A method for quantifying patient setup errors in radiation therapy.

Abstract

A method and system for determining changes between planning and treatment images of a subject is described, the method comprising the steps of: defining one or more clinical volumes on a planning image of a subject and defining a planning envelope volume around the clinical volumes for the planning image, acquiring treatment images from the subject for locations corresponding to locations of the planning image, the treatment images having the same planning envelope volume as the planning image, determining locations of the one or more clinical volumes on the treatment images relative to the planning envelope volume, and determining an inclusion metric for one or more of the one or more clinical volumes defining a degree of inclusion of the clinical volume on the treatment image within the planning envelope volume on the treatment image.

Description

Technical Field

[0001] The present invention relates to the fields of medical imaging and medical image processing, and in particular to identifying changes in medical images due to changes in the anatomical position of a patient when a scan is acquired, and more particularly to the field of radiotherapy.

Background Art

[0002] During radiotherapy, a planning medical image is acquired of the patient, which is most commonly Computed Tomography (CT), although Magnetic Resonance (MR) is increasing. From this medical image, a plan is created for use in a number of radiotherapy sessions. These treatment sessions are known as multiple fractions. One of the multiple problems with this approach is that the planning image is not an accurate representation of the patient's anatomical structure at each treatment session. The position of the patient's anatomical structure varies randomly for each session relative to its appearance on the planning image. There are also systematic changes in the patient's anatomical structure that can occur over time due to weight loss, tumor shrinkage, or other such processes. Image Guided Radiotherapy (IGRT) and Adaptive Radiotherapy (ART) are techniques that use imaging throughout the course of treatment to monitor anatomical changes and, in the case of IGRT, to optimize the position of the patient or, in the case of ART, to modify the treatment plan.

[0003] To generate radiotherapy robust to variations in patient position, margins are applied to several specific regions identified on the planning image. The oncologist identifies the Clinical Target Volume (CTV) as the region that may contain the disease to be treated with radiation. Margins are applied to the CTV to create the Planning Target Volume (PTV). The PTV is the region to which radiation is applied to ensure that the CTV receives the required radiation dose. The PTV is a spatial envelope encompassing all possible positions of the CTV due to variations in anatomical position. Similar definitions exist for sensitive healthy organs that should be avoided. Organs at Risk (OARs) have margins applied to generate the Planning Risk Volume (PRV). These concepts were formalized in ICRU Reports 50, 62 (Non-Patent Literature 1, 2). Since this invention applies equally to both the target area and the healthy area, these definitions are grouped together, with CTV and OAR grouped together as Clinical Volumes (CV), and PTV and PRV grouped together as Planning Envelope Volumes (PEV). PEV is most often explicitly defined during radiotherapy planning by the contour drawn during planning, but it can also be implicitly defined, such as CV plus a 10 mm margin.

[0004] In IGRT and ART, a critical decision is whether the central venous region (CV) is still contained within its respective primary venous field (PEV). A central venous field (CTV) that moves outside the PTV increases the risk of being missed by radiotherapy. Similarly, an orthostatic radiating (OAR) that moves outside the primary venous field (PRV) increases the risk of moving into a high-dose area. If the CV extends outside the PEV, the assumptions of the original plan become invalid, and the patient's position must be shifted to bring the CV back into the PEV, or a new plan should be considered. In scenarios where the user wishes to perform positional correction, most radiotherapy equipment allows for positional correction through automated linear movement of the patient couch. More advanced systems allow for automatic correction of the patient's angle and linear shift.

[0005] Figure 1 shows a flowchart of an exemplary current process 100 for IGRT / ART. This process has two stages. The planning stage 101 is performed before the first radiotherapy. In step 103, one or more clinical volumes (CVs) are identified on a planning 3D image, such as a CT scan. Some CVs correlate precisely with organs visible in the scan, while others, such as clinical target volumes that identify areas at risk of cancerous disease, may not. In step 104, PEVs are generated around one or more CVs to account for possible movement of the CVs during treatment. The number of PEVs may differ from the number of CVs, for example, because PEVs are not generated for all CVs, or because multiple CVs may be combined into a single PEV. Then, in step 105, using the PEVs from step 104, a radiotherapy plan is created to optimize the dose delivered to the therapeutic target while keeping the dose to the organ at risk below a set threshold.

[0006] Treatment phase 102 is performed over one or more treatment phases. Step 106 is performed for each treatment fraction, or selected treatment fraction, in which an imaging system integrated into the treatment machine acquires an image before the treatment is performed, typically up to 15 minutes before the start of the treatment, during which time the patient remains in the treatment position. In step 107, the user visually compares this image with the planning image to verify that each critical CV from step 104 is still contained within the relevant PEV. Typically, this is done on a dedicated imaging workstation that allows for various different methods of image comparison. Based on this evaluation, the user decides in step 120 whether to proceed with the treatment, possibly with corrections for patient position.

[0007] Conventional technologies, most IGRT and ART systems, only provide tools for manual, subjective assessment of whether a central venous cavity (CV) is contained within a primary vein (PEV). A few systems that offer automated assessments designed for real-time assessment of a patient's anatomical structure provide, for example, a Boolean yes / no indicator that shows green if the CV is in the PEV and red otherwise (Non-Patent Literature 3). The problem with such indicators is that they do not provide a means to quantify changes and track the potential progression of the problem over time.

[0008] Several systems attempt to use quantitative measures to track changes over time. Wang et al. (Non-Patent Literature 4) tracked changes in structural volume and arbitrary shifts in the structural center position using Varian's Velocity "Adaptive Monitoring Navigator." While these metrics are useful when the tracked CV is a rigid body, in modern radiotherapy, some CVs are very complex and do not simply move. When tracking the movement of the center of a CV, multiple portions of the CV moving outside of their PEV may be missed.

[0009] A review of academic research revealed numerous other papers reporting frameworks and metrics for monitoring patient changes during radiotherapy. In particular, Brouwer et al. (Non-Patent Literature 5) conducted a systematic review of several papers on techniques for ART in head and neck cancer. The anatomical change metrics they identified from 51 studies were body weight, body thickness, CV volume, CV density, CV location, CV angle, and variations of these (e.g., change, rate of change, or changes occurring at specific anatomical points). As mentioned above, in complex CVs, these metrics may miss multiple portions of the CV moving outside their PEV.

[0010] Several studies (Non-Patent Documents 6-8) have reported more complex metrics of anatomical changes based on either direct changes in patient images or structural changes identified on patient images.

