Method and apparatus for automatically determining the risk of radiation ablation treatment

Abstract

A system and method for cardiac radioablation treatment and planning are disclosed. In some embodiments, a computing device receives a first image of a patient taken for treatment planning. The computing device also receives target definition data characterizing a target region of a patient to be treated. Further, the computing device receives a second image of the patient taken prior to treatment on the day of treatment. The computing device determines whether there is a risk to the patient's organ based on the first image, the target definition data, and the second image. The computing device also displays a risk indication for the patient on a display based on that determination. For example, the risk indication can include a treatment target region and a risk organ region superimposed on the second image. In some examples, the risk indication includes an expected dose level.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims priority to U.S. Application No. 17 / 810,159, filed on June 30, 2022, "Methods and Apparatus for Automatically Determining Risks of Radiation Ablation Therapy", which is hereby incorporated by reference in its entirety.

Background Art

[0002] Aspects of the disclosure generally relate to medical diagnostic and treatment systems, and more particularly to providing radioablation diagnosis, treatment planning, and treatment systems for treating conditions such as atrial fibrillation.

[0003] A variety of techniques are available for acquiring or imaging a patient's metabolic, electrical, and anatomical information. For example, positron emission tomography (PET) is a metabolic imaging technique that generates tomographic images representing the distribution of positron - emitting isotopes in the body. Computed tomography (CT) and magnetic resonance imaging (MRI) are anatomical imaging techniques that create images using X - rays and magnetic fields, respectively. Images obtained from these representative techniques can be combined with each other to generate composite anatomical and functional images such as PET / CT and PET / MRI. For example, software systems such as Varian Medical Systems, Inc.'s Velocity (registered trademark) software can use an image fusion process to combine different types of images, deform and / or align the images, and generate composite images.

[0004] Medical experts such as electrophysiologists and radiation oncologists rely on these images to identify the target area for treatment. For example, in cardiac radioablation, medical experts work together to diagnose cardiac arrhythmias, identify the target area for ablation, prescribe radiation therapy, and create a radioablation treatment plan. Before treating a patient, the patient may be scanned using one or more anatomical imaging techniques to obtain an image of the patient. An electrophysiologist may identify one or more regions (e.g., targets) of the patient's organ to be treated, such as the patient's heart, based on the image for the treatment of cardiac arrhythmias. Once the electrophysiologist defines the target area, the radiation oncologist may prescribe radiation therapy. The prescription may include, for example, the number of fractions of radiation to be delivered, the dose of radiation to be delivered to the target area (e.g., within the area identified by the electrophysiologist), and the maximum dose that can be delivered to adjacent at-risk organs. Additionally, a dosimetrist may create a radioablation treatment plan based on the prescribed radiation therapy. In some examples, the radiation oncologist may review and approve the treatment plan. Further, the electrophysiologist may examine the location, size, and shape of the defined target area to confirm that the target site defined in the radioablation treatment plan is appropriate.

[0005] Systems and methods for cardiac radioablation treatment and planning are disclosed. In some embodiments, prior to treatment, an imaging device scans a patient and acquires one or more images of the patient using one or more of any suitable anatomical imaging techniques such as CT, MRI, or PET. The images may identify the organ to be treated and healthy organs or portions of organs that are not to be treated but may be in proximity to the organ to be treated (e.g., OAR). A treatment plan is then formulated based on the acquired images. For example, a medical professional can identify one or more regions of the organ to be treated based on the images. Additionally, a radiation oncologist may determine the radiation dose to irradiate the target region based on the regions identified by the medical professional and the images. Further, a treatment planning system may store the treatment plan (TP) image, target definition data characterizing the target region, and the radiation dose to be irradiated in a data repository. Thereafter, a treatment schedule for the patient is organized based on the treatment plan, typically several days or weeks after the TP image is acquired.

[0006] On the day of treatment, one or more additional images of the patient (i.e., treatment delivery (TD) images) are acquired by scanning the patient using an imaging device installed on or near the treatment device. Further, a treatment application device retrieves target definition data characterizing the target region and the radiation dose from the data repository and displays the target region overlaid on one of the TD images. For example, the treatment application device may determine the location of the target region within one or more of the TD images and display an overlay of the target region at the determined location over one of the TD images. Prior to treatment, a medical professional, such as a healthcare provider who controls the treatment device to deliver the dose to the patient, can determine whether it is still acceptable to deliver the dose to the target region based on the location of organs such as OAR, as illustrated by the display of the overlay on the TD image of the target region at the determined location.

[0007] In some examples, the treatment application system retrieves TP images and target definition data from a data repository and automatically determines whether there is a risk to a patient's organ (e.g., any OAR) based on the TP images, the target definition data, and one or more TD images. For example, the treatment application system can calculate one or more dose volume histograms (DVHs) based on the relative position of the target region with respect to one or more OARs in the additional images and determine whether one or more DVH dose constraints are violated based on the calculated DVHs. For example, the treatment application system may determine that a DVH dose constraint has been violated if there is a risk that a portion of the OAR will receive a dose exceeding a predetermined threshold when the radiation dose is applied to the target region. In another example, the treatment application system determines the relative position between an organ such as an OAR in a TP image (e.g., obtained before treatment planning) and an organ such as an OAR in a TD image (e.g., obtained during treatment delivery) and may determine that an OAR dose constraint has been violated if the organ has moved more than a predetermined distance. However, in other examples, the treatment application system may determine the positional relationship (e.g., distance) between the target region and an organ such as an OAR in a TP image and determine another relative positional relationship between the target region and the same organ in a TD image. The treatment application system may determine that an OAR dose constraint has been violated based on the relative positional relationships determined for each of the TP image and the TD image. For example, the treatment planning system can compare (e.g., subtract) the determined relative positions and determine that an OAR dose constraint has been violated based on the comparison result (e.g., the difference is greater than or equal to a predetermined threshold).

[0008] The treatment application system may issue a warning when a risk is identified. For example, the treatment application system can provide one or more visual or auditory alerts to notify a risk determined by a medical professional. In some examples, the medical professional can provide an input to the treatment planning system to change the target area based on the alert. In some examples, the treatment application system can prevent supplying any dose to the patient until the medical professional clears the alert. In some examples, approval by at least a minimum number (e.g., two) of medical staff is required before the treatment application system permits irradiation of the patient. For example, the treatment application system can require at least two medical staff to enter identification (e.g., login) and clear the alert before permitting irradiation of the patient.

[0009] In some embodiments, the system includes a database and a computing device communicatively coupled to the database. The computing device is configured to receive a first image of a patient (e.g., a TP image). For example, the computing device may receive magnetic resonance (MR) image data, computed tomography (CT) image data, or positron emission tomography (PET) image data from an imaging system. The first image may be acquired during a treatment plan (e.g., before the treatment date). Also, the computing device is configured to receive target definition data characterizing a target region of the patient for treatment. This target definition data may include, for example, DVH data characterizing the expected dose levels for various portions of the target region. Further, the computing device is configured to receive a second image of the patient (e.g., a TD image). The second image may be acquired on the treatment day (e.g., while the patient is lying on the treatment table before receiving the prescribed treatment). The computing device is configured to determine whether there is a risk to an organ (e.g., an OAR) of the patient based on the first image, the target definition data, and the second image. The computing device is further configured to display a risk indication based on that determination.

