrophysiology for the treatment of arrhythmias. For instance, an electrophysiologist may use combined PET and cardiac CT images as input to manually define the target area for ablation. Once the electrophysiologist has defined the target area, the radiation oncologist can specify the radiation therapy, including, for example, the number of radiation fractions to be delivered, the radiation dose to be delivered to the target area, and the maximum dose to adjacent organs of risk. Once the radiation dose is specified, a dosimeter can typically create a radioablation treatment plan based on the specified radiation therapy. The radiation oncologist then usually reviews and approves the treatment plan to be delivered. Furthermore, before finalizing the radioablation treatment plan, electrophysiologists may want to know the location, size, and shape of the dose region of the defined target volume to confirm that the target location defined by the radioablation treatment plan for the patient is correct.

[0004] Properly identifying and defining the target area for the patient's organ to be treated is crucial for developing and optimizing treatment plans. For example, an over-encompassing target area may result in a defined target volume that includes areas that do not need treatment, while an under-encompassing target area may result in a defined target volume that does not include areas that should be treated. Therefore, there is an opportunity to improve radioablation treatment planning systems used by medical professionals, such as cardiac radioablation systems for diagnostic and radiotherapy planning in cardiac radioablation. Summary of the Invention

[0005] Systems and methods for the diagnosis, treatment, and planning of cardiac radioablation are disclosed. In some examples, a computing device provides a user interface for displaying a target region for a patient to be treated, allowing a medical professional to define the target region. This user interface allows the medical professional to select the treatment region using an interactive target map generated for the patient. The computing device also receives image data of the patient from an imaging system, such as image data identifying 3D volumes of the patient's scanned structures. The computing device can generate a 3D image of the scanned structure based on the received image data for display, and can overlay a target region map on the 3D image. The medical professional can manipulate the target region map to define the target region for treatment of the patient. Once defined, the computing device can transmit the defined target region to the treatment system for treating the patient.

[0006] In some examples, a system includes a computing device configured to receive a first input identifying a therapeutic target region of a patient's organ and to receive a scanned image of that organ. The computing device is also configured to generate a first digital model of the organ type. Furthermore, the computing device is configured to determine the alignment of the scanned image with the first digital model. The computing device is also configured to generate a second digital model comprising at least a portion of the scanned image and the first digital model. The computing device is further configured to store the second digital model in a data repository.

[0007] In some examples, a computer-implemented method includes receiving a first input identifying a therapeutic target region of a patient's organ, and receiving a scanned image of that organ. The method also includes generating a first digital model of the organ type. Furthermore, the method includes determining the alignment of the scanned image with the first digital model. The method also includes generating a second digital model, which includes at least a portion of the scanned image and the first digital model. The method further includes storing the second digital model in a data repository.

[0008] In some examples, a non-transitory computer-readable medium storage instruction, when executed by at least one processor, causes the at least one processor to perform operations including receiving a first input identifying a therapeutic target region of a patient's organ and receiving a scanned image of the organ. The operation also includes generating a first digital model of the type of the organ. Furthermore, the operation includes determining an alignment between the scanned image and the first digital model. The operation also includes generating a second digital model comprising at least a portion of the scanned image and the first digital model. The operation further includes storing the second digital model in a data repository.

[0009] In some examples, a method includes means for receiving a first input identifying a therapeutic target region of a patient's organ and for receiving a scanned image of that organ. The method also includes means for generating a first digital model of the type of the organ. Furthermore, the method includes means for determining the alignment of the scanned image with the first digital model. The method also includes means for generating a second digital model, which includes at least a portion of the scanned image and the first digital model. The method also includes means for storing the second digital model in a data repository. Attached Figure Description

[0010] The features and advantages of this disclosure will become more fully disclosed in the following detailed description of exemplary embodiments, or will become apparent from the following detailed description of exemplary embodiments. The detailed description of the exemplary embodiments will be consistent with the appended... Figure 1 For consideration, the same reference numerals refer to the same parts, and further wherein:

[0011] Figure 1 The illustration shows a cardiac radioablation diagnostic and therapeutic system according to some embodiments;

[0012] Figure 2 The diagram illustrates a target-defined computing device according to some embodiments;

[0013] Figure 3 The illustrations depict some embodiments. Figure 1 An exemplary part of a cardiac radioablation therapy system;

[0014] Figure 4A , 4B Figures 4C, 4D, 4E, and 4F illustrate portions of a graphical user interface according to some embodiments;

[0015] Figure 5A and 5B The illustration shows a portion of a graphical user interface according to some embodiments;

[0016] Figure 6A , 6BFigures 6C, 6D, and 6E illustrate portions of a graphical user interface according to some embodiments;

[0017] Figure 7A The illustration shows a two-dimensional segment model according to some embodiments;

[0018] Figure 7B The illustration shows a three-dimensional segment model according to some embodiments;

[0019] Figure 7C The illustration shows a three-dimensional segment model with spaced boundaries according to some embodiments;

[0020] Figure 8 The illustrations depict some embodiments. Figure 7A Editing options for the two-dimensional segment model;

[0021] Figure 9 The illustrations depict some embodiments. Figure 7B Editing options for the 3D segment model;

[0022] Figure 10A The illustration shows the selection of a segment within a segment model according to some embodiments;

[0023] Figure 10B The illustration shows a three-dimensional segment model of a segment selected by an identifier according to some embodiments;

[0024] Figure 11 This is a flowchart illustrating an example method for generating patient-specific research based on some implementation examples;

[0025] Figure 12 This is a flowchart of an example method for generating an interactive graph for identifying therapeutic target regions, based on some embodiments;

[0026] Figure 13A This is a flowchart illustrating an example method for generating a digital model based on some implementation embodiments; and

[0027] Figure 13B Adjusted according to some embodiments Figure 13A A flowchart illustrating an example method for orienting digital models. Detailed Implementation

[0028] The description of preferred embodiments is intended to be read in conjunction with the accompanying drawings, which should be considered part of the overall written description of this disclosure. Where this disclosure is readily susceptible to various modifications and alternatives, specific embodiments are illustrated by way of example in the drawings, and will be described in detail herein. The aims and advantages of the claimed subject matter will become more apparent from the following detailed description of these exemplary embodiments in conjunction with the accompanying drawings.

[0029] However, it should be understood that this disclosure is not intended to be limited to the specific forms disclosed. Rather, this disclosure covers all modifications, equivalents, and substitutions falling within the spirit and scope of these exemplary embodiments. The terms “coupled,” “coupled,” “operably coupled,” “operably connected,” etc., should be understood broadly as referring to mechanically, electrically, wiredly, wirelessly, or otherwise connecting devices or components together such that the connection allows the related devices or components to operate on each other (e.g., communicate) by means of the relationship as intended.

[0030] Switch to the attached image. Figure 1 The diagram illustrates a block diagram of a cardiac radioablation diagnostic and therapeutic system 100, which includes, communicatively coupled via a communication network 118, an imaging device 102, a treatment planning calculation device 106, one or more target-defined calculation devices 104, and a database 116. For example, the imaging device 102 may be a CT scanner, MR scanner, PET scanner, electrophysiological imaging device, ECG, or ECG imager. In some examples, the imaging device 102 may be a PET / CT scanner or a PET / MR scanner. In some embodiments, the imaging device 102 and the treatment planning calculation device 106 may be part of a radioablation therapy system 126 that allows for radioablation therapy of a patient. For example, the radioablation therapy system 126 may allow for the delivery of a defined dose to one or more treatment areas of a patient.

[0031] Each target-defined computing device 104 and treatment planning computing device 106 can be any suitable computing device, including any suitable hardware or software and hardware combination for processing data. For example, each computing device may include one or more processors, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more state machines, digital circuits, or any other suitable circuitry. Furthermore, each computing device can transmit data to and receive data from the communication network 118. For example, each of the target-defined computing device 104 and treatment planning computing device 106 can be a server (such as a cloud-based server), a computer, a laptop, a mobile device, a workstation, or any other suitable computing device.

[0032] For example, Figure 2The illustration shows a computing device 200, which may be an example of each of a target-defined computing device 104 and a treatment planning computing device 106. The computing device 200 includes one or more processors 201, working memory 202, one or more input / output devices 203, instruction memory 207, transceiver 204, one or more communication ports 207, and a display 206, all operatively coupled to one or more data buses 208. The data bus 208 allows communication between various devices. The data bus 208 may include wired or wireless communication channels.

[0033] Processor 201 may include one or more different processors, each having one or more cores. Each different processor may have the same or different architecture. Processor 201 may include one or more central processing units (CPUs), one or more graphics processing units (GPUs), application-specific integrated circuits (ASICs), digital signal processors (DSPs), etc.