[0011] Schaly et al. (Non-Patent Literature 6) investigated differences in gamma indices between multiple CT scans. The gamma index is a method developed for comparing two three-dimensional dose distributions (Non-Patent Literature 9). It is susceptible to both changes in voxel intensity and the distance to coincidence, which is the distance between a voxel in the reference image and a voxel of the same intensity in the test image. While the gamma index is indeed susceptible to changes in the location of the CV, as a global image-based metric, it does not explicitly measure the validity of the PEV encompassing the CV, and a low gamma index score is susceptible to anatomical changes at any location within that image.

[0012] Fiorino et al. (Non-Patent Literature 7) reported on the use of Jacobian volume histograms to quantify changes in deformable organs. In this method, a deformable registration is applied between a reference image and a test image. The Jacobian matrix of the deformable vector field is then calculated, and multiple values ​​within the CV region are plotted in a histogram. This provides a measure of voxel expansion and contraction across the entire CV. This has the advantage over Schaly's method of focusing on a specific CV rather than the entire volume. However, this does not directly address the issue of the validity of the PEV encompassing the CV. Even if a change occurs within the CV and that CV does not expand beyond the PEV, it will still result in a large change in the Jacobian.

[0013] Hargrave et al. (Non-Patent Literature 8) report an approach that is closest to the present invention. This measurement uses the Hausdorff distance between the original CV and the CV under treatment as a metric. The Hausdorff distance is a measure of the worst-case difference between two sets of contours. While this would find significant changes in complex CVs, it is not explicitly linked to the clinically important issue of whether the CV moves outside the PEV. Further contraction and movement of the CV within the PEV is recorded as a change, not a clinical problem. This lack of directionality in the metric makes its use more difficult when tracking changes. [Prior art documents] [Non-patent literature]

[0014] [Non-Patent Document 1] ICRU Report 50: Prescribing, Recording and Reporting Photon Beam Therapy: Journal of the ICRU, Vol. 26, No. 1, September 1993. [Non-Patent Document 2] ICRU Report 62: Prescribing, Recording, and Reporting Photon Beam Therapy (Supplement to ICRU Report 50): Journal of the ICRU, Vol. 32, No. 1, November 1999. [Non-Patent Document 3] https: / / viewray.com / mri-guided-smart / [Non-Patent Document 4] Wang et al., Adaptive radiotherapy based on statistical process control for oropharyngeal cancer: J Appl Clin Med Phys 2020; 21:9:171-177 [Non-Patent Document 5] Brouwer et al. Identifying patients who may benefit from adaptive radiotherapy: Does the literature on anatomical and dosimetric changes in head and neck organs at risk during radiotherapy provide helpful information? Radiotherapy and Oncology 115 (2015) 285-294. [Non-Patent Document 6] Schaly et al., Alert system for monitoring changes in patient anatomy during radiation therapy of head and neck cancer. J Appl Clin Med Phys. 2021; 22: 168-174. [Non-Patent Document 7] Fiorino et al. Introduction of the Jacobian-volume-histogram of deforming organs: application to parotid shrinkage evaluation, Phys. Med. Biol. 56 (2011) 3301-3312. [Non-Patent Document 8] Hargrave et al. A feature alignment score for online cone-beam CT-based image-guided radiotherapy for prostate cancer. Med Phys. 45 (7), July 2018 2898-2911. [Non-Patent Document 9] Low et al., A technique for the quantitative evaluation of dose distributions. Med Phys. 1998; 25: 656-661. [Overview of the project] [Problems that the invention aims to solve]

[0015] In other words, the present invention solves one or more of the following problems. To help identify changes in a patient's anatomical position, a simple, quantifiable metric is needed that readily correlates changes between images acquired over a period of time. Preferably, this information can be used for treatment planning when geometric changes in the patient are present, and these can be presented to the clinical user in a meaningful way. [Means for solving the problem]

[0016] A method is provided for determining the change between a planning image and a treatment image of a subject according to the present invention. The method comprises several steps: defining one or more clinical volumes on the planning image of the subject and defining a planning envelope volume around the clinical volumes with respect to the planning image; acquiring a treatment image from the subject at a position corresponding to the position of the planning image, wherein the treatment image has the same planning envelope volume as the planning image; determining the position of the one or more clinical volumes on the treatment image relative to the planning envelope volume; and determining an inclusion metric for one or more of the one or more clinical volumes that defines the degree of inclusion of the clinical volumes on the treatment image within the planning envelope volume on the treatment image.

[0017] In one embodiment of the present invention, determining the inclusion metric comprises several steps: designating one or more representative points on the surface of the clinical volume; and determining the shortest distance of the clinical volume to the one or more representative points with respect to the design envelope volume, wherein if the representative points on the clinical volume are within the design envelope volume, the shortest distance is classified as either a positive or negative internal distance; if the representative points on the clinical volume are outside the design envelope volume, the shortest distance is an external distance classified with the opposite sign to the internal distance; and the value of the inclusion metric is determined as either the minimum value of the signed shortest distance when the internal distance is classified as positive, or the maximum value of the signed shortest distance when the internal distance is classified as negative, among the distances to the one or more representative points.

[0018] In a preferred embodiment of the present invention, the planning image and the multiple treatment images are 3D images. More preferably, the planning image and treatment images are CT, PET, SPECT, or MRI images.

[0019] In a preferred embodiment of the present invention, after the planned envelope volume is defined, a distance transform is calculated around the planned envelope volume, which is used to determine the inclusion metric.

[0020] More preferably, the inclusion metric is determined by simulating the translation of one or more treatment clinical volumes relative to the planned envelope volume. In one embodiment of the present invention, the simulated translation is a linear translation.

[0021] In a preferred embodiment of the present invention, determining the position of the one or more clinical volumes on the treatment image relative to the planned envelope volume includes determining the geometric relationship between the planning image and the treatment image, mapping the position of the clinical volume throughout the treatment image, and identifying a plurality of anatomical features on the treatment image to specify the position of the clinical volume, including one or more of these.

[0022] Preferably, the planned envelope volume is defined as the clinical volume plus a margin specified by a preset protocol. In one embodiment of the present invention, the clinical volume is the clinical target volume or the total tumor volume for the treatment plan. In an alternative embodiment of the present invention, the clinical volume represents an organ at risk to be avoided in the treatment.

[0023] In a preferred embodiment, the plurality of steps are repeated over a set period to monitor the change in the inclusion metric on the plurality of treatment images. Preferably, the set period varies between 6 hours and 3 months.

[0024] In an embodiment of the present invention, the change in the inclusion metric over time is displayed to the user. In a preferred embodiment of the present invention, the subject is positioned at a designated location within the scanner for the planning image and the treatment image, and the user of the system can adjust the subject's position according to one or more determined inclusion metrics.

[0025] Preferably, the change in the inclusion metric over time is displayed to the user, and the display further indicates when the inclusion metric exceeds a preset threshold indicating unsafe movement of the subject within the scanner.