[0010] In some embodiments, a computer-implemented method includes receiving a first image of a patient (e.g., a TP image). The first image may be acquired during a treatment plan (e.g., before the treatment day). The computer-implemented method also includes receiving target definition data characterizing a target region of the patient for treatment. The target definition data may include, for example, DVH data characterizing the expected dose levels in various portions of the target region. Further, the computer-implemented method includes receiving a second image of the patient (e.g., a TD image). The second image may be acquired on the treatment day (e.g., while the patient is lying on the treatment table before receiving the prescribed treatment). Also, the method implemented on a computer includes determining whether there is a risk to an organ (e.g., an OAR) of the patient based on the first image, the target definition data, and the second image. Further, the method implemented on a computer includes displaying on a display a risk indication based on that determination.

[0011] In some examples, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations including receiving a first image of a patient (e.g., a TP image). The first image may be acquired during a treatment plan (e.g., before the treatment day). Also, the operations include receiving target definition data characterizing a target region of the patient to be treated. The target definition data may include, for example, DVH data characterizing the expected dose for various portions of the target region. Further, the operations include receiving a second image of the patient (e.g., a TD image). The second image may be acquired on the treatment day (e.g., while the patient is lying on the treatment table before receiving the prescribed treatment). Also, the operations include determining whether there is a risk to an organ (e.g., an OAR) of the patient based on the first image, the target definition data, and the second image. Further, the operations include displaying on a display a risk indication based on that determination.

[0012] In some embodiments, a system including at least one computing device includes means for receiving a first image of a patient (e.g., a TP image). The first image may be acquired during a treatment plan (e.g., before the treatment day). The system also includes means for receiving target definition data characterizing a target region of the patient to be treated. The target definition data may include, for example, DVH data characterizing the expected dose levels for various parts of the target region. Further, the system includes means for receiving a second image of the patient (e.g., a TD image). The second image may be acquired on the treatment day (e.g., while the patient is lying on the treatment table before receiving the prescribed treatment). Also, the system includes means for determining, based on the first image, the target definition data, and the second image, whether there is a risk to an organ (e.g., an OAR) of the patient. Further, the system includes means for displaying a risk indication based on the determination on a display.

[0013] In some embodiments, the system includes a database and a computing device communicatively coupled to the database. The computing device is configured to receive a first image of a patient. For example, the computing device may receive magnetic resonance (MR) image data, computed tomography (CT) image data, or positron emission tomography (PET) image data from an imaging system. The first image may be acquired during a treatment plan (e.g., before the treatment day). Further, the computing device is configured to display a first risk organ region and a first target region overlaid on the first image. Additionally, the computing device is configured to receive a second image of the patient. The second image may be acquired on the treatment day (e.g., when the patient is on the treatment table and before receiving treatment). Also, the computing device is configured to determine a second risk organ region within the second image. The second risk organ region of the second image may include the same organs as the organs within the first risk organ region of the first image. For example, the second risk organ region of the second image may at least partially overlap with the first risk organ region of the first image. The computing device is further configured to determine a second target region within the second image based on the first target region, the first risk organ region, and the second risk organ region. The computing device is also configured to provide a display of the second risk organ region and the second target region overlaid on the second image.

[0014] In some embodiments, a computer-implemented method includes receiving a first image of a patient. The first image may have been acquired during a treatment plan (e.g., prior to the treatment day). The computer-implemented method also includes displaying, overlaid on the first image, a display of a first target region and a first organ region at risk. Further, the computer-implemented method includes receiving a second image of the patient. The second image may be acquired on the treatment day (e.g., while the patient is lying on a treatment table prior to receiving a prescribed treatment). The computer-implemented method further includes determining a second organ region at risk within the second image. The computer-implemented method also includes determining a second target region within the second image based on the first target region, the first organ region at risk, and the second organ region at risk. The computer-implemented method also includes further providing a display of the second organ region at risk and the second target region for display overlaid on the second image.

[0015] In some examples, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations including receiving a first image of a patient. The first image may have been acquired during a treatment plan (e.g., prior to the treatment day). The operations also include displaying, overlaid on the first image, a display of a first organ region at risk and a first target region. Further, the operations include receiving a second image of the patient. The second image may be acquired on the treatment day (e.g., while the patient is lying on a treatment table prior to receiving treatment). Further, the operations include determining a second organ region at risk within the second image. The operations also include determining a second target region within the second image based on the first target region, the first organ region at risk, and the second organ region at risk. Further, the operations include providing a display of the second organ region at risk and the second target region for display overlaid on the second image.

[0016] In some embodiments, a system including at least one computing device includes means for receiving a first image of a patient. The first image may be acquired during a treatment plan (e.g., before the treatment day). The system also includes means for displaying a display of a first target region and a first organ at risk region that is overlaid on the first image. Further, the system includes means for receiving a second image of the patient. The second image may be acquired on the treatment day (e.g., while the patient is on the treatment table and before receiving treatment). The system further includes means for determining a second organ at risk region within the second image. The system also includes means for determining a second target region within the second image based on the first target region, the first organ at risk region, and the second organ at risk region. Further, the system includes means for providing a display of the second organ at risk region and the second target region for overlay display on the second image.

[0017] The features and advantages of the present disclosure will be more fully disclosed or become apparent in the following detailed description of exemplary embodiments. The detailed description of the exemplary embodiments is to be considered in conjunction with the accompanying drawings, where like numbers refer to like parts, and further reference is made to the following.

Brief Description of the Drawings

[0018]

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[0019] The description of the preferred embodiments is intended to be read in conjunction with the accompanying drawings and should be considered a part of the overall description of these disclosures. Although various modifications and alternative forms are possible, specific embodiments are illustrated in the drawings and described in detail herein. In conjunction with the accompanying drawings, the following detailed description of these exemplary embodiments will make the objectives and advantages of the claimed subject matter more apparent.

[0020] However, it should be understood that the present disclosure is not intended to be limited to the specific forms disclosed. Rather, the present disclosure is directed to all modifications, equivalents, and alternatives falling within the spirit and scope of these exemplary embodiments. Terms such as "coupled," "coupled to," "operatively coupled," "operatively connected," etc., are to be broadly understood to connect connecting devices or components to each other in a mechanical, electrical, wired, wireless, or other manner such that the associated devices or components can operate (e.g., communicate) with each other as intended by their relationship.

[0021] Accurately identifying and defining the region of an organ of a patient to be treated is essential for formulating and optimizing a treatment plan. For example, irradiating a dose to an unintended region may harm the patient. For example, when the prescribed dose is irradiated to a part of a risk organ (OAR) outside the treatment target. For example, a plan to irradiate a high single-fraction radiation dose to a part of the anatomical structure of the heart is usually made in CRA treatment. Some organs and structures near the treatment region are very sensitive to external beam radiation. For example, the stomach and esophagus. Since exceeding the dose limit values for these organs may cause serious effects, in the CRA treatment plan, the main purpose is to protect these structures from high-dose radiation.

[0022] In at least some cases, the anatomical arrangement of a patient's organs may be different between when initially scanned to determine the patient's treatment plan and on the treatment day. In such cases, there is no guarantee that the dose to any OAR determined in the treatment plan is still correct. In fact, depending on the change in the patient's anatomical arrangement, the OAR may receive a higher dose than determined in the original treatment plan. The embodiments described herein may improve a radiation ablation treatment planning system used by medical personnel, such as a cardiac radiation ablation treatment planning system used for cardiac radiation ablation treatment planning. One of the advantages is automatically determining whether the patient's OAR is at an unacceptable high risk level on the treatment day.