[0034] Instruction memory 207 may store instructions that can be accessed (e.g., read) and executed by processor 201. For example, instruction memory 207 may be a non-transitory, computer-readable storage medium, such as read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, removable disk, CD-ROM, any non-volatile memory, or any other suitable memory. Processor 201 may be configured to perform a function or operation by executing code stored in instruction memory 207 that embodies a particular function or operation. For example, processor 201 may be configured to execute code stored in instruction memory 207 to perform one or more of the functions, methods, or operations disclosed herein.

[0035] Additionally, processor 201 can store data in and read data from working memory 202. For example, processor 201 can store a working set of instructions, such as instructions loaded from instruction memory 207, in working memory 202. Processor 201 can also use working memory 202 to store dynamic data created during operation of the radioablation diagnostic and treatment planning calculation device 200. Working memory 202 can be random access memory (RAM), such as static random access memory (SRAM) or dynamic random access memory (DRAM), or any other suitable memory.

[0036] Input / output device 203 may include any suitable device that allows data input or output. For example, input / output device 203 may include one or more of a keyboard, touchpad, mouse, stylus, touchscreen, physical button, speaker, microphone, or any other suitable input or output device.

[0037] For example, communication port 209 may include a serial port, such as a Universal Asynchronous Receiver / Transmitter (UART) connection, a Universal Serial Bus (USB) connection, or any other suitable communication port or connection. In some examples, communication port 209 allows programming of executable instructions in instruction memory 207. In some examples, communication port 209 allows the transfer (e.g., uploading or downloading) of data such as image data.

[0038] Display 206 can be any suitable display, such as a 3D viewer or monitor. Display 206 can display user interface 205. User interface 205 enables a user to interact with computing device 200. For example, user interface 205 can be the user interface of an application that allows a user (e.g., a medical professional) to view or manipulate a model to define a target area for patient treatment as described herein. In some examples, the user can interact with user interface 205 by occupying input / output device 203. In some examples, display 206 can be a touchscreen, on which user interface 205 is displayed. In some examples, display 206 displays images (e.g., image slices) of scanned image data.

[0039] Transceiver 204 allows communication with networks (such as...) Figure 1 Communicate with the communication network 118). For example, if Figure 1 If the communication network 118 is a cellular network, then the transceiver 204 is configured to allow communication with that cellular network. In some examples, the transceiver 204 is selected based on the type of communication network 118 in which the radioablation diagnostic and treatment planning computing device 200 will operate. The processor 201 is operable to communicate via the transceiver 204 from, for example, Figure 1 The communication network 118 receives data or sends data to the network.

[0040] Go back for reference Figure 1 Database 116 may be a remote storage device (e.g., including non-volatile memory), such as a cloud-based server, a disk (e.g., a hard disk), a storage device on another application server, a networked computer, or any other suitable remote storage device. In some examples, database 116 may be a local storage device of one or more of the target-defined computing device 104 and the treatment planning computing device 106, such as a hard disk drive, non-volatile memory, or a USB stick.

[0041] Communication network 118 can be WiFi ® Networks, cellular networks (such as 3GPP) ® Network), Bluetooth ®Networks, satellite networks, wireless local area networks (LANs), networks utilizing radio frequency (RF) communication protocols, near field communication (NFC) networks, wireless metropolitan area networks (MANs) connecting multiple wireless LANs, wide area networks (WANs), or any other suitable network. Communication network 118 can provide access to, for example, the Internet.

[0042] Imaging device 102 is operable to scan images (such as images of patient organs) and provide image data 103 (e.g., measurement data) identifying and characterizing the scanned images to communication network 118. Alternatively, imaging device 102 is operable to acquire electrical imaging such as cardiac ECG images. For example, imaging device 102 can scan patient structures (e.g., organs) and can transmit image data 103 of one or more slices identifying the 3D volume of the scanned structures to one or more of target-defined computing device 104 and treatment planning computing device 106 via communication network 118. In some examples, imaging device 102 stores image data 103 in database 116, and one or more of target-defined computing device 104 and treatment planning computing device 106 can retrieve image data 103 from database 116.

[0043] In some examples, target-defined computing device 104 is operable to communicate with treatment planning computing device 106 via communication network 118. In some examples, target-defined computing device 104 and treatment planning computing device 106 communicate with each other via database 116 (e.g., by storing and retrieving data from database 116). In some examples, one or more target-defined computing devices 104 and one or more treatment planning computing devices 106 are part of a cloud-based network that allows for resource and communication sharing with each device.

[0044] In some examples, an electrophysiologist (EP) operates the target delimitation computing device 104 to define the target region for patient treatment as described herein. In some examples, the target delimitation computing device 104 generates target data identifying the target region for the patient and transmits the target data to the treatment planning computing device 106. A radiation oncologist can operate the treatment planning computing device 106 to deliver treatment to the patient via the imaging device 102. In some examples, the target region is integrated into a radiation ablation treatment plan for treating the patient.

[0045] In some embodiments, one or more target-defined computing devices 104 are located in a first region 122 of the medical facility 120, and one or more target-defined computing devices 104 are located in a second region 124 of the medical facility 120. Therefore, the cardiac radioablation diagnostic and therapeutic system 100 allows multiple EPs to collaboratively define target regions. For example, one EP can operate the first target-defined computing device 104 in the first medical facility 122, and a second EP can operate the second target-defined computing device 102 in the second medical facility 124. The first and second target-defined computing devices 104 can communicate via a communication network 118, such as by transmitting and receiving data related to (e.g., defining target regions) target regions (e.g., proposed target regions). Each EP can operate the corresponding target-defined computing device 104 to adjust the target region, and once the two EPs agree on the target region, the target region can be defined.

[0046] Research generation

[0047] The target-limiting computing device 102 can execute an application that generates a user interface (e.g., user interface 205) that can be displayed to a medical professional, such as an EP. The executed application allows the medical professional to limit the target area for treatment of the patient. For example, the user interface allows the medical professional to select a study type (e.g., CT, ECG, MRI, etc.). The study type can identify the type of imaging used for the patient. For example, the study type can identify the type of images captured for the patient.

[0048] In response to a selection of a study type (e.g., via a drop-down menu), the executed application automatically provides a selection of study categories for the selected study type via the user interface. Study categories can identify a list of features (or study focus) specific to a particular study type. For example, and assuming a medical professional selects the study type "ECG," the user interface can provide a selection of one or more study categories, such as "Electro". As another example, the study category "Structure" can be provided for study types such as "CT", "MR", "PET / SPECT", and "US". Additional study categories can include "Metabolic" or any other appropriate study category. In some examples, only one study category is available for a study type (e.g., "Electro" for the "ECG" study type), and therefore, the executed application can automatically select that unique study category for the selected study type.

[0049] Once a study category is selected, the executed application allows selection of study locations via a user interface. This study location can identify a general target area of ​​the patient's organ to be treated, such as one or more segments of the heart. The study locations displayed for selection may depend on the selected study category and / or study type. For example, assuming the study type is "ECG" and the study category is "Electrical," the executed application may provide selections of one or more study locations via the user interface, including "VT exit site," "VT entry site," and "VT entry and exit sites," etc. As another example, assuming the study type is "CT" and the study category is "Structure," the executed application may provide selections of one or more study locations via the user interface, including "Scar."

[0050] In some examples, once a medical professional has selected the research type, research category, and research location, the executed application can provide an interactive model of the organ or its parts for display, such as a 17-segment model representing the basal level, mid-cavity level, and apex of the heart's ventricles. This interactive model allows the medical professional to select one or more parts of the organ to be treated. For example, and assuming the interactive model is a 17-segment model of the heart's ventricles, the interactive model could allow the medical professional to select one or more of the 17 segments (e.g., segments 1 through 17). The medical professional can select each segment by, for example, clicking on each segment (e.g., using input / output device 203). When each segment is selected, in some examples, the executed application can change the color of each segment or provide some other indication that the segment has been selected. In some examples, the color of each selected segment depends on the selected research category. For example, the executed application could display the selected segment for the "Structure" research category in gray and the selected segment for the "Electrical" research category in orange.

[0051] In some examples, the executed application can display the name of each part of an organ. For instance, the executed application can display the name of a segment in a 17-segment model when a medical professional drags the cursor over it.

[0052] In some examples, the user interface allows healthcare professionals to store records in a database such as database 116, where the record identifies the selected study type, study category, study location, and any selected portion (e.g., segment) of the interactive model. In some examples, the executed application allows healthcare professionals to name the record, select the study date, and further provide annotations associated with the record, all of which can be stored as part of the record in the database.

[0053] Target selection

[0054] The executed application can further allow medical professionals to identify target areas for treatment. For example, the executed application can display one or more study category maps, where each study category map (e.g., a “heatmap”) corresponds to a study category. Each study category map can identify one or more parts of a patient’s organ, such as a 17-segment model of the ventricles of the heart. Furthermore, each study category map provides indications of features (e.g., study locations) previously identified for the patient and corresponding to the study category. For example, an “electrograph” can provide indications of one or more arrhythmia origins identified in an “electrical” type study performed on the patient, while a “structural map” can provide indications of one or more scar locations identified in a “structural” type study performed on the patient. For example, data identifying previous studies of the patient can be stored in database 116. Target-defined computing device 104 can obtain this data to generate study category maps.