[0026] Preferably, the method further comprises the step of displaying one or more determined inclusion metrics to a user of the system. In a preferred embodiment, the method further comprises the step of using the inclusion metric to determine a 3D representation showing a movement margin indicating how a subject can be moved within the scanner, while maintaining or improving the position of the clinical volume relative to the position of the planned envelope volume.

[0027] Preferably, the clinical volume is displayed as a 3D-rendered surface, and the inclusion metric for the surface is shown as a heatmap on the surface. In a further embodiment of the present invention, a system is provided for analyzing medical images to determine changes between a planning image and a treatment image of a subject. The system comprises a processor configured to perform the following: determine one or more clinical volumes on a planning image of a subject; define a planning envelope volume around the clinical volumes for the planning image; analyze a treatment image from the subject for a position corresponding to the position of the planning image, wherein the treatment image has the same planning envelope volume as the planning image; determine the position of the one or more clinical volumes on the treatment image relative to the planning envelope volume; and determine an inclusion metric for one or more of the one or more clinical volumes that defines the degree of inclusion of the clinical volumes on the treatment image within the planning envelope volume on the treatment image.

[0028] Preferably, the processor is further configured to perform any of the steps of the method described above. In a preferred embodiment of the present invention, the system further comprises a display that shows at least one of the planning image, the therapeutic image, one or more clinical volumes, and the planning envelope volume.

[0029] In a further embodiment of the present invention, a computer program product comprising a plurality of instructions is also provided, which, when the program is executed by a computer, causes the computer to perform a plurality of steps of the method described above. [Brief explanation of the drawing]

[0030] Further details, aspects, and embodiments of the present invention will be described with reference to the drawings, merely as examples. In the drawings, similar reference numerals are used to identify similar or functionally similar elements. Elements in the figures are illustrated for brevity and clarity and are not necessarily drawn to scale. [Figure 1]Figure 1 shows a flowchart of known processes for image-guided radiotherapy / adaptive radiotherapy. [Figure 2] Figure 2 shows a flowchart of the method according to the first embodiment of the present invention. [Figure 3] Figure 3 shows a flowchart of a method according to a further embodiment of the present invention. [Figure 4a] Figure 4(a) shows the clinical volume and planning envelope volume on the planning image. [Figure 4b] Figure 4(b) shows the clinical volume and planned envelope volume on multiple therapeutic images. [Figure 4c] Figure 4(c) shows the clinical volume and planned envelope volume on multiple therapeutic images. [Figure 5a] Figure 5(a) shows the determination of the measurement metric based on the position of the clinical volume relative to the position of the planned envelope volume. [Figure 5b] Figure 5(b) shows the determination of the measurement metric based on the position of the clinical volume relative to the position of the planned envelope volume. [Figure 5c] Figure 5(c) shows the determination of the measurement metric based on the position of the clinical volume relative to the position of the planned envelope volume. [Figure 6a-b] Figures 6(a) and 6(b) show a method for calculating a measurement metric according to one embodiment of the present invention. [Figure 6c] Figure 6(c) shows a method for calculating a measurement metric according to one embodiment of the present invention. [Figure 7] Figure 7 is a plot of measurement metrics against time for embodiments of the present invention. [Figure 8a] Figure 8(a) is a rendered 3D view of a clinical volume according to one embodiment of the present invention. [Figure 8b] Figure 8(b) is a rendered 3D view of a clinical volume according to one embodiment of the present invention. [Figure 9] Figure 9 is a 3D view of the information regarding measurement metrics for clinical volume. [Figure 10]Figure 10 is a simplified block diagram of a medical imaging system configured to display medical images to a user. [Modes for carrying out the invention]

[0031] The present invention is a system for monitoring and reporting changes between multiple medical images acquired at different times. For example, these may be multiple images acquired during radiotherapy and planning. Specific possible applications include supporting decision-making in image-guided radiotherapy (IGRT) and adaptive radiotherapy (ART), but the invention is not limited to this application. Other possible applications include deformable registration quality control, disease progression tracking, or use in other image-guided medical interventions where clinical volume transitions across sequential images are required.

[0032] Figure 10 shows a simplified block diagram of an example of a medical image processing system 1000 configured to enable the display of medical images to a user. In the illustrated example, the medical image processing system 1000 includes one or more user terminals 1001, such as a workstation, which are configured to access medical images stored in, for example, a database 1002 or other data storage devices. In the illustrated example, a single database 1002 is shown. However, it will be recognized that the user terminal 1001 may be configured to access medical images from two or more data storage devices. Furthermore, in the illustrated example, the database 1002 is shown as being outside the user terminal 1001. However, it will be recognized that the user terminal 1001 may also be configured to access medical images stored locally in a local storage module, shown by 1010, on one or more internal storage elements, such as a memory element shown by 1003 or a disk element shown by 1009. The user terminal 1001 further includes one or more signal processing modules, such as a signal processing module shown overall by 1004. One or more signal processing modules are configured to execute computer program code stored, for example, in a local memory module 1009. In the illustrated example, one or more signal processing modules 1005 are configured to execute computer program code that includes one or more patient change monitoring components, which in this example are exemplified as metric calculation components 1005. In the illustrated example, signal processing module 1004 is further configured to execute computer program code that includes one or more image display components 1006, which are configured to display any metric of patient change, such as one or more metric calculation components 1005, to the user in a meaningful manner, for example, on a display screen 1007.The medical image processing system 1000 may further include one or more user input devices, as shown holistically in 1008, to enable the user to interact with computer program code, etc., running on one or more signal processing modules 1004.

[0033] Figure 2 shows the process shown in Figure 1, incorporating the present invention. With respect to Figure 1, this process has two stages: a planning stage 201 performed before the first radiotherapy, and a treatment stage 202. The treatment process is almost identical. Items 201-206 and 210 all correspond to the same items in Figure 100. In step 203 of the planning stage, one or more clinical volumes are identified on the medical planning image of the subject. In one example of the present invention, the planning image and multiple treatment images are 3D images. Preferably, the planning image and treatment images are CT, PET, SPECT, or MRI images.

[0034] Some clinical volumes, such as clinical target volumes that identify areas of cancer risk, correlate precisely with organs visible in the scan, while others may not. In step 204, multiple planning envelope volumes are generated around one or more clinical volumes to account for possible movement of clinical volumes during treatment. The number of planning envelope volumes may differ from the number of clinical volumes, for example, because planning envelope volumes are not generated for all clinical volumes, or because multiple clinical volumes may be combined into a single planning envelope volume. Then, in step 205, the planning envelope volumes from step 204 are used to create a radiotherapy plan that optimizes the dose delivered to the therapeutic target while keeping the dose to the organ at risk below a set threshold.