[0023] Referring to the drawings, FIG. 1 shows a block diagram of a treatment planning and application system 100 that includes an imaging device 102, a treatment planning calculation device 106, a treatment application calculation device 108, one or more target definition calculation devices 104, a treatment device 110, and a database 116 communicatively coupled via a communication network 118. The imaging device 102 may be, for example, a CT scanner, an MR scanner, a PET scanner, an electrophysiological imaging device, or an electrocardiogram (ECG) imager. In some examples, the imaging device 102 may be a PET / CT scanner or a PET / MR scanner. The treatment device 110 may be a photon (such as a Varian® TrueBeam radiotherapy system) or a proton beam radiotherapy device. In some examples, the target definition calculation device 104, the treatment planning calculation device 106, the treatment application calculation device 108, and the treatment device 100 may be part of a cardiac radioablation treatment system 126 that enables radioablation treatment for a patient. For example, the radioablation treatment system 126 may be capable of irradiating one or more treatment areas of a patient with a defined dose.

[0024] Each target definition calculation device 104, treatment planning calculation device 106, and treatment application calculation device 108 can be any suitable computing device including any suitable hardware or combination of hardware and software for processing data. For example, each can 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 circuits. Further, each can send data to and receive data from communication network 118. For example, each target definition calculation device 104, treatment planning calculation device 106, and treatment application calculation device 108 can be a server such as a cloud-based server, computer, laptop, mobile device, workstation, or any other suitable computing device.

[0025] For example, FIG. 2 shows an exemplary computing device 200, which may be an example of any of the target definition calculation device 104, treatment planning calculation device 106, and treatment application calculation device 108. The exemplary computing device 200 includes one or more processors 201, working memory 202, one or more input / output (I / O) devices 203, instruction memory 207, transceiver 204, one or more communication ports 207, and display 206, all of which are operably coupled to one or more data buses 208. The data bus 208 enables communication between the various devices. The data bus 208 can include a wired or wireless communication channel.

[0026] Processor 201 includes one or more different processors, and each processor has one or more cores. Each different processor can have the same or different architectures. Processor 201 can 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.

[0027] Instruction memory 207 can store instructions that can be accessed (e.g., read) and executed by processor 201. For example, instruction memory 207 can 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 other suitable memory. Processor 201 can be configured to execute a specific function or operation by executing the code stored in instruction memory 207 that performs the specific function or operation. For example, processor 201 can be configured to execute the code stored in instruction memory 207 to perform one or more of the functions, methods, or operations disclosed herein.

[0028] Furthermore, processor 201 can store data in working memory 202 and read data therefrom. Processor 201 can also use working memory 202 to store dynamic data created during the operation of computing device 200. Working memory 202 can be a random access memory (RAM) such as static random access memory (SRAM) or dynamic random access memory (DRAM), or other suitable memory.

[0029] The input / output device 203 can include any suitable device capable of data input and output. For example, the input / output device 203 can include one or more of a keyboard, touchpad, mouse, stylus, touch screen, physical buttons, speaker, microphone, or any other suitable input or output device.

[0030] The communication port 209 can include a serial port such as, for example, a Universal Asynchronous Receiver-Transmitter (UART) connection, a Universal Serial Bus (USB) connection, or other suitable communication port or connection. In some examples, the communication port 209 enables programming of executable instructions in the instruction memory 207. In some examples, the communication port 209 enables transfer (e.g., upload or download) of data such as image data.

[0031] The display 206 can be any suitable display, such as a 3D viewer or monitor. The display 206 can display the user interface 205. The user interface 205 can enable a user to interact with the computing device 200. For example, the user interface 205 can be a user interface for an application that enables a user (e.g., a medical professional) to view or manipulate a model to define a treatment target region of a patient, as described herein. In some examples, the user can operate an input / output device 203, such as a mouse, to interact with the user interface 205. In some examples, the display 206 is a touch screen, and the user interface 205 is displayed on the touch screen. In some examples, the display 206 displays an image of scanned image data (e.g., an image slice).

[0032] The transceiver 204 enables communication with a network such as the communication network 118 of FIG. 1. For example, if the communication network 118 of FIG. 1 is a cellular network, the transceiver 204 is configured to enable communication with that cellular network. In some examples, the transceiver 204 is selected based on the type of communication network 118 in which the exemplary computing device 200 operates. The processor 201 can receive data from a network such as the communication network 118 of FIG. 1 or transmit data to the network via the transceiver 204..

[0033] Referring to FIG. 1, the database 116 can be a remote storage device such as a cloud-based server, a disk (e.g., hard disk), a memory device on another application server, a networked computer, or other suitable remote storage, including non-volatile memory. In some examples, the database 116 can be a local storage device such as a hard drive, non-volatile memory, or USB stick for one or more of the target definition computing device 104, treatment planning computing device 106, and treatment application computing device 108.

[0034] The communication network 118 can be a WiFi (registered trademark) network, a cellular network such as a 3GPP (registered trademark) network, a Bluetooth (registered trademark) network, a satellite network, a wireless local area network (LAN), a network utilizing a radio frequency (RF) communication protocol, a near field communication (NFC) network, a wireless metropolitan area network (MAN) connecting multiple wireless LANs, a wide area network (WAN), or other suitable network. The communication network 118 can provide access to the Internet, for example.

[0035] Imaging device 102 is operable to scan an image such as an image of a patient's organ and provide image data 103 (e.g., measurement data) that identifies and characterizes the scanned image to communication network 118. Alternatively, imaging device 102 is operable to acquire an electrical image such as an electrocardiogram (ECG) image. For example, imaging device 102 scans a patient's structure (e.g., an organ) and can transmit image data 103 that identifies one or more slices of the scanned structure's 3D volume to one or more of treatment planning calculation device 106 and treatment application calculation device 108 via communication network 118. In some examples, imaging device 102 stores image data 103 in database 116 and one or more of treatment planning calculation device 106 and treatment application calculation device 108 may retrieve image data 103 from database 116.

[0036] In some examples, treatment planning calculation device 106 is communicable with treatment application calculation device 108 and target definition calculation device 104 via communication network 118. In some examples, one or more of treatment planning calculation device 106, treatment application calculation device 108, and target definition calculation device 104 communicate with each other via database 116 (e.g., by storing and retrieving data from database 116). In some examples, one or more of treatment planning calculation device 106 and one or more of treatment application calculation device 108 are part of a cloud-based network that enables sharing of resources and communication between the devices.

[0037] In some examples, one or more exemplary calculation devices 104 and 106 are located in a first area 122 of medical facility 120, while calculation device 108 and treatment device 100 are located in a second area 124 of medical facility 120. Thus, treatment planning and application system 100 enables multiple EPs to cooperate to determine a target area.

[0038] [Image generation before treatment application] As described herein, prior to a treatment plan (e.g., before the time of treatment), the imaging scanner 102 may scan a patient to acquire one or more treatment plan (TP) images. The images can identify the patient's anatomical layout by depicting various organs of the patient. Further, the imaging scanner 102 can store in the database 116 the image data 103 that characterizes the images. Based on the images, one or more medical professionals operating the treatment plan calculation device 106 can determine the patient's treatment plan. In the treatment plan, the treatment target area of the patient can be identified and the radiation dose to irradiate that area can be determined.

[0039] For example, the treatment plan calculation device 106 may execute an application that causes the generation of a user interface (e.g., user interface 205) that may be presented to a medical professional such as a dosimetrist. The executed application may assist in the definition of the target area of the patient to be treated by the medical professional. For example, the dosimetrist can operate the treatment plan calculation device 106 to define the patient's treatment target area. In some examples, the treatment plan can also identify one or more OAR areas within the TP image. The treatment plan calculation device 106 can store in the database 116 the target definition data that characterizes the treatment plan.

