Method for quantifying patient setting errors in radiotherapy

Abstract

A method and system for determining a change between a planning image and a treatment image of a subject is described. The method comprises the steps of: defining one of a plurality of clinical volumes on the planned image of a subject and defining a planned envelope volume for the planned image around the clinical volume; acquiring, from the subject, a treatment image for a position corresponding to the position of the planning image; wherein the treatment image will have the same planned envelope volume as the planned image; determining a position of the one or more clinical volumes relative to the planned envelope volume on the treatment image; encapsulation metrics for one or more of the clinical volumes are determined that define a degree of encapsulation of the clinical volume on the treatment image within the planned 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 the scan is acquired, and in particular in the field of radiation therapy. Background Art

[0002] During radiation therapy, the patient will have a planning medical image, most commonly a computed tomography (CT) scan, but in an increasing number of cases this may be a magnetic resonance (MR). Based on the medical image, a plan is developed for multiple radiation therapy sessions. These treatment sessions are called fractions. One of the problems with this approach is that the planning images do not accurately represent the patient's anatomy for each treatment session. The patient's anatomy will randomly change from session to session relative to where it appears on the planning image. The patient's anatomy may also change systematically over time due to weight loss, tumor shrinkage, or other such processes. Image-guided radiation therapy (IGRT) and adaptive radiation therapy (ART) are techniques that use imaging to monitor anatomical changes during treatment in order to optimize patient position in the case of IGRT or modify the treatment plan in the case of ART.

[0003] In order to produce a radiotherapy that is robust to changes in patient position, margins are applied to certain areas marked on the planning image. Oncologists identify the clinical target volume (CTV) as an area that may contain the disease to be treated with radiation. A margin will be applied to the CTV to create a planning target volume (PTV). The PTV is the area to which radiation will be applied to ensure that the CTV receives the required radiation dose. The PTV is a spatial envelope that encapsulates all possible positions of the CTV due to changes in anatomical position. For sensitive healthy organs to be avoided, there is a similar definition. Organs at risk (OAR) have a margin that is applied to generate a planned volume at risk (PRV). These concepts are formalized in ICRU reports 50 and 62 [1, 2]. Since the present invention is equally applicable to target areas and healthy areas, we will group these definitions, where CTV and OAR are grouped together as clinical volumes (CV), and PTV and PRV are grouped together as planning envelope volumes (PEV). PEVs are almost always explicitly defined during radiotherapy treatment planning via contours drawn during planning, but they can also be defined implicitly, such as the CV plus a margin of 10 mm.

[0004] During IGRT and ART, a critical decision is whether the CV is still enclosed by its corresponding PEV. A CTV that moves outside the PTV has an increased risk of being missed by radiation therapy. Similarly, an OAR that moves outside the PRV has an increased risk of moving into a high-dose area. If the CV extends outside the PEV, the assumptions of the original plan are invalid and the patient position must be moved to bring the CV back into the PEV or a new plan should be considered. In the event that the user wishes to perform a position correction, most radiotherapy devices facilitate this with automatic linear movement of the patient table. More advanced systems will allow automatic correction of patient angles as well as linear shifts.

[0005] Figure 1 A flow chart of an example current process 100 for IGRT / ART is shown. The process has two stages. The planning stage 101 occurs before the first radiation treatment. In step 103, a single or multiple clinical volumes (CVs) are identified on a planning 3D image, such as a CT scan. Some CVs will be precisely associated with organs visible in the scan, but other CVs may not be associated, such as clinical target volumes that identify risk areas for cancer disease. In step 104, PEVs are generated around single or multiple CVs to account for potential movement of the CVs during treatment. The number of PEVs may be different from the number of CVs, for example because a PEV is not generated for each CV, or because multiple CVs can be combined into a single PEV. Subsequently, a radiation treatment plan is generated in step 105, using the PEVs of step 104 to optimize the dose delivered to the treatment target while keeping the dose to organs at risk below a set threshold.

[0006] Treatment stage 102 will be performed once or for multiple treatment fractions. Step 106 is performed for each treatment fraction or selected treatment fractions, wherein an imaging system integrated into the treatment machine acquires an image prior to treatment delivery, typically up to 15 minutes before treatment begins, however the patient will remain in the treatment position during this time. In step 107, the user visually compares the image with the planned image to verify that each critical CV in step 104 is still contained within the associated PEV. Typically, this is done on a dedicated image review workstation that will allow the use of a variety of different image comparison methods. Based on this evaluation, in step 120, the user decides whether to continue with treatment and may also need to correct the patient position.