[0035] Treatment phase 202 is performed over one or more treatment phases. Step 206 is performed for each treatment fraction, or a selection of treatment fractions, during which an imaging system integrated into the treatment machine acquires a treatment image before the treatment is performed, typically this is acquired up to 15 minutes before the start of the treatment, during which time the patient remains in the treatment position. Preferably, the treatment image for the subject is acquired at a position corresponding to the position of the planning image, such that the treatment image has the same planning envelope volume as the planning image.

[0036] The present invention supports user evaluation of images such that step 107 in Figure 1 is replaced by steps 207-209 in Figure 2. In step 207, the locations of one or more clinical volumes on the therapeutic image are identified, preferably, determined relative to the location of the planning envelope volume. There are various ways to perform the task of step 207, and the present invention is not dependent on any particular method and can be performed in any technically feasible way. For example, it can be performed by determining the geometric relationship between the planning image and the therapeutic image and mapping the overall location of the volumes. Another method is to manually or automatically identify multiple volumes from anatomical structures on the therapeutic image to determine the location of the clinical volumes. In step 208, an encapsulation metric, which is further detailed below, is calculated between one or more clinical volumes on the therapeutic image and the associated planning envelope volume from the planning image. Preferably, the inclusion metric for one or more of the clinical volumes defines the degree of inclusion (encapsulation) of one or more clinical volumes on the therapeutic image within the planned envelope volume on the therapeutic image. This is shown to the user in step 209. This view can take several forms, incorporating any combination of patient images, clinical and planned envelope volumes, metrics for this therapeutic session, trends in previous sessions, a graphical representation of where problems were detected, metric values ​​organized by surface angles, or summary information in numerical or text format. Some of these elements are further illustrated in Figures 700, 800, and 900.

[0037] A further example of the method of the present invention is shown in Figure 3. Items 301-306 and 320 all correspond to the same items in Figure 1. As shown in Figure 2, in step 307, the location of one or more clinical volumes on the therapeutic image is determined, and in step 308, an inclusion metric is calculated between each of the one or more therapeutic clinical volumes and the associated design envelope volume. In step 309, a correction is determined for the location of the therapeutic image that best inclusions one or more clinical volumes within the design envelope volume and thus minimizes the inclusion metric. If there are multiple inclusion metrics to optimize, any standard method for managing multiple objectives in the optimization problem can be used, for example, by setting multiple priority rules by volume type, or by weighting separate metrics and summing them up to generate an overall score. In step 310, the individual inclusion metrics for this optimized therapeutic location are calculated. In step 311, the inclusion metrics for the original patient location are reported to the user along with the possible inclusion metric values ​​if the optimal correction is performed.

[0038] Figure 400 illustrates several concepts of clinical volume and planning envelope volume in planning and treatment situations. Figure 4(a) shows the situation in the planning image. A coordinate system is defined with O (item 402) as the origin. The position of O is determined during the planning process and can be any point on the treatment image, but typically corresponds to the isocenter of the radiotherapy device. The position of the clinical volume in the planning scan is indicated as A0 (item 403). The planning envelope volume is indicated as B (item 404), so as to completely encompass the clinical volume with margins. Figure 4(b) shows the situation in one of several treatment fractions (referred to as i). The time between multiple treatment fractions typically varies between 6 hours and 1 week. Since the treatment is defined relative to the machine geometry, the origin (item 406) and the position of the planning envelope volume (item 408) are invariant for each treatment fraction. However, since patients will generally not be seated or reclined within the machine geometry in exactly the same position as during the planning phase, the position of the clinical volume will change as indicated by Ai (item 407). In this example of the invention, the clinical volume has moved to the upper right quadrant relative to the axis, but still remains within the boundaries of the planned envelope volume. Figure 4(c) shows the situation in different treatment fractions of multiple treatment fractions. In this case as well, the origin (item 410) and the planned envelope volume (item 411) remain unchanged. The clinical volume (item 412) in this example has not only moved relative to the planned envelope volume, but has also deformed compared to its original shape (item 403) that it had during the planning phase. The combination of movement and shape change causes a portion of the clinical volume to move outside the space defined by the planned envelope volume, as indicated by item 413.

[0039] The inclusion metric used to quantify the extent to which a clinical volume lies within the relevant design envelope volume is defined as follows: For one or more points on the clinical volume, the shortest distance to the design envelope volume is determined. In one example of this invention, if a point on the clinical volume lies within the design envelope volume, this distance is assigned a positive sign. If a point on the clinical volume lies outside the design envelope volume, this distance is assigned a negative sign. The overall inclusion metric is then the minimum from one or more signed shortest distances.

[0040] An example of an inclusion metric is shown in Figure 5. Figure 5(a) shows a portion of the clinical volume (item 502) enclosed by a portion of the planned envelope volume (item 503). A representative point on the surface of the clinical volume is indicated by item 504. The continuous distance from the representative point 504 on the clinical volume to the planned envelope volume 503 is shown as arrows of different lengths (item 505). The shortest distance from the representative point 504 on the clinical volume to the planned envelope volume 503 is indicated by a thick arrow (item 506).

[0041] Figure 5(b) shows a case where the clinical volume 509 is still completely enclosed within the design envelope volume 508. One or more representative points are designated on the surface of the clinical volume. For each of the one or more representative points on the clinical volume, the shortest distance to the design envelope volume, indicated by a thin arrow 510, is determined. In one example of the present invention, if one or more representative points on the clinical volume are within the design envelope volume, the distance is classified as a positive or negative internal distance, while if one or more representative points on the clinical volume are outside the design envelope volume, the distance is an external distance, classified with the opposite sign to the internal distance. The value of the inclusion metric is determined as either the minimum value of the signed shortest distance for the distances of the one or more representative points when the internal distance is classified as positive, or the maximum value of the signed shortest distance when the internal distance is classified as negative.

[0042] In this case, as shown in Figure 5(b), each distance from the clinical volume to the design envelope volume has a positive value because the clinical volume is completely enclosed by the design envelope volume. The smallest of these, which is reported as the inclusion metric, is indicated by the thick arrow 511. All other arrows in this figure have a length longer than the arrow indicated by 511.