[0055] In some examples, each study category graph indicates the number of corresponding selections for each of one or more parts of a patient's organ. For example, and assuming a 17-segment model, the executed application can display each segment in a specific color based on the number of times a segment has been selected as a clinical interest in that study category. For example, and for "electrograph," segments that have never been selected (e.g., during previous studies) can be displayed in white, segments that have been selected up to a threshold amount (e.g., once) can be displayed in light orange, and segments that have been selected more than the threshold amount can be displayed in dark orange.

[0056] Each research category graph can display segments in different colors (e.g., different shades of color) based on the corresponding range of selections. For example, and for the "Structure Graph," segments that have never been selected can be displayed in white, segments that have been selected up to a threshold amount can be displayed in light gray, and segments that have been selected beyond the threshold amount can be displayed in dark gray. In some examples, the executed application also provides bar charts indicating the range and corresponding color for each research category graph.

[0057] In some examples, the executed application can display each segment of the study category map in a specific color based on the percentage of times a segment is selected as a clinical interest. The target-limited computing device 104 can obtain patient data from database 116 and, for each study category (e.g., electrical, structural, etc.), can determine the number of times each segment is selected across all study types. Based on the number of selections for each segment, the target-limited computing device 104 can determine the total number of selections for each study category. Furthermore, for each segment, based on the number of studies in that particular study category and the total number of selections for that segment, the target-limited computing device 104 can determine the percentage of times the segment is selected for a study category (e.g., (number of segment selections / total number of studies)). 100).

[0058] For example, for "Electricity Graph," segments with no previous selections can be displayed in white, segments with a selection percentage of "Electricity" research category up to a threshold amount can be displayed in light orange, and segments with a selection percentage exceeding the threshold amount for the "Electricity" research category can be displayed in dark orange. Similarly, for "Structure Graph," segments with no previous selections can be displayed in white, segments with a selection percentage of "Structure" research category up to a threshold amount can be displayed in light gray, and segments with a selection percentage exceeding the threshold amount for the "Structure" research category can be displayed in dark gray. In some examples, the executed application also provides bar charts indicating the percentage and corresponding color for each research category graph.

[0059] The threshold values ​​described herein can be configurable. For example, a medical professional can provide the threshold value to the target-limited computing device 104 via a user interface provided by an executed application, and the target-limited computing device 104 can store the threshold in a database 116.

[0060] In some examples, the target-defined computing device 104 generates a probability map, which may have the same form as a study category map. For example, if the study category map is a 17-segment model, the probability map may also be a 17-segment model. The probability map may indicate the probability of treatment for those parts of an organ based on one or more parts of an organ identified by one or more study category maps. In one example, the target-defined computing device 104 determines the number of choices offered to each part (e.g., segment) of an organ, regardless of the study category (e.g., the total number of choices offered to segments across all study categories). For example, the probability map may combine two or more study category maps and provide an indication of how many times one or more parts of an organ are selected (e.g., as indicated by an individual study category map). Based on the number of choices determined for each part, the executed application displays the corresponding part of the probability map with a corresponding color, or uses another appropriate indication, such as a corresponding shaded line.

[0061] In some examples, the target-defined computing device 104 determines the percentage of times each segment is selected across all study categories. Based on the percentage determined for each segment, the executed application displays the corresponding segment of the probability map with the corresponding color, or uses any other appropriate indicator.

[0062] In some examples, the target-limited computing device 104 determines the average number of times each segment is selected across all study categories. For example, the target-limited computing device 104 can determine the average number of times the segment is selected across all study categories and divide by the number of study categories. Based on the determined average for each segment, the executed application displays the corresponding segment of the probability map with the corresponding color, or uses any other appropriate indication.

[0063] In some examples, the target-limited computing device 104 assigns weights (e.g., multipliers) to each study category. For instance, the target-limited computing device 104 can determine the number of selections for a first portion of the probability graph as described above, and can multiply the total number of selections by a first value to determine a first weighting value. Similarly, the target-limited computing device 104 can determine the number of selections for a second portion of the probability graph as described above, and can multiply the total number of selections by a second value to determine a second weighting value. The first value can be less than or greater than the second value. Based on the first and second weighting values, the target-limited computing device 104 can display the corresponding portion of the probability graph in a corresponding color, or use any other appropriate indication.

[0064] In some examples, the target-limited computing device 104 can equally weight each study category graph, regardless of how many times the corresponding part of an organ was selected within the corresponding study category. For example, the target-limited computing device 104 can display a study category graph based on the percentage of each part of an organ selected within that study category, as described above. The target-limited computing device 104 can determine the value of each part based on the percentage of that part in each study category. Based on the value determined for each part, the executed application displays the corresponding part of the probability graph in the corresponding color, or uses any other appropriate indication. In some examples, the target-limited computing device 104 weights (e.g., applies a multiplier) the percentage of each part and determines the value based on the weighted percentage. The multipliers for at least two segments may be different. In some examples, the executed application allows medical professionals to configure the multipliers. The target-limited computing device 104 can store the multipliers in a database 116.

[0065] In some examples, the executed application can generate a target-defined model to allow medical professionals to identify target areas for treatment (e.g., ablation areas), which may be a 17-segment model of the heart's ventricles. In some embodiments, the medical professional can select one or more portions of the target-defined model to identify the target area. For example, and in the example of the 17-segment model, the medical professional can select a segment by clicking on it (e.g., using input / output device 203). In some examples, the executed application changes the color of the selected segment or may otherwise indicate the selected segment to the medical professional.

[0066] Furthermore, in some examples, the target-limiting computing device 104 can determine whether a selected segment is "impossible" or unlikely to be selected based on a probability map and / or a corresponding study category map (e.g., values ​​used to generate the study category map). For example, the target-limiting computing device 104 can apply one or more rules (e.g., algorithms) to the values ​​determined to generate the study category map to determine whether a selected portion is impossible. For example, data identifying and characterizing the rules can be stored in a database 116. As an example, one rule can specify that a selected portion (e.g., a segment) corresponding to a percentage below a threshold in the probability map is "impossible." As another example, another rule can specify that a selected portion corresponding to the number of selections indicated in the probability map below a threshold is "impossible." The rules are not limited to these examples, and any suitable rule can be adopted.

[0067] In some examples, one or more trained machine learning models are applied to patient data to determine whether a selected segment is impossible. For instance, a machine learning model (such as a neural network or a decision tree-based machine learning model) can be trained using historical patient data to identify possible treatment areas. A trained machine learning model can be applied to a specific patient's historical data (e.g., treatment data stored in database 116) and the patient's selected segment to classify the selected segment as possible or impossible. The model can be applied to a wide selection of diagnostic data, such as medical imaging and electrodiagnostic studies (e.g., ECG, ECGI, old catheter maps, etc.).

[0068] For any selected segment determined to be "impossible," the executing application (e.g., via a pop-up window) generates a message with a warning indicating the impossibility of the selection. Medical professionals can consider this warning and may reject it after providing input via the user interface.

[0069] Target alignment

[0070] Based on a target-defined model, the target-defined computing device 104 can generate a three-dimensional (3D) model of the corresponding structure (e.g., an organ). For example, assuming the target-defined model is a two-dimensional (2D) 17-segment model of the heart's ventricles, the target-defined computing device 104 can generate a 3D representation of the 17-segment model. This 3D model can identify the base, middle cavity, apex, and apex regions of the heart's ventricles. For example, the 3D representation of the 17-segment model can be based on the shape of the surface mesh of the left ventricular structure.

[0071] For example, Figure 7A The illustration shows a 2D heart model 700, which includes a 2D ventricular model 702 adjacent to the right ventricular model 704. As shown, the 2D ventricular model 702 comprises 17 segments, each labeled with a corresponding number. Legend 706 identifies the ventricular portion associated with each segment.

[0072] Figure 7B The illustration shows a 3D ventricular model 720, which is a 3D representation of a 2D ventricular model 702. The 3D ventricular model 720 identifies the basal region 724, the mid-cavity region 726, the apical region 728, and the apical region 730 of the heart ventricle, each part including a structure along the long axis 722 of the 3D model 720.

[0073] Figure 7C The illustration shows a 3D heart model 750 including a 3D ventricular model 720 adjacent to a right ventricular model 760. The 3D ventricular model 720 includes a basal region 724 extending from the top 754 of the ventricle plane to the top 752 of the basal plane, a ventricle region 726 extending from the top 756 of the apex plane to the top 754 of the ventricle plane, and an apex region 728 extending from the top 730 of the apex to the top 756 of the apex plane. Furthermore, the 3D heart model 750 includes a septal boundary 762 that defines the intersection between the right ventricular model 760 and the 3D ventricular model 720. A top point 764 is illustrated along the septal boundary 762, at which the top 752 of the basal plane contacts the right ventricle 760.