[0040] On the treatment day (e.g., immediately prior), the patient is scanned, for example, using a kV-cone beam CT integrated with a linear accelerator to obtain one or more treatment delivery (TD) images. The treatment application calculation device 108 may also obtain, from the database 116, one or more of the TP images and the target definition data. The TP images may identify the patient's original anatomical disposition at the time the TP images were acquired, while the TD images may identify the patient's current anatomical disposition. In at least some instances, the patient's current anatomical disposition may not be the same as the patient's original anatomical disposition. As such, the dose applied to the patient may be applied to the patient at sites and dose levels that were not previously considered, and the treatment plan originally prescribed for the patient may not be appropriate.

[0041] For example, on the day of treatment, the treatment application calculation device 108 may display the TP image obtained from the imaging scanning device 102 for treatment planning, together with an overlay of the treatment target region defined by the target definition data and the indication of one or more OAR regions. FIG. 4A shows a TP image 400 that identifies various structures such as structures 417, 419, 421, etc., and further includes a TP OAR region 402 defined by a dashed circle, and a TP treatment target region 404. As shown, the TP treatment target region 404 identifies various portions identified by hashing techniques, and each portion corresponds to a different level of the expected dose (e.g., DVH level). For example, the first portion 405 may identify the region of the TP treatment target region 404 that is expected to receive the highest dose. Further, the second portion 407 identifies the region of the TP treatment target region 404 that is expected to receive the next highest dose (e.g., less than the first portion 405), and the third portion 409 identifies the region of the TP treatment target region 404 that is expected to receive the next highest dose (i.e., less than the second portion 407). The fourth portion 411 may identify the region of the TP treatment target region 404 that is expected to receive the lowest dose level (i.e., less than the dose of the third portion 409 but more than the minimum dose, e.g., more than no dose at all). In this example, the TP OAR region 402 overlaps with the fourth region 411 and the overlapping region 420, but does not overlap with any of the first region 405, the second region 407, and the third region 409.

[0042] Referring to FIG. 1, the treatment application calculation device 108 can also display the TP image on the treatment day, together with an indication of the treatment target area defined by the target definition data and an overlay of indications of one or more OAR areas corresponding to the TP image. The OAR can be contoured manually or automatically using various available algorithms. For example, the treatment application calculation device 108 can display the TP image and receive input from a medical expert, such as a dosimetrist, who characterizes one or more corresponding OAR areas. When performed manually, the dosimetrist can "draw" the corresponding OAR area on the display, for example, using a user interface tool provided by the treatment planning software. The treatment application calculation device 108 can generate OAR data characterizing the OAR area based on the input and save the OAR data in the database 116.

[0043] Treatment application calculation device 108 may display the TP image along with an overlay of the treatment target area defined by the target definition data. Also, the determined OAR area (e.g., defined by the OAR data) may be displayed. For example, FIG. 4B shows a TD image 450 including a TD OAR area 452 and a TD treatment target area 454. The TD OAR area 452 may correspond to the TP OAR area determined by a medical expert (e.g., based on the TD image 450). Further, similar to the TP treatment target area 404, the TD treatment target area 454 identifies various portions distinguished by different hashings in this example, and each portion corresponds to a different level of the expected dose (e.g., DVH level). For example, the first portion 455 identifies the area of the TD treatment target area 454 expected to receive the highest dose. Further, the second portion 457 identifies the area of the TD treatment target area 454 expected to receive the next highest dose (e.g., less than the first portion 405), and the third portion 459 may identify the area of the TD treatment target area 454 expected to receive the next highest dose (i.e., less than the second portion 457). The fourth portion 461 may identify the area of the TD treatment target area 454 expected to receive the lowest dose level (i.e., less than the dose of the third portion 459 but more than the lowest dose, e.g., any dose more than zero dose).

[0044] In some examples, the treatment application calculation device 108 determines the TD treatment target region 454 based on applying one or more conversion models (e.g., algorithms) to one or more of the TP treatment target region 404, the TP OAR region 402, and the TD OAR region 452. For example, the treatment application calculation device 108 can identify corresponding structures, such as structures 417, 419, and 421, between the TP image 400 and the TD image 450. In some examples, the treatment application calculation device 108 inputs the TP image 400 and the TD image 450 into a trained machine learning model and identifies the corresponding structures based on receiving, as output, elements of output data that characterize the similar structures between the input images. In some examples, a medical practitioner provides an input to the treatment application calculation device 108 to identify the similar structures between the TP image 400 and the TD image 450.

[0045] Further, based on the determined corresponding structures, the treatment application calculation device 108 applies a transformation model to determine the association between the TP image 400 and the TD image 450. For example, the treatment application calculation device 108 can determine the positions of the structures in each of the TP image 400 and the TD image 450. The positions may each be defined by a bounding box that includes ranges for each of two dimensions (e.g., an X range and a Y range). In some examples, the positions specify the ranges of pixels of the corresponding images in each of a number of dimensions such as two or three dimensions. In some examples, the positions specify a single point (e.g., a pixel) of the image based on two-dimensional coordinates (e.g., X, Y positions). The single point may be, for example, the center of each structure. The treatment application calculation device 108 may apply the transformation model to the determined positions to determine a mapping (e.g., a two-dimensional mapping from the TP image 400 to the TD image 454). The transformation model may be, for example, a deformable image registration (DIR) algorithm. The DIR algorithm may be executed to map the spatial correspondence between two images. For example, the output generated by the executed DIR algorithm may include a vector that defines the mapping between the two images.

[0046] Furthermore, based on the mapping determined to be the TP treatment target area 404, the treatment application calculation device 108 generates image data characterizing the TD treatment target area 454. In this example, the TD treatment target area 454 overlaps with the fourth part 461 in the overlapping area 470 and also overlaps with the third part 459 in the overlapping area 472. For example, the area of the overlapping area 420 is smaller than the sum of the areas of the overlapping areas 470 and 472. Therefore, when the TP treatment plan is implemented, the TD OAR area 452 may receive a higher dose level than initially expected in the TP OAR area 402. After visually examining the TD image 450, the TD OAR area 452, and the TD treatment target area 454, a medical professional responsible for implementing the treatment plan may determine not to perform the treatment or to modify the treatment plan before implementing the treatment.

[0047] [Determination of Organ Risk] Referring to FIG. 1, as described herein, the treatment application calculation device 108 can determine whether there is a risk to a patient's organ based on one or more of the TP image, the target definition data, and the additional TD image.

[0048] For example, in some examples, the treatment application calculation device 108 can determine the relative positioning of the target area with respect to one or more OAR areas in the current image. As an example, the treatment application calculation device 108 can determine the distance between the TD OAR area 452 and the TD treatment target area 454. In some examples, the calculated distance may be the distance from the outermost edge of the OAR area to the outermost edge of the treatment target area (e.g., the closest distance). In some examples, the calculated distance may be the distance from the center of the OAR area to the center of the treatment target area. The treatment application calculation device 108 may determine that the OAR dose constraint is violated if the calculated distance is less than a predetermined threshold.

[0049] In other examples, the treatment application calculation device 108 may determine the relative positioning between an organ such as an OAR in the TP image and the TD image acquired later, and if the organ has moved more than a predetermined distance, it may determine that there is a risk to that organ. For example, the treatment application calculation device 108 can determine the position (e.g., distance) of an organ in the TP image based on the position of another organ, e.g., one of the structures 417, 419, 421 within the TP image 400, and further, determine the position of the same structure 417, 419, 421 relative to the same other structures 417, 419, 421 within the TD image, e.g., within the TD image 450. The treatment application calculation device 108 can then compare the positions of the TP image and the TD image to determine whether the organ has moved more than a predetermined amount. For example, the treatment application calculation device 108 can determine that the OAR dose constraint has been violated if the organ has moved more than a predetermined threshold (e.g., whether the organ has moved closer or farther to / from another organ by more than a predetermined threshold). For example, if the heart has moved at least 3 millimeters closer to the esophagus, there may be a violation of the OAR constraint.