[0043] Figure 5(c) shows a case where a portion of the clinical volume 513 moves beyond the planned envelope volume 514. In this example of the invention, a portion of the clinical volume 520 is still within the planned envelope volume, while a portion of the clinical volume 521 is now outside the planned envelope volume. For each point on the clinical volume (both outside and inside the planned envelope volume), the shortest distance to the planned envelope volume is determined. For these points on the portion of clinical volume 520 that is still within the planned envelope volume, this distance is assigned a positive value, indicated by a closed-tip arrow 515. For these points on the region of clinical volume 521 that is outside the planned envelope volume, this distance is assigned a negative value, indicated by a hollow-tip arrow 516. The reported inclusion metric is the minimum of several signed shortest distances, as indicated by a thick arrow 517. In this example of the invention, the inclusion metric also has a negative value, since the shortest distance is between the PEV and a portion of the CV that is outside the PEV.

[0044] In one example of the present invention, after a design envelope volume is defined, a distance transform is calculated around the design envelope volume, which is used to determine the inclusion metric. Alternatively, the inclusion metric is determined by simulating the translation of one or more therapeutic clinical volumes relative to the design envelope volume. In one embodiment of the present invention, the simulated translation is a linear translation.

[0045] An example of an efficient method for calculating the inclusion metric is shown in Figure 6. Figure 6(a) shows the calculation during radiotherapy planning stage 601. Once the PEV 602 (shown by the dashed line) is determined, the signed distance transform (603) is calculated, which is shown in legend 604. The signed distance transform is a three-dimensional matrix of signed distance values. In this example, the values ​​of the matrix are represented by consecutive radial sections with a width of 5 mm. The section at the center of the image is +25 to +30 mm from the PEV. This is surrounded by radial sections of +20 to +25 mm. The image has consecutive concentric 5 mm sections ranging from +5 mm to 0 mm at the inner boundary of the PEV, from 0 to -5 mm outside the PEV, and from -10 to -15 mm at the edge of the image furthest from the PEV.

[0046] A signed distance transformation for determining the inclusion metric can be calculated by any method detailed in the scientific literature, such as the method by Borgefors [1]. This signed distance transformation shows the shortest distance from the clinical volume to the PEV for any point in the therapeutic coordinate system. In one example of the present invention, the value is positive if the point is inside the PEV (as shown in Figure 5(a)) and negative if the point is outside the PEV (as shown in Figure 5(b)).

[0047] The radiotherapy stage is shown in Figure 6(b). The signed distance transformation 603 from the planning stage is directly copied to the treatment space 606. That is, the three-dimensional matrix of signed distance values ​​is mapped to the treatment space represented in this figure by a series of radial sections having signed distance values ​​from 25 to 30 mm to -15 to -10 mm. The surface of the clinical volume 607 is shown, and in this example of the invention, the clinical volume is completely contained within the planning envelope volume, so the containment metric has a positive value in this example of the invention. As shown, the clinical volume is offset to the upper right quadrant with respect to the origin of the original planning envelope volume. Given this and the position of the clinical volume 607 on the therapeutic image, the minimum value among these signed shortest distance values ​​on the boundaries of the clinical volume can be found by any means. A particular trivial method is by an exhaustive search along all boundary points of the clinical volume, but more efficient algorithms such as priority queues or sorting algorithms are equally possible. Alternatively, a convention can be applied to the distance transformation in which positive values ​​are assigned to multiple parts of the CV outside the PEV and negative values ​​are assigned to multiple parts of the CV inside the PEV (the reverse of the convention used in Figure 5). In this scenario, the inclusion measurement is the maximum of multiple signed shortest distances on the boundary of the CV, rather than the minimum value. This is shown in Figure 6(c).

[0048] The inclusion metric of the present invention can be calculated using 3D or 2D geometry. In the 3D case, the inclusion metric is susceptible to volume changes occurring in any direction. An example of use in the 3D case is when the clinical volume being tracked is a clinical target volume and movement in any direction is clinically important. In the 2D case, the inclusion metric is susceptible to volume changes occurring along a specific image axis. An example of use in the 2D case is when the spinal cord is the clinical volume being tracked, but the clinical volume extends above and below the area of ​​the therapeutic image. In this case, it may be better if the inclusion metric is less susceptible to craniocaudal direction changes, as those changes may not be clinically relevant. In the method described in Figure 600, the difference between the 2D and 3D methods lies in the calculation of the signed distance transformation 603. To generate the 3D inclusion metric, the signed distance transformation describes the distance from the clinical volume to the planned envelope volume in any direction. To generate a 2D inclusion metric, the signed distance transformation describes the distance from the clinical volume to the planned envelope volume only in a 2D plane orthogonal to the direction of exclusion. A further extension of the present invention is clipping the area from which the metric is calculated to avoid areas that are likely to produce inaccurate results, such as the top and bottom of a planning or therapeutic image sequence, or to remove areas that are far from the treatment area.

[0049] An alternative, but less efficient, method for calculating the minimum margin encapsulation metric is to simulate numerous linear translations of one or more therapeutic clinical volumes and identify the magnitude of the linear translation required for each clinical volume to exceed the relevant design envelope volume.

[0050] In one example of the present invention, the planned envelope volume is defined as the clinical volume plus a margin specified by a pre-defined protocol. Preferably, the clinical volume is the clinical target volume or total tumor volume for the treatment plan. Alternatively, the clinical volume represents organs at risk to be avoided during treatment.

[0051] A key advantage of the minimum margin inclusion metric over others previously reported is that this inclusion metric is signified such that movement of clinical volume outside the planned envelope volume (negative value of the inclusion metric) appears different from movement of clinical volume inside the planned envelope volume (positive value of the inclusion metric). In one example of the present invention, the method also includes a step of displaying one or more determined inclusion metrics to the system user. The principle of displaying the inclusion metrics to the user can be extended to give greater insight and flexibility. Further embodiments of the present invention use the inclusion metric to track changes in a patient. In one example of the present invention, multiple steps of the method may be repeated over a set period of time to monitor changes in the inclusion metric on multiple therapeutic images. Preferably, the set period may vary between one day and three months. In some examples of the present invention, changes in the inclusion metric are displayed to the user. For this purpose, a tracking view of changes is shown in Figure 700, which preferably plots the inclusion metric for each day 703 of treatment, but other time intervals may be used. This plot allows for the display of multiple thresholds of interest 702, where values ​​of the inclusion metric exceeding a pre-set threshold indicate an unsafe amount of subject movement within the scanner. These thresholds can be either global values ​​for each design envelope volume or individual different values ​​for each design envelope volume. Each threshold can be set in numerous ways, such as as a user input value, as an organ-specific standard value, or algorithmically determined based on any combination of location, size, radiation tolerance or objective, treatment technique, and proximity to other clinical volumes or design envelope volumes. In this example, several points for this case 704 show a downward trend, with the clinical volume moving outside the design envelope volume on day 6 of treatment.Other implementations of systems that utilize inclusion metrics for tracking change may include using inclusion metrics as input to decision support algorithms that autonomously monitor changes in inclusion metrics against statistical criteria and advise on when statistically significant changes in behavior have occurred.