[0074] The target-defined computing device 104 can generate model data that identifies and characterizes one or more of the 2D model 702, the 3D ventricular model 720, and the 3D heart model 750, and store the data in the database 116.

[0075] In some examples, a medical professional can (e.g., via input / output device 203) provide input to the target-defined computing device 104 to adjust any of the 2D model 702, the 3D ventricular model 720, and the 3D heart model 750. An application executing this can receive the input and adjust the corresponding model, as described herein.

[0076] For example, Figure 8 The illustration shows a 2D heart model 700 with drag points 802 and 804. A medical professional can provide input to a target-defined computing device 104 and adjust the position of the anterior interventricular groove 803 by adjusting drag point 802. Similarly, the medical professional can provide input to the target-defined computing device 104 and adjust the position of the inferior interventricular groove 805 by adjusting drag point 804. Drag points 802 and 804 are configured to slide along the outer edge of the 2D model 702.

[0077] Medical professionals can make adjustments to 3D models, such as the 3D ventricle 720. For example, Figure 9 The illustration shows a three-dimensional ventricular model 720 with adjustable drag points 902, 904, 906, 908, and 910. A medical professional can adjust drag point 906 to adjust the position of the anterior interventricular groove 956. Similarly, a medical professional can adjust drag point 908 to adjust the position of the inferior interventricular groove 954. In this way, alignment with a ventricle (such as the right ventricle 760) can be achieved.

[0078] Medical professionals can also adjust the orientation of the 3D ventricular model 720 by adjusting drag point 902. For example, if a medical professional drags drag point 902 to the right, the 3D ventricular model 720 will "tilt" to the right (e.g., tilt by a certain number of degrees). Medical professionals can also adjust the length 980 by adjusting drag point 902 along the long axis 722. For example, a medical professional can cause the 3D ventricular model 720 to lengthen by dragging drag point 902 upwards, and can cause the 3D ventricular model 720 to shorten by dragging drag point 902 downwards. In some examples, adjustments to length 980 cause equal or nearly equal changes to lengths 980A, 980B, and 980C.

[0079] Dragging point 904 can lengthen (e.g., by dragging point 904 upwards) or shorten (e.g., by dragging point 904 downwards). For example, dragging point 904 can cause a change in length 980A. Similarly, dragging point 910 can lengthen or shorten tip region 730, thereby causing a change in length 940.

[0080] Figure 10A and 10B The illustration shows the generation of ablation volume based on the selected target segment. For example, Figure 10A The illustration shows a 2D segment model 1002A, which can be a target-defined model. Figure 10BThe corresponding 3D segment model 1002B is illustrated. The 2D segment model 1002A illustrates the left ventricular chamber 1008, which has a specific wall thickness 1006A (e.g., 10 mm) measured from the inner surface 1010A. This inner surface surrounds a center point 1004A. Figure 10A The illustration also shows the selected segment 1012A that a medical professional may have chosen (e.g., segment 9 of the 17-segment model of the heart ventricle).

[0081] The 3D segment model 1002B includes a left ventricular chamber 1008B, which has a wall thickness 1006B measured from its inner surface 1010B. The inner surface 1010B surrounds a lateral line 1004B. The lateral line 1004B corresponds to a center point 1004A. Figure 10B The ablation volume 1012B is also shown, which corresponds to the selected segment 1012A.

[0082] Therefore, if a medical professional selects segment 1012A, the target-limited computing device 104 can automatically generate the ablation volume 1012B of the 3D segment model 1002B and display the 3D segment model 1002B.

[0083] Go back for reference Figure 1 The target-defined computing device 104 can acquire patient image data 103. Image data 103 includes images of the patient's scanned structures. For example, image data 103 may include the 3D volume of the patient's scanned structures. The scanned structures may correspond to organs or parts thereof identified by a 3D representation model. The target-defined computing device 104 can map the 3D model of the corresponding structure onto the image of the scanned structure. For example, the target-defined computing device 104 can determine the initial alignment of the 3D model with the scanned structure in the image. To determine the initial alignment, the target-defined computing device 104 can execute an alignment algorithm. For example, the initial alignment of a 17-segment model with the left ventricular anatomy is described below.

[0084] First, the intersection of the surfaces of the left and right ventricles is detected by manually expanding the uploaded images, identifying the interventricular septum contour on the left ventricular surface. The long axis is determined based on the geometry of the left ventricle and the orientation of the septal plane. Then, the basal, caval, and apical segment planes are identified based on the following steps. The top of the basal plane is positioned to correspond to the uppermost point of the septal contour, perpendicular to the long axis. The apical segment is placed at the very tip of the ventricle along the long axis with a default thickness (e.g., 10 mm). The apical, caval, and basal planes are evenly distributed along the long axis. Further, segments are located based on the following steps. The position of the septal segment is determined by the anterior and posterior interventricular grooves, which are identified corresponding to the anterior and lowermost points of the interventricular septum contour. Then, the other basal and caval segments are evenly distributed throughout the basal and caval segments, respectively. Four segments at 90 degrees are each distributed in the apical segment. They are positioned so that the apical septal segment is aligned with the center of the basal, mid-lower lateral, and anterolateral segments.

[0085] The target-defined computing device 104 can then overlay the 3D model onto the image according to the determined alignment to generate a 3D structural image. The executed application can provide a 3D structural image for display (i.e., an image of the scanned structure overlaid with the 3D model).

[0086] Once mapped, the executed application allows medical professionals to adjust the alignment and / or orientation of the 3D model relative to the image, as described herein. For example, target-defining computing device 104 can determine along the long axis of the 3D model and can further determine the boundaries of the target region for treatment on the 3D model. The executed application may include one or more “drag points” along the 3D model, whereby a medical professional can (e.g., using input / output device 203) drag each point to a new location to adjust portions of the 3D model relative to structures in the image. The medical professional can also drag the long axis to a new location to change the orientation of the 3D model relative to structures in the image.

[0087] In some examples, the 3D model includes a target region map that a medical professional can manipulate to define the target region (e.g., ablation region) for patient treatment. Initially, the target region map corresponds to an image portion defined by the 3D model, which corresponds to a selected portion (e.g., a segment) of the target-defined model (e.g., the target region map). For example, if a medical professional selects segments 17 and 16 of a 17-segment model for ablation, the target-defined computing device 104 determines the corresponding segments defined by the 3D model. In some examples, the executed application displays the target region map in different colors. Furthermore, portions of the scanned structure within the determined 3D portion of the image can be displayed in a different color (e.g., red). The medical professional can adjust drag points to adjust the target region map. For example, the medical professional can adjust one or more drag points to define the shape of the target region map of the 3D model.

[0088] In some examples, the target-defined computing device 104 determines whether each adjustment by a healthcare professional violates one or more predetermined rules. If an adjustment violates a rule, the executing application can display a pop-up message with a warning. For example, the rule may include determining whether the current alignment has deviated from the initial alignment by more than a threshold amount, such as a percentage deviation beyond the threshold. The healthcare professional can view the warning and act on it, or they can reject the warning. The application of the rules acts as a "reasonableness check" for each adjustment.

[0089] In some examples, the implemented application allows medical professionals to select one or more other organs that can be displayed in conjunction with the 3D structural image. For example, the implemented application may allow medical professionals to select to display the esophagus or lungs adjacent to a 3D structural image of the heart's ventricles. The display of other organs may include the display of 3D models of such organs. In some examples, the display includes scanned images of the patient's corresponding organs. These features can assist medical professionals during alignment and can illustrate how other organs may be affected by the proposed treatment (e.g., as identified by the ablation area).

[0090] In some examples, the executed application allows panning and zooming across 3D structural images. In some examples, the executed application includes pre-configured selections (e.g., presets) for specific views of the 3D structural image. These pre-configured selections can be configured by medical professionals.

[0091] Once the medical professional has completed alignment, they can (e.g., via input / output device 203) provide input to the executed application to save the 3D structural image to a data repository, such as database 116. In some examples, target-defined computing device 104 transmits the 3D structural image to treatment planning computing device 106 to deliver treatment to the patient based on the identified ablation area.

[0092] Figure 3 The diagram shows... Figure 1 This is an exemplary part of a cardiac radioablation diagnostic and treatment system. In this example, the target-limiting computing device 104 includes a study-limiting generation engine 302, a target selection engine 304, and an alignment determination engine 306. In some examples, one or more of the study-limiting generation engine 302, target selection engine 304, and alignment determination engine 306 may be implemented in hardware. In some examples, one or more of the study-limiting generation engine 302, target selection engine 304, and alignment determination engine 306 may be held as tangible, non-transitory memory (such as...). Figure 2 An executable program in instruction memory 207, which can be processed by one or more processors (such as...) Figure 2 The processor 201) executes.