[0007] In the prior art, most IGRT and ART systems only provide tools for manual, subjective assessment of whether the CV is contained within the PEV. The few systems designed to provide automated assessments of the patient's anatomy in real time provide a Boolean yes / no indicator, e.g., green if the CV is within the PEV and red otherwise [3]. The problem with using indicators like this is that they cannot quantify changes and track the potential development of problems over time.

[0008] Some systems have attempted to use quantitative measurements to track changes over time. Wang et al. [4] used Varian's velocity "adaptive monitoring navigator" to track changes in the volume of a structure and any shifts in the location of the structure's center. These metrics are useful if the CV being tracked is rigid, but in modern radiotherapy, some CVs can be very complex and do not simply move. Tracking the movement of the CV center may miss portions of the CV that move outside of its PEV.

[0009] A survey of academic research found many other papers reporting frameworks and metrics for monitoring patient changes during radiation therapy. In particular, Brouwer et al. [5] conducted a systematic review of papers on technologies used for ART in head and neck cancer. The metrics of anatomical change they identified from 51 studies were weight, body thickness, CV volume, CV density, CV position, CV angle, and their changes, such as change, rate of change, or multiple changes occurring at a specific anatomical point. As with the complex CVs described above, these metrics may miss portions of the CV that move outside of their PEV.

[0010] Several studies [6–8] reported more complex anatomical change measures that were directly based on changes in patient images or structures identified on them.

[0011] Schaly et al. [6] investigated gamma index differences between CT scans. The gamma index is a method developed to compare two 3D radiation dose distributions [9]. It is sensitive to changes in voxel intensity and to the match distance, which is the distance between a voxel in the reference image and a voxel with the same intensity in the test image. The gamma index is certainly sensitive to changes in CV position, but as a global image-based metric, it does not unambiguously measure the adequacy of PEV in covering the CV; a poor gamma index score will be affected by anatomical changes at any location in the image.

[0012] Fiorino et al. [7] reported 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 the values ​​in the CV region are plotted in a histogram. This provides a measure of the expansion and contraction of voxels throughout the CV. The advantage of this method over Schaly's method is that it focuses on a specific CV rather than the entire volume. However, it does not directly address the issue of the adequacy of the PEV in covering the CV. There may be changes within the CV that do not cause the CV to extend beyond the PEV but that result in higher Jacobian changes.

[0013] A method that is closest to the present invention is reported by Hargrave et al. [8]. This measure uses the Hausdorff distance between the original CV and the CV during treatment as a metric. The Hausdorff distance is a measure of the worst-case difference between the two sets of profiles. This will detect significant changes in complex CVs, but there is no clear link to the clinical concern, namely, CV movement outside the PEV. A narrowing of the CV and further movement inside the PEV is not a clinical concern, but would be recorded as a change. The lack of directionality in the metric makes its use in tracking changes more difficult. [1] ICRU Report 50: Prescribing, Recording and Reporting of Photon Beam Therapy: ICRU Journal, Vol. 26, No. 1, September 1993 [2] ICRU Report 62: Prescribing, recording and reporting of photon beam therapy (Supplement to ICRU Report 50): ICRU Journal, Vol. 32, No. 1, November 1999 [3]https: / / viewray.com / mri-guided-smart / [4] Wang et al., Statistical process control-based adaptive radiation therapy for oropharyngeal cancer: Journal of Applied Clinical Medical Physics, 2020;21:9:171–177 [5] Brouwer et al., Identifying patients who may benefit from adaptive radiation therapy: 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. [6] Schaly et al., An alarm system for monitoring changes in patient anatomy during radiation therapy for head and neck cancer, Journal of Applied Clinical Medical Physics, 2021;22:168–174 [7] Fiorino et al., Introduction of the Jacobian volume histogram of deformable organs: Application to the evaluation of parotid gland atrophy, Physics in Medicine and Biology, 56 (2011) 3301–3312. [8] Hargrave et al., Feature alignment scores for online cone-beam CT-based image-guided radiotherapy for prostate cancer, Medical Physics, 45(7), July 2018, pp. 2898-2911. [9] Low et al., Techniques for quantitative evaluation of dose distribution, Med Phys, 1998;25:656-661. Summary of the invention

[0014] Therefore, the following problems are solved by the present invention.

[0015] What is needed is a simple, quantifiable metric that easily correlates changes between images acquired over a period of time to help identify changes in a patient's anatomical position. Preferably, this information can be used for treatment planning in the event of changes in patient geometry, and these can be presented to the clinical user in a meaningful way.