[0052] While the global encapsulation metric is useful for supporting immediate decision-making and error tracking, in one example of the present invention, other views can also help the user understand what is happening in the patient case. In a further embodiment of the present invention, Figures 8(a) and 8(b) show two alternative 3D surface rendering views of a clinical volume 801. In Figure 8(a), each cube 801 represents a portion of the clinical volume surface. Its surface can be algorithmically partitioned, preferably according to a grid or mesh set by the user, or based on the patient's geometry, surface curvature, and dose grid or any combination of multiple image parameters. If the partitioning is fine enough, the surface appears smooth and continuous to the user (see Figure 8(b)). Directional labels 802 allow the user to relate the 3D rendered surface view to the patient's anatomical structure. In this example of the present invention, the encapsulation metric is determined using the minimum signed distance, but the encapsulation metric is also applied to the maximum distance determined as described above. The representation of each element on the surface is modified by means of color, transparency, texture, or label, according to the shortest distance (or, if an alternative method for calculating the metric is used) to the planned envelope volume at its location on the surface of the clinical volume, as shown in Legend 803. One example of this representation is a heatmap, where lower values ​​of the metric are displayed hotter. The user can set any other color scheme they deem more useful. This allows the user to quickly identify the area of ​​greatest interest 804. Figure 8(b) shows a 3D surface image of the volume, rather than a cubic version.

[0053] The only way for the user to immediately correct any patient positioning issues highlighted by the determination of the inclusion metric is to move the patient platform, but this may only allow for linear movement. In one example of the present invention, the subject may be positioned in a designated position within the scanner for planning and therapeutic images, and the system user can adjust the subject's position within the scanner according to one or a determined inclusion metric.

[0054] Therefore, further embodiments calculate a minimum margin metric by orientation and display this information to the user. A particular method for achieving this is to assign each point on the surface of a clinical volume to an orientation based on the 3D angle of the structural surface at that point. The 3D angle of the structural surface can be easily determined from the gradient of the signed distance transformation 603. Another standard method is to determine the 3D angle of the surface orientation by existing in computer graphics. The consecutive 3D angle values ​​for all points on the surface are then classified (binned) into discrete directions based on a set of predetermined limits. In calculating the inclusion metric, the multiple points on the CV surface that fall into each orientation bin are processed independently of each other, resulting in separate inclusion metric values ​​for each orientation.

[0055] Another method achieves a directional inclusion metric by simulating the patient's positional shift in each direction and reporting the distance moved in each direction before the clinical volume exceeds the planned envelope volume. This advises the user whether the couch can be moved in any direction without moving the clinical volume outside the planned envelope volume. This directional minimum margin metric can be presented to the user in many forms, such as a simple table, using multiple 2D or 3D directional arrows of equal length to the metric value, or using a 2D or 3D representation of a CV shape with multiple indicators on the edge corresponding to each direction. An example of how this information is presented is shown in Figure 900. The 3D view is shown with indicators 901 showing the minimum margin metric in each direction. Multiple directions are provided by direction labels 902. As previously mentioned, the indicator colors indicate the minimum margin in each direction, as explained by legend 903. In this case, the user finds that the margin metric is slightly worse in the rearward direction (904) than in the forward direction (905), and this situation can be improved by a small forward movement of the platform (couch).

[0056] [1] Borgefors, On Digital Distance Transforms in Three Dimensions, Computer Vision and Image Understanding, 1996, 64 (3); 368-376 Other image-guided interventional medical modalities include tracking the patient's anatomical structure over time.

[0057] Embodiments of the present invention can be applied to any or all of the following: namely, picture archiving and communication systems (PACS); advanced visualization workstations; image acquisition workstations; web-based or cloud-based medical information and imaging systems; therapy planning systems (TPS); linear accelerator consoles for radiotherapy; proton therapy consoles for radiotherapy.

[0058] The present invention has been described with reference to the accompanying drawings. However, it will be recognized that the present invention is not limited to the specific embodiments described herein and illustrated in the accompanying drawings. Furthermore, since the exemplary embodiments of the present invention can be implemented using electronic components and circuits known to those skilled in the art, details beyond what is considered necessary for understanding and appreciating the underlying concepts of the present invention and to avoid obscuring or confusing the teachings of the present invention are not described above.

[0059] The present invention may be implemented as a computer program for execution on a computer system, and includes at least a code portion for performing steps of the method according to the present invention when executed on a programmable device such as a computer system, or for enabling a programmable device to perform functions of a device or system according to the present invention.

[0060] A computer program is a list of instructions, such as a specific application program and / or an operating system. A computer program can include, for example, one or more of the following: subroutines, functions, procedures, object methods, object implementations, executable applications, applets, servlets, source code, object code, shared libraries / dynamically loaded libraries, and / or other sequences of instructions designed to run on a computer system. Therefore, some examples describe a non-temporary computer program product having stored executable program code for automatic contouring of cone-beam CT images.

[0061] Computer programs may be stored internally on tangible, non-temporary computer-readable storage media, or they may be transmitted to a computer system via computer-readable transmission media. All or part of a computer program may be provided to computer-readable media permanently, removablely, or remotely coupled to an information processing system. Tangible, non-temporary computer-readable media may include, but are not limited to, any number of the following: magnetic storage media, including disks and tape storage media; optical storage media, such as compact disk media (e.g., CD-ROM, CD-R, etc.) and digital video disc storage media; non-volatile memory storage media, including semiconductor-based memory units such as flash memory, EEPROM, EPROM, and ROM; ferromagnetic digital memory; MRAM; volatile storage media, including registers, buffers or caches, main memory, RAM, etc.; and any number of these.

[0062] A computer process typically includes a running program or part of a program, current program values ​​and state information, and resources used by the operating system to manage the execution of the process. An operating system (OS) is software that manages the sharing of computer resources and provides programmers with interfaces used to access those resources. The OS processes system data and user input and responds by assigning and managing tasks and internal system resources as services to users and programs of the system.

[0063] A computer system may include, for example, at least one processing unit, associated memory, and multiple input / output (I / O) devices. When a computer program is executed, the computer system processes information according to the computer program and generates resulting output information via the I / O devices.

[0064] The present invention has been described in this specification with respect to specific embodiments of the present invention. However, various modifications and changes may be made without departing from the scope of the present invention as described in the appended claims, and it is clear that the claims are not limited to the specific embodiments described above.

[0065] Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative, and that alternative embodiments may merge logic blocks or circuit elements, or impose alternative functional decompositions on various logic blocks or circuit elements. Therefore, it should be understood that the architectures shown herein are merely illustrative, and in practice, many other architectures can be implemented to achieve the same functionality.