[0050] In some examples, the treatment application calculation device 108 determines the volume of an organ within the TP image and the volume of the same organ within the TD image. The treatment application calculation device 108 can determine that the OAR dose constraint has been violated if the absolute value of the difference between the calculated volumes is greater than a predetermined threshold.

[0051] However, in other examples, the treatment application calculation device 108 may determine the positioning (e.g., distance) between the target region in the TP image and an organ such as an OAR, and another relative positioning between the target region in the TD image and the same organ. The treatment application calculation device 108 may determine that there is a risk to the organ based on the relative positionings determined for each of the TP image and the TD image. For example, the treatment application calculation device 108 may compare (e.g., subtract) the determined relative positionings and determine that the OAR dose constraint has been violated based on the comparison (e.g., the difference is greater than or equal to a predetermined threshold).

[0052] In some examples, the treatment application calculation device 108 can determine whether one or more DVH dose constraints are violated in one or more portions of the OAR region of the TP image, such as portions 455, 457 of the TP OAR region 452 of the TP image 450 (shown in FIG. 4B), or any of portions 455, 457, 459, 462 of the TP OAR region 452 of the TP image 450 (shown in FIG. 4B), based on the calculated DVH. For example, the treatment application calculation device 108 can determine the expected dose level within the TP overlap region of a TP image (e.g., an image taken during treatment planning), such as the initial overlap region 420. Further, the treatment application calculation device 108 can determine the region of the initial overlap region 420 (shown in FIG. 4A) and multiply the region by the expected dose level within the fourth portion 411 to determine the expected dose level within the TP overlap region 420. Similarly, the treatment application calculation device 108 can determine the TD dose level within the TD overlap regions 470, 472. For example, the treatment application calculation device 108 can determine the dose level within each of the overlap regions 470, 472 and add the dose levels to determine the TD dose level. The treatment application calculation device 108 can compare the expected dose level with the TD dose level to determine whether the DVH dose constraint has been violated. For example, the treatment application calculation device 108 can determine the difference between the TD dose level and the expected dose level and compare the difference with a predetermined threshold. If the difference is greater than the predetermined threshold, the treatment application calculation device 108 determines that the DVH dose constraint has been violated.

[0053] In some examples, the treatment application calculation device 108 may determine that the DVH dose constraint is violated if there is a risk that a part of the OAR region receives a dose exceeding a predetermined threshold (for example, when the radiation dose defined in the treatment plan irradiates the target region). For example, the treatment application calculation device 108 may determine that the OAR region overlaps a part of the current treatment target region and can determine whether the dose level associated with a part of the current treatment target region exceeds a predetermined threshold. If the dose level exceeds the predetermined threshold, the treatment application calculation device 108 determines that the DVH dose constraint is violated.

[0054] [Alert Generation] In some examples, the treatment application calculation device 108 displays an indication of whether the constraint has been violated. For example, as described herein, the treatment application calculation device 108 can determine whether the OAR or DVH dose constraint has been violated and display an indication within the user interface indicating whether any of the OAR and DVH dose constraints have been violated.

[0055] For example, FIG. 5A shows a graphical user interface (GUI) 500 that may be displayed by, for example, the display 206. The GUI 500 includes a DVH chart 502 and a risk alert pane 504. The DVH chart 502 may include a plot of the calculated DVH for each of one or more organs or parts of an organ. For example, the first plot 505 may correspond to the esophagus, and the second plot 507 may correspond to the target volume. In this example, the constraint is not violated. Therefore, the risk alert pane 504 indicates that no risk has been detected.

[0056] However, as shown in FIG. 5B, the risk alert pane 504 of the GUI 500 includes a risk warning that the dose threshold has been exceeded (DVH dose constraint violation). In this example, this alert may have been generated based on the DVH calculated for the first plot 505 to indicate that the esophagus may receive more dose than shown in FIG. 5A. In FIG. 5C, the GUI 500 displays the TD target region 522 and the TD OAR region 524 overlaid on the TD image 520, respectively. In this example, the treatment application calculation device 108 can determine that the OAR dose constraint has been violated, for example, based on the overlapping region 530 of the TD target region 522 and the TD OAR region 524. Thus, the treatment application calculation device 108 can display a risk warning of an OAR dose violation within the alert pane 504. In some examples, each risk warning is associated with a warning identification number (e.g., warning ID). For example, the exceeded dose threshold in FIG. 5B may be associated with the warning identification number 0x0001, while the OAR dose violation in FIG. 5C may be associated with the warning identification number 0x0100. The warning identification number identifies the specific constraint that has been violated. The treatment application calculation device 108 may display the warning identification number corresponding to each warning.

[0057] Referring to FIG. 1, in some embodiments, a medical expert such as a dosimetrist may operate the treatment planning calculation device 106 to formulate an initial treatment plan for a patient. In the case of cardiac radioablation, an electrophysiologist may also be involved in formulating the treatment plan. The initial treatment plan is determined based on the TP images acquired for the patient and can identify the initial treatment target region and the expected dose levels (e.g., DVH) within the initial treatment target region. Thereafter, the patient's treatment schedule is arranged. On the day of treatment, before any dose is applied, the patient is scanned to generate image data 103 that characterizes the TD image having a field of view corresponding to the treatment region. Such a scan may be performed by a linear accelerator integrated kV-cone beam CT or any other type of scanner near or integrated with the treatment device 110. The current anatomical arrangement of the patient's organs, such as the organs in the patient's chest region, is captured within the TD image. The treatment application calculation device 108 can apply a transformation model to the image and the TD image to generate output data that characterizes the anatomical changes between the images. Based on the output data and the initial treatment target region, the treatment application calculation device 108 determines the current treatment target region within the current image. Further, the treatment application calculation device 108 determines the expected dose levels within the current target region and determines whether the current treatment target region includes a risk organ (i.e., OAR).

[0058] In some examples, the treatment application calculation device 108 displays a comparison of the TP treatment target region for the TP image and the TD treatment target region for the TD image, including any calculated dose levels. For example, the treatment application calculation device 108 may display the expected dose levels for any organs to be treated, including any organs and any OARs. Further, based on the determined dose levels for any OARs, the treatment application calculation device 108 can determine whether any constraints are violated (e.g., whether a specific dose threshold for the OAR is exceeded). If the treatment application calculation device 108 determines that a constraint is violated, the treatment application calculation device 108 can provide an alert, such as an audible or visual alert. In some examples, the treatment application calculation device 108 sends a message, such as an email or SMS message identifying the warning, to one or more predetermined recipients.

[0059] Figure 3 shows an exemplary portion of the treatment application calculation device 108. In this example, the treatment application calculation device 108 includes an image reconstruction engine 302, a treatment day (TD) target determination engine 304, a treatment plan (TP) target reconstruction engine 306, and a treatment risk determination engine 308. In some examples, one or more of the image reconstruction engine 302, the treatment day (TD) target determination engine 304, the treatment plan (TP) target reconstruction engine 306, and the treatment risk determination engine 308 may be implemented in hardware. In some examples, one or more of the image reconstruction engine 302, the treatment day (TD) target determination engine 304, the treatment plan (TP) target reconstruction engine 306, and the treatment risk determination engine 308 may be implemented as an executable program held in a tangible and non-transitory memory, such as the instruction memory 207 of FIG. 2, executable by one or more processors, such as the processor 201 of FIG. 2.