[0093] In this example, each target-defined computing device 104 includes a study-defined generation engine 302, a target selection engine 304, and an alignment determination engine 306, which can receive user input 301. For example, a medical professional can provide user input 301 via input / output device 203 or via a touchscreen of display 206. User input 301 can be received within a graphical user interface (GUI) provided by an executed application. Each of the study-defined generation engine 302, target selection engine 304, and alignment determination engine 306 can receive data (e.g., user input 301) from the GUI and can provide data to the GUI, such as data for display.

[0094] Based on user input 301, the study constraint generation engine 302 can generate study constraint data 303 that identifies study data records. As described herein, study data records can identify study type, study category, study location, and any selected portion (e.g., segment) of an interactive model. As described herein, study data records can also identify the name of the study data record, the date of the study data record, and any annotations provided by medical professionals. The study constraint generation engine 302 provides the study constraint data 303 to the target selection engine 304. In some examples, the study constraint generation engine 302 stores the study constraint data 303 in a database 116.

[0095] Target selection engine 304 can perform operations to identify target regions for treatment. For example, target selection engine 304 can generate one or more study category maps for display, where each study category map (e.g., a “heatmap”) corresponds to a study category. Each study category map can identify one or more parts of a patient’s organ, such as a 17-segment model of the ventricles of the heart. Furthermore, target selection engine 304 can generate probability maps for display, which in some examples may be in the same form as the study category maps. As described herein, the probability map can indicate the probability of treatment for each part of the organ based on those parts identified by the study category maps (e.g., using different colors). For example, target selection engine 304 can obtain patient data 310 for a corresponding patient from database 116. Patient data 310 can identify previous studies the patient has received, and any study data records corresponding to that treatment. As described herein, based on patient data 310, target selection engine 304 can determine how likely the patient is to be treated.

[0096] The target selection engine 304 can also generate a target-defined model for display, such as a 17-segment model of the heart ventricles, to allow medical professionals to identify target regions for treatment (e.g., ablation areas). Medical professionals can provide user input 301 to select one or more portions of the target-defined model to identify the target region. In some examples, the target selection engine 304 determines whether a selection, as described herein, is "impossible," and when a selection is determined to be impossible, provides a warning about the selection (e.g., via a pop-up window). The target selection engine 304 generates selected target data 305 identifying the selected portions of the target-defined model and provides the selected target data 305 to the alignment determination engine 306.

[0097] The alignment determination engine 306 can perform operations to generate and provide a 3D model of an organ or part thereof corresponding to a target-defined model for display. Furthermore, the alignment determination engine 306 can acquire image data 103 of a patient identifying corresponding scanned structures, such as a 3D image of the ventricles of a patient's heart. The alignment determination engine 306 can determine the alignment between the image and the 3D model, and can overlay the 3D model onto the image based on the determined alignment to generate a 3D structural image. The alignment determination engine 306 can then provide the 3D structural image for display, such as for display on a monitor 206.

[0098] Furthermore, the alignment determination engine 306 can receive user input 301 that identifies and characterizes adjustments to the 3D structural image. In response to the user input 301, the alignment determination engine 306 can adjust the 3D structural image accordingly. For example, the alignment determination engine 306 can refine the alignment of the 3D model with the image, or it can adjust drag points to define a target region map that identifies the target area for treatment. The alignment determination engine 306 can generate target definition data 307 that identifies and characterizes the 3D structural image including the target region map, and can store the target definition data 307 in the database 116.

[0099] In some examples, the alignment determination engine 306 determines whether each medical professional's adjustment violates one or more predetermined rules. If the adjustment violates a rule, the alignment determination engine 306 may cause a pop-up message with a warning to be displayed. In some examples, the alignment determination engine 306 receives one or more user inputs 301 that identify the selection of one or more other organs that can be displayed in conjunction with a 3D structural image. In response, the alignment determination engine 306 provides a 3D model of such an organ for display. In some examples, the alignment determination engine 306 provides image data 103 of the patient's corresponding organ for display.

[0100] In some examples, the alignment determination engine 306 receives one or more user inputs 301 that identify a translation or zoom action. In response, the alignment determination engine 306 can translate or zoom across the 3D structural image. In some examples, the alignment determination engine 306 receives one or more user inputs 301 that identify a pre-configured selection for a specific view of the 3D structural image. The alignment determination engine 306 can adjust the 3D structural image according to the selected specific view and can provide an adjusted 3D structural image for display.

[0101] Figure 4AThe illustration shows a first portion 402 of a GUI 400, which allows medical professionals, such as EP, to define a target area for treatment (e.g., ablation). The GUI 400 may be generated by an application executed by a target-defined computing device 104 and may be displayed to a medical professional on a display such as a display 206.

[0102] GUI 400 facilitates a number of steps to define a target area for treatment, including generating research data records, identifying the target area for treatment, and aligning the target area with an image of the patient's organ. These steps are represented by a research icon 406, a target selection icon 408, and an alignment icon 410, each of which is illustrated below the target definition icon 404. Selecting one of the research icon 406, target selection icon 408, or alignment icon 410 displays the corresponding portion of GUI 400 to the user.

[0103] To initiate target definition, the first section 402 includes a study icon 401, which, if selected, allows the generation of a new study data record. Page 402 also includes a report icon 411, which, if selected, generates a report based on the corresponding study data record. This report may include the study data record, any selected target region (e.g., a segment), a scanned image of the patient (e.g., scanned using image scanning device 102), and data identifying and characterizing the alignment of the selected target region with the patient's organ image.

[0104] Figure 4B The illustration shows when medical professionals choose Figure 4A The second part 420 of the GUI 400 can be displayed when the Add Research icon 401 is clicked. For example, the second part 420 may be a pop-up window that appears after a medical professional clicks the Add Research icon 401. The second part 420 includes a research type drop-down menu 424, a research category drop-down menu 428, and a research location drop-down menu 430.

[0105] The study type drop-down menu 424 allows healthcare professionals to select a study type based on the study type record. For example, and as... Figure 4B As shown, the study type drop-down menu 424 allows medical professionals to select from multiple study types (e.g., imaging types), such as CT, catheter mapping, ECG, ECGI, and MRI.

[0106] Once a healthcare professional selects a research type, GUI 400 automatically determines one or more research categories based on the selected research type. Each research category can identify a list of features (or research focus) for a specific research type. Healthcare professionals can view the available research categories using the research category dropdown menu 426. For example, and as... Figure 4D As shown, when the research type is "ECG", medical professionals can select the "Electrical" research category.

[0107] Once a study category is selected, GUI 400 automatically determines one or more study localizations based on the selected study category and / or study type. Study localizations can identify general target regions of the patient's organ to be treated, such as one or more segments of the heart. For example, and as... Figure 4D As shown in the drop-down menu 430, when the selected study type is "ECG" and the selected study category is "Electricity", medical professionals can select the study location of "VT exit site", "VT entry site" and "VT entry and exit site".

[0108] Go back for reference Figure 4B , 4C In addition to 4D, the second part 420 also includes: a study name text box 426 that allows medical professionals to provide the name of the study record; a study date selection box 432 that allows the selection of a date (e.g., the current date); and a note text box 434 that allows medical professionals to enter notes (e.g., treatment notes, reminders, notes for other medical professionals, etc.).

[0109] Furthermore, the second part 420 includes an interactive model 422, which in this example is a 17-segment model representing segments of the heart's ventricles. Medical professionals can select one or more portions of the interactive model 422, which can be areas used for treatment. For example, and as... Figure 4E As shown, medical professionals can select a first segment 423A (e.g., segment 11), a second segment 423B (e.g., segment 16), and a third segment 423C (e.g., segment 15). Additionally, in some examples, when the cursor 489 is placed over a segment (e.g., segment 4), the GUI 400 displays the segment's name (e.g., via a pop-up window). In this example, the cursor 489 appears over segment 4 of the interactive model 422, and in response, the GUI 400 displays a name box 425 that identifies segment 4 as the "subbasal" portion of the heart's ventricles.

[0110] To create a research data record, a medical professional can click the Add icon 490. In response, 104 generates data that identifies and characterizes the information provided to the GUI 400, and stores the generated data in a data repository, such as within a database 116. If the medical professional wishes to restart and not save the research data record, they can click the Cancel icon 492, which results in the clearing of any provided input, and in some examples, such as... Figure 4A The first part 402 is shown.

[0111] refer to Figure 4F The GUI 400 may include a third section 478 displaying a summary of the generated research data record. For example, the GUI 400 may respond to a click by a medical professional. Figure 4E The addition of icon 490 displays section 478. In some examples, GUI 400 responds to clicks by medical professionals. Figure 4A Research icon 406 shows part 478.

[0112] Part 3, 478, includes display areas for the study category 480A, study name 480B, selected segment 480C, acquisition date 480D, and annotations 480E for each generated study data record. Study category 480A corresponds to the selected study category 428 for each generated study data record. Similarly, study name 480B, acquisition date 480D, and annotations 480E correspond to the study name 426, study date 432, and annotations 434 for each study data record.