[0016] According to the present invention, a method for determining changes between a planning image and a treatment image of a subject is provided, comprising the following steps: defining one of a plurality of clinical volumes on a planning image of the subject and defining a planning envelope volume for the planning image around the clinical volume; acquiring a treatment image from the subject for a position corresponding to the position of the planning image; wherein the treatment image will have the same planning envelope volume as the planning image; determining the position of one or more clinical volumes on the treatment image relative to the planning envelope volume; determining one or more packaging metrics of the clinical volumes, which define the degree of packaging of the clinical volume on the treatment image within the planning envelope volume on the treatment image.

[0017] In an embodiment of the present invention, determining the encapsulation metric includes the following steps: specifying one or more representative points on the surface of a clinical volume; determining the shortest distance from the one or more representative points of the clinical volume to the planning envelope volume; wherein when the representative point on the clinical volume is located within the planning envelope volume, the shortest distance is classified as a positive or negative internal distance, and when the representative point on the clinical volume is located outside the planning envelope volume, the shortest distance is an external distance, which is classified with a sign opposite to that of the internal distance; and the encapsulation metric value is determined as: the minimum value of the signed shortest distance value among 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 among the distances of the one or more representative points when the internal distance is classified as negative.

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

[0019] In a preferred embodiment of the invention, after the planning envelope volume has been defined, a distance transform is calculated around the planning envelope volume and this is used to determine the packaging metric.

[0020] Further preferably, the encapsulation metric is determined by simulating a translation of one or more treatment clinical volumes relative to the planning envelope volume. In an embodiment of the invention, the simulated translation is a linear translation.

[0021] In a preferred embodiment of the present invention, determining the position of one or more clinical volumes on a treatment image relative to a planning envelope volume includes one or more of the following: determining a geometric relationship between the planning image and the treatment image and mapping the clinical volume position to the treatment image; identifying anatomical features on the treatment image to locate the clinical volume.

[0022] Preferably, the planning envelope volume is defined as the clinical volume plus a margin specified by a predetermined protocol.

[0023] In an embodiment of the invention, the clinical volume is a clinical target volume or a total tumor volume for treatment planning. In an alternative embodiment of the invention, the clinical volume represents an organ at risk to be avoided in treatment.

[0024] In a preferred embodiment, the steps are repeated for a set period of time to monitor changes in the encapsulation metric on the treatment images. Preferably, the set period of time varies between 6 hours and 3 months.

[0025] In an embodiment of the present invention, changes in packaging metrics over time are displayed to the user.

[0026] In a preferred embodiment of the invention, the subject is positioned in a designated position in the scanner for planning and treatment images, and the system user can adjust the position of the subject based on one or more determined packaging metrics.

[0027] Preferably, the change in the encapsulation metric over time is displayed to the user, wherein the display also shows when the encapsulation metric exceeds a pre-set threshold, which indicates unsafe movement of the subject in the scanner.

[0028] Preferably, the method further comprises the step of displaying one or more determined packaging metrics to a system user.

[0029] In a preferred embodiment, the method further comprises the step of determining a 3D representation using the packaging metric, the 3D representation showing a movement margin showing how the subject may move in the scanner while maintaining or improving the position of the clinical volume relative to the position of the planning envelope volume.

[0030] Preferably, the clinical volume is displayed as a 3D rendered surface, with the encapsulation metrics for the surface indicated as a heat map on the surface.

[0031] In another embodiment of the present invention, a system for analyzing medical images to determine changes between a planning image and a treatment image of a subject is also provided, comprising: a processor, the processor being configured to: determine one or more clinical volumes on a planning image of the subject, and define a planning envelope volume for the planning image around the clinical volumes; analyze a treatment image from the subject for a location corresponding to the location of the planning image; wherein the treatment image will have the same planning envelope volume as the planning image; determine the position of one or more clinical volumes on the treatment image relative to the planning envelope volume; determine one or more packaging metrics of the clinical volumes, the packaging metrics limiting the degree of packaging of the clinical volumes on the treatment image within the planning envelope volume on the treatment image.

[0032] Preferably, the processor is further configured to perform any one of the method steps described above.

[0033] In a preferred embodiment of the invention, the system further comprises a display for displaying at least one of: a planning image, a treatment image, one or more clinical volumes and a planning envelope volume.

[0034] In a preferred embodiment of the present invention, a computer program product is also provided, which includes instructions. When the instructions are executed by a computer, the instructions enable the computer to perform the method steps described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Further details, aspects and embodiments of the present invention will be described by way of example only and with reference to the accompanying drawings. In the accompanying drawings, similar reference numerals are used to identify similar or functionally similar elements. The elements in the figures are shown for simplicity and clarity and are not necessarily drawn to scale.