[0066] Any arrangement of components to achieve the same functionality is effectively “associated” in such a way that the desired functionality is achieved. Therefore, any two components combined herein to achieve a particular functionality, regardless of whether they are architectural or intermediate components, can be considered “associated” with one another in such a way that the desired functionality is achieved. Similarly, any two components thus associated can also be considered “operably connected” or “operably coupled” with one another in such a way that the desired functionality is achieved.

[0067] Furthermore, those skilled in the art will recognize that the boundaries between the operations described above are merely illustrative. Multiple operations may be combined into a single operation, a single operation may be distributed among additional operations, and operations may be performed with at least partial overlap in time. Furthermore, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be modified in various other embodiments.

[0068] However, other modifications, variations, and substitutions are possible. Therefore, the specifications and drawings should be considered illustrative rather than restrictive. In the claims, no reference numerals in parentheses should be construed as limiting the claims. The term “including” does not exclude the existence of other elements or steps other than those enumerated in the claims. Furthermore, the terms “a” or “an” as used herein are defined as one or more. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed as meaning that the introduction of an element of another claim by the indefinite article “a” or “an” limits a particular claim containing such introduced element of a claim to an invention containing only one such element, even if the same claim contains the introductory phrase “one or more” or “at least one” and an indefinite article such as “a” or “an”. The same applies to the use of definite articles. Unless otherwise specified, terms such as “first” and “second” are used to arbitrarily distinguish between the elements they describe. Thus, these terms are not necessarily intended to indicate a temporal or other priority of such elements. The mere fact that certain means are described in different claims does not mean that combinations of these means cannot be used advantageously. The technical concepts that can be understood from the above embodiments are described below as an addendum. [Note 1] A method for determining the changes between a subject's planning image and a treatment image, The method involves defining one or more clinical volumes on the subject's planning image, and defining a planning envelope volume around the clinical volumes on the planning image. The process involves acquiring a therapeutic image from the subject at a position corresponding to the position of the planning image, wherein the therapeutic image has the same planning envelope volume as the planning image. Determining the position of one or more clinical volumes on the therapeutic image relative to the planned envelope volume, A method comprising the steps of determining an inclusion metric for one or more of the one or more clinical volumes that define the degree of inclusion of the clinical volume on the therapeutic image within the planned envelope volume on the therapeutic image. [Note 2] Determining the inclusion metric means Designating one or more representative points on the surface of the clinical volume, The process includes determining the shortest distance to one or more representative points of the clinical volume relative to the planned envelope volume, If the representative point on the clinical volume is within the design envelope volume, the shortest distance is classified as either a positive or negative internal distance; if the representative point on the clinical volume is outside the design envelope volume, the shortest distance is an external distance classified with the opposite sign to the internal distance. The method according to Appendix 1, wherein the value of the inclusion metric is determined as either the minimum value of the signed shortest distances among the distances for one or more representative points when the internal distance is classified as positive, or the maximum value of the signed shortest distances when the internal distance is classified as negative. [Note 3] The method according to Appendix 1 or 2, wherein the planning image and the multiple treatment images are 3D images. [Note 4] The method described in Appendix 3, wherein the planning image and multiple treatment images are CT, PET, SPECT, or MRI images. [Note 5] The method according to any one of the appendices 1 to 4, wherein, after the planned envelope volume is defined, a distance transformation is calculated around the planned envelope volume, and the said distance transformation is used to determine the inclusion metric. [Note 6] The method according to any one of the appendices 1 to 4, wherein the inclusion metric is determined by a simulation of the movement of one or more therapeutic clinical volumes relative to the planned envelope volume. [Note 7] The simulated movement is a linear movement, as described in Appendix 6. [Note 8] Determining the position of the one or more clinical volumes on the therapeutic image relative to the planned envelope volume is: The geometric relationship between the planning image and the treatment image is determined, and the position of the clinical volume is mapped to the entire treatment image. The method according to any one of the items in Appendix 1 to 7, comprising identifying a plurality of anatomical features on the therapeutic image in order to determine the location of the clinical volume. [Note 9] The planned envelope volume is defined as the clinical volume plus a margin specified by a pre-set protocol, as described in any one of the appendices 1 to 8. [Note 10] The method according to any one of the appendices 1 to 9, wherein the clinical volume is the clinical target volume or total tumor volume for the treatment plan. [Note 11] The method according to any one of the appendices 1 to 9, wherein the aforementioned clinical volume represents an organ that poses a risk to be avoided in treatment. [Note 12] The method according to any one of the appendices 1 to 11, wherein the plurality of steps are repeated over a set period of time to monitor changes in the inclusion metric on the plurality of therapeutic images. [Note 13] The aforementioned set period varies between 6 hours and 3 months, as described in Appendix 12. [Note 14] The method described in Appendix 12 or 13, wherein the changes in the aforementioned inclusion metric over time are displayed to the user. [Note 15] The method according to any one of the appendices 1 to 14, wherein the subject is positioned in a designated location within the scanner for the planning image and the treatment image, and the user of the system can adjust the position of the subject according to one or more determined inclusion metrics. [Note 16] The method according to Appendix 15, as per Appendix 14, wherein the change in the inclusion metric over time is displayed to the user, and the display further indicates when the inclusion metric exceeds a preset threshold indicating unsafe movement of the subject within the scanner. [Note 17] The method according to any one of the appendices 1 to 16, further comprising the step of displaying one or more determined inclusion metrics to a user of the system. [Note 18] The method according to any one of the appendices 1 to 17, further comprising the step of using the inclusion metric to determine a 3D representation showing a movement margin indicating how a subject can be moved within the scanner, while maintaining or improving the position of the clinical volume relative to the position of the planned envelope volume. [Note 19] The method according to any one of the appendices 1 to 18, wherein the clinical volume is displayed as a 3D rendered surface, and the inclusion metric for the surface is shown as a heatmap on the surface. [Note 20] A system for analyzing medical images to determine changes between a subject's planning image and treatment image, It is equipped with a processor, and the processor is Determine one or more clinical volumes on the subject's planning image, and define a planning envelope volume around the clinical volumes for the planning image. The analysis involves analyzing therapeutic images from the subject at positions corresponding to the positions of the planning images, wherein the therapeutic images have the same planning envelope volume as the planning images. Determining the position of one or more clinical volumes on the therapeutic image relative to the planned envelope volume, A system configured to perform the following: determine an inclusion metric for one or more of the one or more clinical volumes that define the degree of inclusion of the clinical volume on the therapeutic image within the planned envelope volume on the therapeutic image. [Note 21] The system according to Appendix 20, wherein the processor is further configured to perform a plurality of steps of the method described in any one of Appendix 2 to 19. [Note 22] The system according to appendix 21 or 22, further comprising a display for displaying at least one of the planning image, the treatment image, one or more clinical volumes, and the planning envelope volume. [Note 23] A computer program product comprising a plurality of instructions, wherein, when the program is executed by a computer, the plurality of instructions cause the computer to execute the method described in any one of the appendices 1 to 19.