[0113] In this example, two abstracts are illustrated, including a first research abstract 495A and a second research abstract 495B. The first research abstract 495A includes a “Structure” research category 480A, and the corresponding interactive model 491 for the selected segments 11, 15, and 16 illustrated. The second research abstract 495B includes a “Electrical” research category 480A, and the corresponding interactive model 4912 for the selected segments 10 and 15 illustrated. In some examples, when the cursor 489 is placed over the corresponding portion of the interactive model, the GUI 400 displays the name of that segment (e.g., via a pop-up window). In this example, the cursor 489 appears over segment 10 of the interactive model 492, and in response, the GUI 400 displays a name box 493 that identifies segment 0 as the “lower middle” portion of the heart ventricle.

[0114] Figure 5A The diagram illustrates the target selection section 501 of GUI 400. Once, for example, as described above... Figure 4A- As discussed in 4F, research data records are generated, and GUI 400 can display a target selection section 501 to medical professionals. In some examples, in response to a medical professional clicking... Figure 4A The target selection icon 408 is displayed in GUI 400, and the target selection section 501 is displayed in GUI 501.

[0115] In this example, the target selection section 501 displays a first study category diagram 510 based on the “Electrical” study category 428 and a second study category diagram 520 based on the “Structure” study category 428. As described herein, each study category diagram 510, 520 can identify one or more parts of a patient’s organ, such as a 17-segment model of the ventricles of the heart. Furthermore, each study category diagram 510, 520 provides an indication of previous studies performed on the patient corresponding to the corresponding study category. Additionally, each study category diagram 510, 520 is displayed together with a corresponding bar chart 512, 522. Each bar chart 512, 522 indicates the range of therapeutic doses determined for each study category as described herein, and its corresponding shaded line used within the segment of each corresponding study category diagram 510, 520.

[0116] The target selection section 502 also includes a probability plot 502 that indicates the probability of treating one or more parts of the patient's organ (in this example, the patient's heart) based on which parts of the organ are identified by the study category plots 510, 520. The probability plot 502 is displayed together with a corresponding bar chart 506 that indicates the range of treatment segment probabilities as described herein, and the corresponding shaded lines used within the segments of the probability plot 502.

[0117] Furthermore, the target selection section 502 includes a target definition map 530, which in this example takes the form of a 17-segment model of the heart's ventricles. The target definition map 530 allows a medical professional to identify target regions for treatment. For example, a medical professional can select (e.g., using input / output device 203 to manipulate cursor 489) a segment of the target definition map 530 to identify a target region 532. In this example, target region 532 includes segment 17 of the target definition map 530.

[0118] Figure 5B and Figure 5A Similar to the target region 542, medical professionals can select segment 16 of the target definition map 530 to identify the target region 542. Once the medical professional has identified the target regions 532 and 542 by selecting a portion of the target definition map 530, the medical professional can proceed to the next step by clicking the next icon 545.

[0119] Figure 6AThe diagram illustrates the alignment portion 601 of the GUI 400, which displays a 3D structural image 602 including a 3D segment model 606 superimposed on a scanned image 604. For example, the 3D segment model 606 could be a 3D segment model of a cardiac ventricle. The scanned image 604 could be an image scanned by an image scanning device 102, such as a 3D volume of a patient's scanned structure. The 3D structural image 602 also includes a target region map 648 that defines a target region for treatment of the patient. The target region map 648 may, at least initially (e.g., before adjustment by the EP), correspond to one or more selected target regions of a target-defined map (such as target regions 532, 542 of target-defined map 530). In some examples, the target region map 648 is displayed in different colors. In some examples, different shading is used to display the target region map 648, or any other suitable mechanism that allows the EP to easily determine the shape of the target region map 648. Furthermore, as shown, the longitudinal axis 650 passes through the tip 608 of the 3D structural image 602.

[0120] In some examples, the alignment portion 601 may also display a reference symbol 680. The reference symbol 680 is displayed by a view based on the orientation of the 3D structural image 602. For example, if the orientation of the 3D structural image 602 is such that it is being displayed in a top view when the corresponding organ is located inside the patient, then the reference symbol 680 is displayed in the top view. This allows the EP to easily determine what view and / or orientation is currently being used to display the 3D structural image 602.

[0121] In some examples, alignment portion 601 may include a text input box 640 that allows input values. In this example, the input value is myocardial thickness (e.g., left ventricular myocardial thickness). Myocardial thickness can be used to reconstruct the surface of the ventricular myocardium, where all identified target segments are projected as described herein. For example, target-defined computing device 104 may execute an algorithm to generate a final 3D target volume by combining all regions defined by the selected segments and the underlying projection. If the user (e.g., EP) has not edited the myocardial thickness, a default value, such as 10 mm, is used. For example, target-defined computing device 104 may generate a 3D target volume based on selected segments as described herein. For example, in the example of the heart, target-defined computing device 104 may take the selected segment (e.g., which may be a portion of the epicardial wall) and stretch the volume toward the center of the left ventricle at a depth defined by the wall thickness.

[0122] In some examples, the alignment portion 601 includes one or more adjustment icons 655, which allow adjustments to the 3D structure image 602. For example, the adjustment icons 655 may allow zooming, panning, and rotation functions.

[0123] refer to Figure 6B The alignment section 601 may display one or more drag points, such as drag points 670A and 670B, which allow the EP to adjust the 3D structural image 602. For example, the EP can adjust the longitudinal axis 650 by dragging drag point 670A to a new position. In response, the GUI 400 adjusts the orientation of the scanned image 604 relative to the 3D segment model 606. Similarly, the EP can adjust the target region map 648 by dragging drag point 670B to a new position.

[0124] In some examples, GUI 400 allows the creation or removal of drag points. For instance, the EP can right-click a drag point, such as drag point 670B, and select the "Remove" option to remove the drag point. Similarly, the EP can right-click a portion of the 3D segment model 606 and select the "Add" option to add a drag point.

[0125] Figure 6C The illustration shows a 3D structural image 602 after the EP provides input to rotate the 3D structural image 602 clockwise about the longitudinal axis 650. In this example, drag point 670C allows the EP to adjust the anterior interventricular groove 686 of the 3D structural image 602.

[0126] Adjusting icon 655 can also allow the EP to display images of additional organs, such as organs adjacent to the organ identified in scan image 604. For example, and referring to... Figure 6D EP can choose to adjust icon 655 to display organ selection box 675, which allows EP to select from one or more organs for display.

[0127] For example, and assuming EP selects "lung" (e.g., right lung "lung_r_p" or left lung "lung_l_p") and "esophagus", GUI 400 can display the rendering (e.g., 3D rendering) of the first organ 685 (e.g., lung) and the second organ 687 (e.g., esophagus), such as Figure 6E As shown. For example, the rendering could be a 3D model pre-stored in database 116. In other examples, the rendering is a scanned image of the patient's corresponding structure.

[0128] Figure 11 This is a flowchart of an example method 1100 that can be executed by, for example, a target-defined computing device 104. Starting at step 1102, a first input is received. The first input identifies the selected research type. For example, the EP can use input / output device 203 to provide input to a GUI (such as GUI 400) displayed on a display 206 for the executed application. The EP can select a research type 424 to be displayed within a portion 420 of GUI 400.

[0129] In step 1104, multiple research categories are provided for display. These categories are determined based on the selected research type. For example, the GUI 400 may display multiple research categories within a research category dropdown menu 428. Proceeding to step 1106, a second input is received. This second input identifies the selected research category among the multiple research categories. For example, the EP may select one of the multiple research categories displayed within the research category dropdown menu 428.

[0130] In step 1108, multiple research locations are provided for display. These multiple research locations are determined based on the selected research category. For example, the GUI 400 may display multiple research locations within a research location drop-down menu 430. In step 1110, a third input is received. The third input identifies the selected research location among the multiple research locations. For example, an EP may select one of the multiple research locations displayed within the research location drop-down menu 430.

[0131] Proceeding to step 1112, the selected research type, research category, and research location are stored in a data repository. For example, step 104 can generate research data records identifying the selected research category and research location, and these records can be stored in database 116. The method then concludes.

[0132] Figure 12 This is a flowchart of an example method 1200 that can be performed, for example, by a target-defined computing device 104. Starting in step 1202, a patient's study data record is obtained. The study data record identifies multiple studies performed on the patient. For example, the target-defined computing device 104 may obtain the patient's study-defined data 303 from a database 116. In step 1204, a study category is determined for each of the multiple studies. For example, each of the multiple studies may be associated with a study category such as "electricity" or "structure". In step 1206, the number of studies in each different category is determined. Furthermore, in step 1208, the therapeutic target region for each of the multiple studies is determined. For example, each of the multiple studies may be associated with one or more segments of a targeted therapy.