[0060] In this example, the database 116 stores TP image data 315, TP target definition data 317, risk determination data 319, and other data. The TP image data 315 characterizes the TP images taken of the patient for treatment planning. The TP target definition data 317 characterizes treatment regions such as the TP treatment target region 404 and the expected dose levels within the treatment region for the corresponding initial image, and may further characterize one or more OAR regions such as the TP OAR region 402. Further, the risk determination data 319 can characterize risk data such as constraints related to the risk for the treatment region (e.g., OAR dose constraints and DVH dose constraints, rules).

[0061] The TP planned target reconstruction engine 306 obtains the TP image data 315 and the TP target definition data 317 from the database 116. Based on the TP target definition data 317 and the TP image data 315, the TP planned target reconstruction engine 306 generates TP target data 307 that characterizes a TP image including the TP treatment target region and one or more TP OAR regions. For example, the TP planned target reconstruction engine 306 may generate target region image data indicating the TP treatment target region based on the TP target definition data 317, and may further generate TP OAR image data indicating the TP OAR region based on the TP target definition data 317. The TP planned target reconstruction engine 306 may overlay the target region image data and the TP OAR image data on the TP image characterized by the TP image data 315, construct a final TP output image, and generate TP target data 307 that characterizes the final TP output image.

[0062] Further, on the treatment day, the patient is scanned, for example, using imager 355 (for example, a linear accelerator integrated with kV-cone beam CT), and treatment-day image data 301 is acquired. Image reconstruction engine 302 can acquire the patient's image data 301 and reconstruct an image based on the acquired image data 301. In some examples, the reconstructed image may be a three-dimensional image of one or more organs of the patient. Image reconstruction engine 302 generates image reconstruction data 303 characterizing the reconstructed image and provides the image reconstruction data 303 to target determination engine 304.

[0063] The TD target determination engine 304 receives the image reconstruction data 303 from the image reconstruction engine 302, and further obtains the TP target definition data 317 from the database 116. Also, the TD target determination engine 304 receives the TP target data 307 from the TP planned target reconstruction engine 306. Based on the TP target data 307, the TP target definition data 317, and the image reconstruction data 303, the TD target determination engine 304 generates the TD target data 305 that characterizes the TD image including the current treatment target area and one or more TD OAR areas. For example, the TD target determination engine 304 can determine the mapping between the image reconstruction data 303 and the TP target data 307 (e.g., based on identifying corresponding structures and applying a conversion model as described herein). Further, based on the TP treatment target area characterized by the TP target data 307 and the determined mapping, the TD target determination engine 304 generates the TD target image data that characterizes the TD treatment target area, and in some embodiments, the TD OAR image data. In some embodiments, the TP planned target reconstruction engine 306 receives input from a user (e.g., a medical professional) who identifies and characterizes the TD OAR area as described herein, and generates the TD OAR image data based on the received input. The TD target determination engine 304 may overlay the TD target image data and the TD OAR image data on the TD image characterized by the image reconstruction data 303, construct the final TD output image, and generate the TD target data 305 that characterizes the final TD output image.

[0064] The treatment risk determination engine 308 receives TD target data 305 from the TD target determination engine 304, receives TP target data 307 from the TP plan target reconstruction engine 306, and further obtains risk determination data 319 from the database 116. The treatment risk determination engine 308 also obtains risk determination data 319 from the database 116 and generates risk warning data 309 that characterizes one or more risk warnings based on the TD target data 305, the TP target data 307, and the risk determination data 319. For example, the risk determination data 319 can characterize one or more DVH dose constraints. The treatment risk determination engine 308 determines the relative position of the OAR in the TP image characterized by the TP target data 307 and the relative position of the OAR in the current image characterized by the TD target data 305, and may calculate one or more dose volume histograms (DVHs) based on the relative position. Further, the treatment risk determination engine 308 can determine whether there is a violation of one or more of the DVH dose constraints based on the DVH. For example, the treatment risk determination engine 308 can determine that there is a violation of the DVH dose constraint if there is a portion of the OAR that is at risk of receiving a dose exceeding a predetermined threshold characterized by the DVH dose constraint when the radiation dose is applied to the target area.

[0065] In other examples, the risk determination data 319 characterizes one or more OAR dose constraints. For example, each OAR dose constraint may include a threshold value. In some examples, the treatment risk determination engine 308 determines the relative position between an organ such as an OAR in the TP image and the current image, and determines that the OAR dose constraint is violated if the organ has moved more than a predetermined distance characterized by the OAR dose constraint. In some embodiments, the treatment risk determination engine 308 may determine the positional relationship (e.g., distance) between the target region and the OAR in the TP image and the positional relationship between the same OAR and the target region in the TD image. The treatment risk determination engine 308 can determine that the OAR dose constraint is violated based on the relative positions determined for each of the TP image and the TD image. For example, the treatment risk determination engine 308 compares (e.g., subtracts) the determined relative positions, and determines that the OAR dose constraint is violated if the difference is less than or exceeds a predetermined threshold characterized by the OAR dose constraint.

[0066] In some examples, the treatment risk determination engine 308 determines that the OAR dose constraint is violated based on determining an overlapping region between the current target region and the current OAR region. For example, it is the overlapping region 530 such as the overlapping region between the TD target region 522 and the TD OAR region 524. For example, the treatment risk determination engine 308 may determine that the dose corresponding to the overlapping region exceeds a predetermined threshold characterized by the OAR dose constraint. In some examples, the treatment risk determination engine 308 determines that the OAR dose constraint is violated if the overlapping region includes at least a predetermined percentage (e.g., 10%) of the current OAR region.

[0067] If a constraint (e.g., a rule) is violated, the treatment risk determination engine 308 may generate risk warning data 309 that characterizes one or more risk warnings. For example, the risk warning data 309 can identify one or more of a constraint identification value (e.g., a constraint ID), a TI image (e.g., a corresponding TI image ID) characterized by the TP target data 307, and a TD image (e.g., a corresponding TD image ID) characterized by the TD target data 305. The treatment risk determination engine 308 may store the risk warning data 309 in the database 116. In some examples, the treatment risk determination engine 308 provides the risk warning data 309 to the display 206 for display.

[0068] The treatment risk determination engine 308 may further generate overlay image data 311 that characterizes an OAR region and a target region overlaid on an image such as a TP image or a TD image based on the TD target data 305 and the TP target data 307. For example, the overlay image data 311 may include a TD target region such as the TD target region 522 overlaid on a TD image such as the TD image 520, and a TD OAR region such as the TD OAR region 524. In some examples, the treatment risk determination engine 308 may generate overlay image data 311 that includes a TD target region and a TD OAR region overlaid on a TI image. In other examples, the treatment risk determination engine 308 may generate overlay image data 311 that includes a TD image overlaid on a TI image. The treatment risk determination engine 308 may store the overlay image data 311 in the database 116. In some examples, the treatment risk determination engine 308 provides the overlay image data 311 to the display 206 for display.

[0069] Figure 6 shows a flowchart of an exemplary method 600 that can be implemented, for example, by treatment application calculation device 108. Starting from step 602, treatment application calculation device 108 receives a first image of a patient (e.g., a TP image) acquired prior to the treatment plan. This image may be, for example, a CT image. This image may be stored in a database such as database 116. On the day of treatment, treatment application calculation device 108 may retrieve the image (e.g., TP image data 315) from database 116. Further, at step 604, treatment application calculation device 108 may receive target definition data characterizing the target region of the patient to be treated. For example, treatment application calculation device 108 may retrieve TP target definition data 317 corresponding to the acquired image.

[0070] Proceeding to step 606, treatment application calculation device 108 may receive a second image of the patient (e.g., a TD image). This image may be, for example, a cone beam CT image. At step 608, treatment application calculation device 108 determines whether there is a risk to the patient's organs based on the first image, the target definition data, and the second image. For example, as described herein, treatment application calculation device 108 can determine whether constraints such as OAR dose constraints or DVH dose constraints are violated based on the first image, the target definition data, and the second image.