Claims

1. A system that analyzes multiple medical images to determine the changes between a subject's planning image and treatment image, Processor and A memory for storing multiple instructions, wherein when the multiple instructions are executed by the processor, the processor receives Receiving the subject's planning images and the subject's treatment images, The clinical volume is determined from the aforementioned planning image, wherein the clinical volume includes at least one of the clinical target volume for treatment planning, the total tumor volume, and the clinical volume representing organs at risk to be avoided during treatment. A design envelope volume is defined around the aforementioned clinical volume, where the design envelope volume is defined as the volume obtained by adding a margin specified by a pre-defined protocol around the aforementioned clinical volume. The position of the clinical volume on the therapeutic image is determined relative to the planned envelope volume, Designate one or more representative points on the surface of the aforementioned clinical volume, Determining the shortest distance between one or more representative points on the surface of the clinical volume and the boundary of the planned envelope volume, The inclusion metric for the aforementioned therapeutic image is determined, and the following is performed: The inclusion metric for the therapeutic image is determined in part on the shortest distance between one or more representative points on the surface of the clinical volume and the boundary of the planned envelope volume, in a system.

2. The one or more representative points on the surface of the clinical volume are a plurality of representative points designated across the entire surface of the clinical volume, The system according to claim 1, wherein if one of the plurality of representative points on the clinical volume is within the design envelope volume, the shortest distance associated with the representative point is a signed number greater than or equal to zero, and if the representative point on the clinical volume is outside the design envelope volume, the shortest distance associated with the representative point is a signed number less than zero, and the value of the inclusion metric is the minimum of the shortest distances.

3. The system according to claim 1, wherein the planning image and the treatment image are three-dimensional (3D) images.

4. The system according to claim 1, wherein at least one of the planning image and the treatment image is a CT (computerized tomography) image, a PET (positron emission tomography) image, a SPECT (single photon emission computerized tomography) image, or an MRI (magnetic resonance imaging) image.

5. The system according to claim 1, wherein, when the plurality of instructions are executed, the processor further causes the processor to calculate a distance transformation representing the distance from the boundary of the planned envelope volume around the planned envelope volume, and the inclusion metric is determined in part on the distance transformation.

6. When the aforementioned multiple instructions are executed, the processor further: To simulate the linear shift of multiple clinical volumes on the therapeutic image relative to the planned envelope volume, Identifying the magnitude of the linear movement required for each clinical volume to exceed the planned envelope volume, The system according to claim 1, which performs the following: determining the inclusion metric based on the magnitude of the identified linear movement.

7. A non-temporary computer-readable medium containing instructions, wherein, when the instructions are executed by the processor of the treatment planning system, the treatment planning system will... Receiving the subject's planning images and the subject's treatment images, The clinical volume is determined from the aforementioned planning image, wherein the clinical volume includes at least one of the clinical target volume for treatment planning, the total tumor volume, and the clinical volume representing organs at risk to be avoided during treatment. A design envelope volume is defined around the aforementioned clinical volume, where the design envelope volume is defined as the volume obtained by adding a margin specified by a pre-defined protocol around the aforementioned clinical volume. The position of the clinical volume on the therapeutic image is determined relative to the planned envelope volume, Designate one or more representative points on the surface of the aforementioned clinical volume, Determining the shortest distance between one or more representative points on the surface of the clinical volume and the boundary of the planned envelope volume, The inclusion metric for the aforementioned therapeutic image is determined, and the following is performed: The inclusion metric for the therapeutic image is determined in part on the shortest distance between one or more representative points on the surface of the clinical volume and the boundary of the planned envelope volume in a non-temporal, computer-readable medium.

8. When the aforementioned instruction is executed by the processor of the treatment planning system, the treatment planning system further: To determine the geometric relationship between the planning image and the treatment image, Identifying one or more anatomical features on the aforementioned therapeutic image, A non-temporary computer-readable medium according to claim 7, which causes the following to be performed: determining the positions of a plurality of clinical volumes on a therapeutic image, in part based on the geometric relationships and the one or more anatomical features, the positions of the clinical volumes on the therapeutic image, in part based on the geometric relationships and the one or more anatomical features.

9. The non-temporary computer-readable medium according to claim 7, wherein, when the instruction is executed by the processor of the treatment planning system, the treatment planning system further causes the inclusion metric to repeatedly determine inclusion metric in order to monitor changes in the inclusion metric over a period of time.

10. The non-temporary computer-readable medium according to claim 9, wherein the aforementioned period is between 6 hours and 3 months.

11. The non-temporary computer-readable medium according to claim 7, wherein, when the instruction is executed by the processor of the treatment planning system, the treatment planning system further causes the inclusion metric and the certain period to display on a display.

12. A computer-based method for analyzing medical images, The image planning system's processor receives the subject's planning image and the subject's treatment image, The processor determines the clinical volume from the planning image, wherein the clinical volume includes at least one of the clinical target volume for treatment planning, the total tumor volume, and the clinical volume representing an organ at risk to be avoided in treatment. The processor defines a planned envelope volume around the clinical volume, where the planned envelope volume is defined as the volume obtained by adding a margin specified by a pre-configured protocol around the clinical volume. The processor determines the position of the clinical volume on the therapeutic image relative to the planned envelope volume, The processor designates one or more representative points on the surface of the clinical volume, The processor determines the shortest distance between one or more representative points on the surface of the clinical volume and the boundary of the planned envelope volume, The processor comprises determining an inclusion metric for the therapeutic image, A computer-based method by which the inclusion metric for the therapeutic image is determined, in part, on the shortest distance between one or more representative points on the surface of the clinical volume and the boundary of the planned envelope volume.

13. The processor generates a heat map representing the distance to the planned envelope volume for each position on the surface of the clinical volume, The method performed by a computer according to claim 12, further comprising the processor displaying the heatmap on a display.

14. The processor determines the inclusion metric, in part, based on a distance transformation that represents the distance from the boundary of the planned envelope volume, derived around the planned envelope volume, or The computer-based method according to claim 12, further comprising: the processor simulating a linear shift of a plurality of clinical volumes on the therapeutic image relative to the planned envelope volume; identifying the magnitude of the linear shift required for each clinical volume to exceed the planned envelope volume; and determining the inclusion metric based on the identified magnitude of the linear shift.

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