[0133] Proceeding to step 1210, a first graph is generated for each study category based on the corresponding number of research and treatment target regions. For example, the target-limited computing device 104 can determine the percentage of studies that treat each segment of a patient's organ across multiple segments for each study category. For example, each first graph in the first graph could be a study category graph 510, 520.

[0134] In step 1212, a second graph is generated. The second graph is generated based on the first graph and the corresponding therapeutic target region. For example, the second graph may indicate the probability of research on one or more parts of the patient's organ based on the parts identified in the first graph. For example, the second graph may be probability graph 502, which indicates the probability of treatment for one or more parts of the patient's organ based on the parts of the organ identified in research category graphs 510, 520.

[0135] In step 1214, a first image and a second image are provided for display. For example, the first image and the second image may be displayed within the target selection section 501 of the GUI 400. The method then ends.

[0136] Figure 13A This is a flowchart of an example method 1300 that can be executed, for example, by a target-defined computing device 104. At step 1302, first data is received. The first data identifies a therapeutic target region of the patient's organ. For example, based on a segment of a target definition map 530 that has been selected by the EP to identify the target region 532, the target-defined computing device 104 can determine the therapeutic target region. In step 1304, an image of the patient's organ is obtained. For example, the target-defined computing device 104 can obtain an image of the patient's organ scanned by an image scanning device 102, such as a 3D volumetric image.

[0137] Proceeding to step 1306, a first digital model of the patient's organ type is generated. For example, the target-defined computing device 104 can generate a 3D model of the patient's organ, such as a 3D ventricular model 720 or a 3D segment model 1002B. In step 1308, the alignment of the image of the patient's organ with the first digital model is determined. Furthermore, and in step 1310, a second digital model is generated. The second digital model includes at least a portion of the image of the patient's organ and the first digital model. For example, the target-defined computing device 104 can overlay a 3D segment model 606 onto the scanned image 604 to generate a 3D structural image 602. In step 1312, the second digital model is provided for display. For example, the target-defined computing device 104 can display the second digital model to the EP. Then, the method ends.

[0138] Figure 13B This is a flowchart of an example method 1350 that can be executed by, for example, a target-defined computing device 104. Starting at step 1352, a digital model for display is provided. The digital model includes portions of an image of a patient's organ and a second digital model of the organ's type. For example, it can be based on... Figure 13AMethod 1300 generates a digital model. At step 1354, input is received. This input identifies alignment adjustments to the digital model. For example, 104 can use input / output device 203 to receive input from EP to adjust the 3D structural image 602 by dragging one or more drag points 670 as described herein.

[0139] Proceeding to step 1356, adjustments to the digital model are determined based on the input. For example, this adjustment could be a change in the orientation of an image of a patient's organ relative to the second digital model. For instance, the EP can adjust the orientation by dragging one or more drag points 670 to move the longitudinal axis 650. In some examples, the adjustment could be a change to the target region map of the digital model. For example, the adjustment could be to the target region map 648 of the 3D structural image 602.

[0140] In step 1358, the digital model is regenerated based on the determined adjustments. Further, in step 1360, the regenerated digital model is provided for display. In some examples, 104 transmits the regenerated digital model to the radiation ablation therapy system 126 for treating the patient. The method then terminates.

[0141] In some examples, a system includes a computing device. The computing device is configured to receive a first input identifying a patient's organ and to receive a scanned image of that organ. The computing device is also configured to generate a first digital model of the organ type. Furthermore, the computing device is configured to determine the alignment of the scanned image with the first digital model. The computing device is also configured to generate a second digital model comprising at least a portion of the scanned image and the first digital model. The computing device is further configured to store the second digital model in a data repository. In some examples, receiving the first input is in response to the selection of a portion of a displayed target-defined map. In some examples, the organ is a heart. In some examples, the computing device is configured to provide the second digital model for display.

[0142] In some examples, the computing device is configured to receive a second input identifying an adjustment to the alignment of the scanned image with the first digital model. The computing device is also configured to adjust the second digital model based on the second input. The computing device is further configured to store the adjusted second digital model in a data repository.

[0143] In some examples, the computing device is configured to receive a second input identifying a therapeutic target region of an organ. The computing device is also configured to determine a corresponding portion of a second digital model based on the therapeutic target region of the organ. Furthermore, the computing device is configured to regenerate the second digital model to identify the corresponding portion. In some examples, regenerating the second digital model includes associating the corresponding portion with different features used for display. In some examples, the computing device is also configured to transmit treatment data identifying the therapeutic target region of the organ to a radiation ablation therapy system.

[0144] In some examples, the computing device is configured to acquire patient study data records, wherein each study data record identifies one of a plurality of study types performed on the patient and a study target region within a plurality of study target regions. The computing device is also configured to determine a first number of each of the plurality of study types performed on the patient based on the study data records. Furthermore, the computing device is configured to: for each of the plurality of study types, determine a second number of studies performed on the patient in each study target region within the plurality of study target regions. The computing device is also configured to generate a first graph for each of the plurality of study types based on the corresponding first and second numbers. The computing device is further configured to store the first graph in a data repository. In some examples, each first graph indicates the frequency of the corresponding study type on each study target region within the plurality of study target regions. In some examples, the computing device is further configured to generate a second graph based on the first and second numbers and store the second graph in the data repository, wherein the second graph indicates the probability of treatment for each study target region within the plurality of study target regions.

[0145] In some examples, a computer-implemented method includes receiving a first input identifying a patient's organ and receiving a scanned image of that organ. The method also includes generating a first digital model of the organ type. Furthermore, the method includes determining the alignment of the scanned image with the first digital model. The method also includes generating a second digital model, the second digital model including at least a portion of the scanned image and the first digital model. The method also includes storing the second digital model in a data repository. In some examples, the first input is received in response to selection of a portion of a displayed target-defined map. In some examples, the organ is a heart. In some examples, the method includes providing a second digital model for display.

[0146] In some examples, the method includes receiving a second input that identifies an adjustment to the alignment of the scanned image with the first digital model. The method also includes adjusting a second digital model based on the second input. The method further includes storing the adjusted second digital model in a data repository.

[0147] In some examples, the method includes receiving a second input identifying a therapeutic target region of an organ. The method also includes determining a corresponding portion of a second digital model based on the therapeutic target region of the organ. Furthermore, the method includes regenerating the second digital model to identify the corresponding portion. In some examples, regenerating the second digital model includes associating the corresponding portion with different features used for display. In some examples, the method includes transmitting treatment data identifying the therapeutic target region of the organ to a radiation ablation therapy system.

[0148] In some examples, the method includes obtaining a patient's study data record, wherein each study data record identifies one of a plurality of study types performed on the patient and a study target region within a plurality of study target regions. The method also includes determining a first number of each of the plurality of study types performed on the patient based on the study data record. Furthermore, the method includes determining a second number of studies performed on the patient in each study target region within the plurality of study target regions for each of the plurality of study types. The method also includes generating a first graph for each of the plurality of study types based on the corresponding first and second numbers. The method further includes storing the first graph in a data repository. In some examples, each first graph indicates the frequency of the corresponding study type on each study target region within the plurality of study target regions. In some examples, the method includes generating a second graph based on the first and second numbers and storing the second graph in a data repository, wherein the second graph indicates the treatment probability for each study target region within the plurality of study target regions.

[0149] In some examples, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause the processor to perform operations including receiving a first input identifying a patient organ and receiving a scanned image of the organ. The operation also includes generating a first digital model of the organ type. Furthermore, the operation includes determining an alignment between the scanned image and the first digital model. The operation also includes generating a second digital model comprising at least a portion of the scanned image and the first digital model. The operation further includes storing the second digital model in a data repository. In some examples, the first input is received in response to selection of a portion of a displayed target-defined map. In some examples, the organ is a heart. In some examples, the operation includes providing a second digital model for display.

[0150] In some examples, the operation includes receiving a second input that identifies an adjustment to the alignment of the scanned image with the first digital model. The operation also includes adjusting the second digital model based on the second input. Furthermore, the operation includes storing the adjusted second digital model in a data repository.

[0151] In some examples, the operation includes receiving a second input identifying a therapeutic target region of an organ. The operation also includes determining a corresponding portion of a second digital model based on the therapeutic target region of the organ. Furthermore, the operation includes regenerating the second digital model to identify the corresponding portion. In some examples, regenerating the second digital model includes associating the corresponding portion with different features used for display. In some examples, the operation includes transmitting treatment data identifying the therapeutic target region of the organ to a radiation ablation therapy system.