[0071] If a risk is determined in step 610, the method proceeds to step 612, and a risk warning is displayed based on the determined risk. For example, the treatment application calculation device 108 may generate risk warning data 309 based on one or more determined risks (e.g., constraint violations), and provide at least a portion of the risk warning data 309 for display, such as on the display 206. For example, the treatment application calculation device 108 may display an alert such as "DVH dose violation" or "OAR dose violation" within the alert pane 504. However, if no risk is determined in step 610, the method proceeds to step 614, and the treatment application calculation device 108 displays a no-risk indication. For example, the treatment application calculation device 108 may display an indication of "no risk" within the alert pane 504. Thereafter, the method ends.

[0072] FIG. 7 is a flowchart of an exemplary method 700 that can be implemented, for example, by the treatment application calculation device 108. Starting from step 702, the treatment application calculation device 108 receives a first image of the patient taken during treatment planning. For example, prior to the day of treatment, the patient lies on the table of the imaging scanning device 102, and a TP image of the patient is taken. The TP image may be, for example, a CT image. The TP image may be stored in a database such as the database 116. On the day of treatment, the treatment application calculation device 108 may retrieve the image (e.g., TP image data 315) from the database 116. In step 704, the treatment application calculation device 108 displays an indication of a first risk organ region (e.g., a TP OAR region) and a first target region (e.g., a TP target region) overlaid on the first image. For example, as described herein, the treatment application calculation device 108 may display the TP OAR region 402 and the TP treatment target region 404 overlaid on the TP image 400.

[0073] Proceed to step 706, where the treatment application calculation device 108 receives a second image of the patient. For example, on the day of treatment, the patient lies on the treatment table 110 and is scanned to obtain a TD image. The patient may be scanned by the onboard imager of the device 110 such that a cone beam CT image is obtained. In step 708, the treatment application calculation device 108 determines a second OAR region (e.g., TD OAR region) within the second image. For example, the treatment application calculation device 108 may display the second image and further receive input from a medical expert to characterize the second OAR region (e.g., the medical expert may "draw" the second OAR region on a user interface that displays the second image). Alternatively, the contour may be automatically drawn using a known calculation algorithm.

[0074] In step 710, the treatment application calculation device 108 determines a second target region (e.g., TD target region) within the second image based on the first target region, the first OAR region, and the second OAR region. For example, as described herein, the treatment application calculation device 108 can apply a transformation model to the first image and the second image to generate output data that characterizes the anatomical changes between the images. Based on the output data and the first target region, the treatment application calculation device 108 can determine the second target region within the second image.

[0075] Proceed to step 712, and the treatment application calculation device 108 overlays and displays the display of the second OAR region and the second target region on the second image. For example, the treatment application calculation device 108 may generate overlay image data 311 by overlaying a second target region (e.g., TD target region 522) and a second OAR region (e.g., TD OAR region 524) on a second image (e.g., TD image 520), respectively. The treatment application calculation device 108 can provide the overlay image data 311 to the display 206 for display and cause it to be displayed. Then, this method ends.

[0076] In some embodiments, the system includes a database and a computing device communicatively coupled to the database. The computing device is configured to receive a first image (e.g., TP image) of a patient. The computing device is also configured to receive target definition data characterizing a target region of the patient for treatment. Further, the computing device is configured to receive a second image (e.g., TD image) of the patient. The computing device is also configured to determine whether there is a risk to the patient's organ based on the first image, the target definition data, and the second image. The computing device is also configured to provide a risk display for display based on the determination.

[0077] In some examples, the first image is acquired before the second image, and the second image is acquired on the treatment day. In some embodiments, the target definition data characterizes a first risk organ region and a first treatment target region. In some embodiments, the arithmetic processing unit is configured to determine a second treatment target region based on the first risk organ region, the first treatment target region, and the second risk organ region. The second risk organ region may include the same organ as the first risk organ region.

[0078] In some embodiments, the computing device is configured to display a first image and receive input data characterizing a second risk organ region. In some embodiments, the computing device is configured to determine structures in the first image and the second image. The computing device is configured to determine a mapping between the first image and the second image based on the structures. Further, the computing device is configured to determine a second treatment target region based on the mapping.

[0079] In some embodiments, the computing device is configured to determine an overlapping region between the second treatment target region and the second risk organ region. The computing device is configured to determine a risk to the patient's organ based on the overlapping region. In some embodiments, the target definition data characterizes a first dose level within the first treatment target region. In some embodiments, the computing device is configured to determine a second dose level within the overlapping region and determine a risk to the patient's organ based on the second dose level.

[0080] In some embodiments, the computing device is configured to overlay the second treatment target region and the second risk organ region on the second image. In some embodiments, the display of the risk consists of overlaying the second treatment target region and the second risk organ region on the second image.

[0081] In some embodiments, a computer-implemented method includes receiving a first image (e.g., a TP image) of a patient. The computer-implemented method also includes receiving target definition data characterizing a target region of the patient for treatment. Further, the computer-implemented method includes receiving a second image (e.g., a TD image) of the patient. The computer-implemented method also includes determining whether there is a risk to the patient's organ based on the first image, the target definition data, and the second image. The computer-implemented method further includes providing to display a display of the risk based on the determination.

[0082] In some embodiments, the first image is acquired before the second image, and the second image is acquired on the treatment day. In some embodiments, the target definition data characterizes a first risk organ region and a first treatment target region. In some embodiments, the method further includes determining a second treatment target region based on the first risk organ region, the first treatment target region, and a second risk organ region.

[0083] In some embodiments, a computer-implemented method includes displaying a first image and receiving input data characterizing a second risk organ region. In some embodiments, a computer-implemented method includes determining structures within the first image and the second image. The method also includes determining a mapping between the first image and the second image based on the structures. Further, the method includes determining a second treatment target region based on the mapping.

[0084] In some embodiments, a computer-implemented method includes determining an overlapping region between the second treatment target region and the second risk organ region. The computer-implemented method also includes determining a risk to a patient's organ based on the overlapping region.

[0085] In some embodiments, the target definition data characterizes a first dose level within the first treatment target region. In some embodiments, the method includes determining a second dose level within the overlapping region. In some embodiments, the method includes determining a risk to a patient's organ based on the second dose level.

[0086] In some embodiments, a computer-implemented method includes overlaying the second treatment target region and the second risk organ region on the second image. In some embodiments, a display of the risk includes an overlay of the second treatment target region and the second risk organ region on the second image.

[0087] In some embodiments, the non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations including receiving a first image (e.g., a TP image) of a patient. This operation also includes receiving target definition data characterizing a target region of the patient for treatment. Further, this operation includes receiving a second image (e.g., a TD image) of the patient. This operation also includes determining whether there is a risk to the patient's organ based on the first image, the target definition data, and the second image. Further, the operation includes receiving a second image (e.g., a TD image) of the patient. The operation also includes determining whether there is a risk to the patient's organ based on the first image, the target definition data, and the second image. The operation further includes providing a display of the risk for display based on the determination.

[0088] In some embodiments, the first image is acquired before the second image, and the second image is acquired on the treatment day. In some embodiments, the target definition data characterizes a first risk organ region and a first treatment target region. In some embodiments, the operation further includes determining a second treatment target region based on the first risk organ region, the first treatment target region, and the second risk organ region.

[0089] In some embodiments, the operation includes displaying the first image and receiving input data characterizing the second risk organ region. In some embodiments, the operation includes determining structures in the first image and the second image. The operation also includes determining a mapping between the first image and the second image based on the structures. Further, the operation includes determining the second treatment target region based on the mapping.

[0090] In some examples, the operation includes determining an overlapping region between a second treatment target region and a second risk organ region. The operation also includes determining a risk to the patient's organ based on the overlapping region.

[0091] In some examples, the target definition data characterizes a first dose level within a first treatment target region. In some examples, the operation includes determining a second dose level within the overlapping region. In some examples, the operation includes determining a risk to the patient's organ based on the second dose level.

[0092] In some examples, the operation includes overlaying a second treatment target region and a second risk organ region on a second image. In some examples, the risk display includes the second treatment target region and the second risk organ region overlaid on the second image.

[0093] In some embodiments, the system includes means for receiving a first image of a patient. The system also includes means for receiving target definition data characterizing a target region of the patient for treatment. Further, the system includes means for receiving a second image of the patient. Also, the system includes means for determining whether there is a risk to the patient's organ based on the first image, the target definition data, and the second image. Further, the system includes means for displaying a risk display on a display based on the determination.

[0094] In some embodiments, the first image is acquired before the second image, and the second image is acquired on the treatment day. In some embodiments, the target definition data characterizes a first risk organ region and a first treatment target region. In some embodiments, the system includes means for determining a second treatment target region based on the first risk organ region, the first treatment target region, and the second risk organ region.

[0095] In some embodiments, the system includes means for displaying a first image and receiving input data characterizing a second risk organ region. In some embodiments, the system includes means for determining structures within the first image and the second image. The system also includes means for determining a mapping between the first image and the second image based on the structures. Further, the system includes means for determining a second treatment target region based on the mapping.

[0096] In some embodiments, the system includes means for determining an overlapping region between the second treatment target region and the second risk organ region. The system also includes means for determining the risk to the patient's organ based on the overlapping region.

[0097] In some embodiments, the target definition data characterizes a first dose level within the first treatment target region. In some embodiments, the system includes means for determining a second dose level within the overlapping region. In some embodiments, the system includes means for determining the risk to the patient's organ based on the second dose level.

[0098] In some embodiments, the system includes means for overlaying the second treatment target region and the second risk organ region on the second image. In some embodiments, the display of the risk consists of overlaying the second treatment target region and the second risk organ region on the second image.

[0099] The method described above refers to the illustrated flowchart, but it will be understood that many other methods for performing the acts associated with the method can be used. For example, the order of some operations may be changed and some of the operations described may be optional.

[0100] Furthermore, the methods and systems described herein can be embodied, at least in part, in the form of computer-implemented processes and apparatuses for carrying out those processes. Also, the disclosed methods can be embodied, at least in part, in the form of a tangible, non-transitory machine-readable storage medium encoded with computer program code. For example, the steps of the method can be embodied in hardware, executable instructions executed by a processor (e.g., software), or a combination of both. Such storage media include, for example, RAM, ROM, CD-ROM, DVD-ROM, BD-ROM, hard disk drives, flash memory, or other non-transitory machine-readable storage media. When the computer program code is loaded and executed on a computer, that computer becomes an apparatus for carrying out the method. Also, the method can be embodied, at least in part, in the form of a computer on which the computer program code is loaded or executed, in which case the computer becomes a dedicated computer for carrying out the method. When implemented on a general-purpose processor, segments of the computer program code configure the processor to create specific logic circuits. The method can also be embodied, at least in part, in an application-specific integrated circuit for carrying out the method.

[0101] The foregoing is provided for the purpose of exemplifying, explaining, and describing embodiments of these disclosures. Modifications and adaptations to these embodiments will be 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: a database; and a computing device communicatively coupled to the database, wherein the computing device is configured to: receive a first image of a patient; receive target definition data characterizing a target region of the patient to be treated; receive a second image of the patient; determine whether there is a risk to an organ of the patient based on the first image, the target definition data, and the second image; and provide a risk indication for display based on the determination.

2. The system of claim 1, wherein the first image is taken prior to the second image, and the second image is taken on a treatment day.

3. The target definition data characterizes a first organ risk region and a first treatment target region, and the computing device is configured to determine a second treatment target region based on the first organ risk region, the first treatment target region, and a second organ risk region. The system of claim 1 or 2.

4. The computing device is configured to: provide the first image for display; and receive input data characterizing the second organ risk region. The system of claim 3.

5. The computing device is configured to: determine structures in the first image and the second image; determine a mapping between the first image and the second image based on the structures; and determine the second treatment target region based on the mapping. The system of claim 3 or 4.

6. The computing device is configured to: determine an overlap range between the second treatment target region and the second organ risk region; and determine the risk to the organ of the patient based on the overlap range. The system of claim 3 or 4.

7. The target definition data characterizes a first dose level within the first treatment target region, and the computing device is configured to: determine a second dose level within the overlap range; and determine the risk to the organ of the patient based on the second dose level. The system of claim 6.

8. ​ The computing device superimposes the second treatment target region and the second organ risk region on the second image, and the sign of the risk is configured to consist of the second treatment target region and the second organ risk region superimposed on the second image. The system according to claim 3 or 4.

9. A computer-implemented method comprising: Receiving a first image of a patient; Receiving target definition data characterizing a target region for treatment of the patient; Receiving a second image of the patient; Determining whether there is a risk to an organ of the patient based on the first image, the target definition data, and the second image; Providing a sign of the risk for display based on the determination. A method including this.

10. The computer-implemented method according to claim 9, wherein the first image is taken earlier than the second image, and the second image is taken on the day of treatment.

11. The target definition data characterizes a first organ risk region and a first treatment target region, and the method further determines a second treatment target region based on the first organ risk region, the first treatment target region, and a second organ risk region. The computer-implemented method according to claim 9 or 10, further including this.

12. Providing the first image for display; Receiving input data characterizing the second organ risk region. The computer-implemented method according to claim 11, further including this. The system according to claim 3.

13. Determining structures in the first image and the second image; Determining a mapping between the first image and the second image based on the structures; Determining the second treatment target region based on the mapping. The computer-implemented method according to claim 11 or 12, further including this.

14. Determining an overlapping range of the second treatment target region and the second organ risk region; Determining the risk to the organ of the patient based on the overlapping range. The computer-implemented method according to claim 11 or 12, further including this.

15. The target definition data characterizes a first dose level within the first treatment target region, and the method further: Determining a second dose level within the overlapping range. Determining the risk to the organ of the patient based on the second dose level The computer-implemented method according to claim 14, further comprising this.

16. Superimposing the second treatment target area and the second organ risk area on the second image, and the sign of the risk includes the second treatment target area and the second organ risk area superimposed on the second image. The computer-implemented method according to claim 11 or 12, further comprising this.

17. A non-transitory computer-readable medium, when executed by at least one processor, receiving a first image of a patient, receiving target definition data characterizing a target area for treatment of the patient, receiving a second image of the patient, determining whether there is a risk to the organs of the patient based on the first image, the target definition data, and the second image, providing a sign of the risk for display based on the determination, A non-transitory computer-readable medium storing instructions for causing the at least one processor to execute an operation including this.

18. The first image is taken earlier than the second image, and the second image is taken on the day of treatment. The non-transitory computer-readable medium according to claim 17.

19. The target definition data characterizes a first organ risk area and a first treatment target area, and the operation further includes determining a second treatment target area based on the first organ risk area, the first treatment target area, and a second organ risk area. The non-transitory computer-readable medium according to claim 17 or 18.

20. The operation is providing the first image for display, receiving input data characterizing the second organ risk area, The non-transitory computer-readable medium according to claim 19, further comprising this.

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