[0152] In some examples, the operation includes obtaining a patient's study data record, wherein each study data record identifies one of a plurality of study types performed on the patient and a study target region within a plurality of study target regions. The operation also includes determining a first number of each of the plurality of study types performed on the patient based on the study data record. Furthermore, the operation includes determining a second number of studies performed on the patient in each study target region within the plurality of study target regions for each of the plurality of study types. The operation also includes generating a first graph for each of the plurality of study types based on the corresponding first and second numbers. The operation also includes storing the first graph in a data repository. In some examples, each first graph indicates the frequency of the corresponding study type on each study target region within the plurality of study target regions. In some examples, the operation includes generating a second graph based on the first and second numbers and storing the second graph in a data repository, wherein the second graph indicates the treatment probability for each study target region within the plurality of study target regions.

[0153] In some examples, a computer-implemented method includes means for receiving a first input identifying a patient's organ and receiving a scanned image of that organ. The method also includes means for generating a first digital model of the organ type. Furthermore, the method includes means for determining the alignment of the scanned image with the first digital model. The method also includes means for generating a second digital model, which includes at least a portion of the scanned image and the first digital model. The method also includes means for storing the second digital model in a data repository. In some examples, the first input is received in response to selection of a portion of a displayed target-defined map. In some examples, the organ is a heart. In some examples, the method includes means for providing the second digital model for display.

[0154] In some examples, the method includes means for receiving a second input identifying adjustments to the alignment of the scanned image with the first digital model. The method also includes means for adjusting the second digital model based on the second input. The method further includes means for storing the adjusted second digital model in a data repository.

[0155] In some examples, the method includes means for receiving a second input identifying a therapeutic target region of an organ. The method also includes means for determining a corresponding portion of a second digital model based on the therapeutic target region of the organ. Furthermore, the method includes means for regenerating the second digital model to identify the corresponding portion. In some examples, regenerating the second digital model includes associating the corresponding portion with different features for display. In some examples, the method includes means for transmitting treatment data identifying the therapeutic target region of the organ to a radiation ablation therapy system.

[0156] In some examples, the method includes means for obtaining a patient's research data record, wherein each research data record identifies one of a plurality of research types performed on the patient and a research target region within a plurality of research target regions. The method also includes means for determining a first number of each of the plurality of research types performed on the patient based on the research data record. Furthermore, the method includes means for determining a second number of studies performed on the patient in each research target region within the plurality of research target regions for each of the plurality of research types. The method also includes means for generating a first graph for each of the plurality of research types based on the corresponding first and second numbers. The method also includes means for storing the first graph in a data repository. In some examples, each first graph indicates the frequency of the corresponding research type on each research target region within the plurality of research target regions. In some examples, the method includes means for generating a second graph based on the first and second numbers and storing the second graph in a data repository, wherein the second graph indicates the treatment probability for each research target region within the plurality of research target regions.

[0157] Although the method described above refers to the flowchart shown in the figure, it will be understood that many other ways can be used to perform the actions associated with the method. For example, the order of some operations can be changed, and some of the operations described can be optional.

[0158] Furthermore, the methods and systems described herein can be embodied, at least in part, as computer-implemented processes and means for performing those processes. The disclosed methods can also be embodied, at least in part, as tangible, non-transitory, machine-readable storage media encoded with computer program code. For example, the steps of the method can be embodied as hardware, executable instructions (e.g., software) executed by a processor, or a combination of both. The media can include, for example, RAM, ROM, CD-ROM, DVD-ROM, BD-ROM, hard disk drive, flash memory, or any other non-transitory, machine-readable storage medium. When the computer program code is loaded into and executed by the computer, the computer becomes a means for performing the method. The method can also be embodied, at least in part, as a computer in which the computer program code is loaded or executed, making that computer a dedicated computer for performing the method. When implemented on a general-purpose processor, the computer program code segments configure the processor to create specific logic circuits. Alternatively, the method can be embodied, at least in part, in an application-specific integrated circuit (ASIC) for performing the method.

[0159] The above content is provided for the purpose of illustrating, explaining, and describing embodiments of these disclosures. Modifications and variations to these embodiments will be readily apparent to those skilled in the art and can be made without departing from the scope or spirit of these disclosures.

Claims

1. A system comprising: The computing device is configured as follows: The first input to identify the patient's organs; Receive scanned images of the organ; Generate a first digital model of the type of said organ; Determine the alignment of the scanned image with the first digital model; A second digital model is generated based on the alignment, the second digital model including at least a portion of the scanned image and the first digital model; Receive a second input that identifies the therapeutic target region of the organ; Receive the patient's patient data; A probability map is generated based on the patient data, the probability map indicating the treatment probability of the treatment target region of the organ; The second digital model and the probability graph are provided for display; as well as The second digital model and the probability graph are stored in a data repository.

2. The system according to claim 1, wherein the computing device is further configured to: Receive a third input, the third input identifying an adjustment for the alignment of the scanned image with the first digital model; Adjust the second digital model based on the third input; and The adjusted second digital model is stored in the data repository.

3. The system according to claim 1, wherein the computing device is further configured to: Based on the therapeutic target region of the organ, the corresponding portion of the second digital model is determined; and The second digital model is regenerated to identify the corresponding parts.

4. The system of claim 3, wherein regenerating the second digital model comprises: The corresponding portion is associated with different features used for display.

5. The system of claim 3, wherein the computing device is further configured to transmit treatment data identifying the treatment target region of the organ to the radioablation therapy system.

6. The system of claim 1, wherein the computing device is further configured to: Obtain the patient's research data record, wherein each research data record identifies one of multiple research types and one research target region among multiple research target regions performed on the patient; Based on the research data records, a first number of each of the plurality of research types performed on the patient is determined; For each of the plurality of study types, a second number of studies to be performed on the patient in each of the plurality of study target regions; Based on the corresponding first number and second number, a first graph is generated for each of the plurality of research types; as well as The first graph is stored in the data repository.

7. The system of claim 6, wherein each first figure indicates the frequency of a corresponding research type in each of the plurality of research target regions.

8. The system of claim 6, wherein the computing device is further configured to: A second graph is generated based on the first number and the second number, wherein the second graph indicates the probability of treatment for each of the plurality of study target regions; and The second graph is stored in the data repository.

9. The system of claim 8, wherein the first input is received in response to selection of a portion of the displayed target definition map.

10. A computer-implemented method, comprising: The first input to identify the patient's organs; Receive scanned images of the organ; Generate a first digital model of the type of said organ; Determine the alignment of the scanned image with the first digital model; A second digital model is generated based on the alignment, the second digital model including at least a portion of the scanned image and the first digital model; Receive a second input that identifies the therapeutic target region of the organ; Receive the patient's patient data; A probability map is generated based on the patient data, the probability map indicating the treatment probability of the treatment target region of the organ; The second digital model and the probability graph are provided for display; as well as The second digital model and the probability graph are stored in a data repository.

11. The computer-implemented method according to claim 10, comprising: Receive a third input, the third input identifying an adjustment for the alignment of the scanned image with the first digital model; Based on the third input, adjust the second digital model; as well as The adjusted second digital model is stored in the data repository.

12. The computer-implemented method according to claim 10, comprising: Based on the therapeutic target region of the organ, the corresponding part of the second digital model is determined; as well as The second digital model is regenerated to identify the corresponding part.

13. The computer-implemented method according to claim 12, comprising: The treatment data identifying the treatment target region of the organ is transmitted to the radioablation treatment system.

14. The computer-implemented method according to claim 10, comprising: Obtain the patient's research data record, wherein each research data record identifies one of multiple research types and one research target region among multiple research target regions performed on the patient; Based on the research data records, a first number of each of the plurality of research types performed on the patient is determined; For each of the plurality of study types, a second number of studies to be performed on the patient in each of the plurality of study target regions; Based on the corresponding first number and second number, a first graph is generated for each of the plurality of research types; as well as The first graph is stored in the data repository.

15. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations, the operations comprising: The first input to identify the patient's organs; Receive scanned images of the organ; Generate a first digital model of the type of said organ; Determine the alignment of the scanned image with the first digital model; A second digital model is generated based on the alignment, the second digital model including at least a portion of the scanned image and the first digital model; Receive a second input that identifies the therapeutic target region of the organ; Receive the patient's patient data; A probability map is generated based on the patient data, the probability map indicating the treatment probability of the treatment target region of the organ; The second digital model and the probability graph are provided for display; as well as The second digital model and the probability graph are stored in a data repository.

16. The non-transitory computer-readable medium of claim 15, wherein the operation further comprises: Based on the therapeutic target region of the organ, the corresponding part of the second digital model is determined; as well as The second digital model is regenerated to identify the corresponding part.

17. The non-transitory computer-readable medium of claim 15, wherein the operation further comprises: Obtain the patient's research data record, wherein each research data record identifies one of multiple research types and one research target region among multiple research target regions performed on the patient; Based on the research data records, a first number of each of the plurality of research types performed on the patient is determined; For each of the plurality of study types, a second number of studies to be performed on the patient in each of the plurality of study target regions; Based on the corresponding first number and second number, a first graph is generated for each of the plurality of research types; as well as The first graph is stored in the data repository.

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