[0036] Figure 1 A flow chart showing a known process for image-guided radiation therapy / adaptive radiation therapy;

[0037] Figure 2 A flow chart showing a method according to a first embodiment of the present invention;

[0038] Figure 3 A flow chart showing a method according to another embodiment of the present invention;

[0039] FIG4( a ) shows the clinical volume and the planning envelope volume on the planning image;

[0040] Figures 4(b) and (c) show the clinical volume and planning envelope volume on the treatment images;

[0041] 5(a), (b) and (c) illustrate determining a measurement metric based on the location of a clinical volume relative to the location of a planning envelope volume;

[0042] 6(a) to (c) illustrate a method for calculating a measurement metric according to an embodiment of the present invention;

[0043] Figure 7 is a graph of measurement metrics versus time for an embodiment of the present invention;

[0044] Figure 8(a) and 8(b) is a rendered 3D view of a clinical volume according to an embodiment of the present invention;

[0045] Fig. 9 is a 3D view of information related to measurement metrics for clinical volumes;

[0046] Fig.10 is a simplified block diagram of a medical imaging system arranged to display medical images to a user. DETAILED DESCRIPTION

[0047] The invention is a system for monitoring and reporting changes between medical images acquired at different times. For example, they can be images acquired during radiotherapy and planning. One possible application is to support IGRT (Image Guided Radiotherapy) and ART (Adaptive Radiotherapy) decision making, but the invention is not limited to this application. Other possible applications are deformable registration quality control, tracking disease progression or for other image-guided medical interventions where movement of a clinical volume across sequential images is required.

[0048] Fig.10A simplified block diagram of an example of a medical imaging system 1000 arranged to enable medical images to be displayed to a user is shown. In the example shown, the medical imaging system 1000 includes one or more user terminals 1001, such as workstations or the like, which are arranged to access medical images stored, for example, in a database 1002 or other data storage device. In the example shown, a single database 1002 is shown. However, it should be understood that the user terminal 1001 may be arranged to access medical images from more than one data storage device. Furthermore, in the example shown, the database 1002 is shown as being external to the user terminal 1001. However, it should be understood that the user terminal 1001 may also be arranged to access medical images stored locally in a local storage module shown in 1010 on one or more internal storage elements, such as a memory element shown in 1003 or a disk element shown in 1009. The user terminal 1001 also includes one or more signal processing modules, such as a signal processing module generally shown in 1004. The signal processing module is arranged to execute computer program code, for example, computer program code stored in the local storage module 1009. In the example shown, the signal processing module 1005 is arranged to execute computer program code including one or more of the patient change monitoring components, shown in this example as the metric calculation component 1005. The signal processing module 1004 in the example shown is also arranged to execute computer program code including one or more image display components 1006; the image display component 1006 is arranged to display any patient change metrics, such as those generated by the metric calculation component 1005, to a user in a meaningful manner, for example, on a display screen 1007 or the like. The medical imaging system 1000 may also include one or more user input devices, such as shown generally at 1008, to enable a user to interact with the computer program code or the like executed on the signal processing module 1004.

[0049] exist Figure 2 Now, in conjunction with the present invention, Figure 1 The process shown in Figure 1 100 . The process has two phases. A planning phase 201 and a treatment phase 202 that occur before the first radiation treatment. The treatment process remains essentially unchanged. Items 201-206 and 210 all correspond to the same items in FIG. 100 . In step 203 of the planning phase, a single or multiple clinical volumes are identified on a medical planning image of the subject. In an example of the present invention, the planning image and the treatment image are 3D images. Preferably, the planning image and the treatment image are CT, PET, SPECT or MRI images.

[0050] Some clinical volumes will correlate precisely with organs visible in the scan, but others may not, such as clinical target volumes that identify risk areas for cancer disease. In step 204, a planning envelope volume is generated around the single or multiple clinical volumes to account for potential movement of the clinical volumes during treatment. The number of planning envelope volumes may be different from the number of clinical volumes, for example because a planning envelope volume is not generated for each clinical volume, or because multiple clinical volumes may be combined into a single planning envelope volume. Subsequently, a radiation treatment plan is generated in step 205, using the planning envelope volumes of step 204 to optimize the dose delivered to the treatment target while keeping the dose to organs at risk below a set threshold.

[0051] The treatment phase 202 will be performed once or for multiple treatment fractions. Step 206 is performed for each treatment fraction or selected treatment fractions, wherein an imaging system integrated into the treatment machine acquires treatment images prior to treatment delivery, typically up to 15 minutes before treatment begins, while the patient remains in the treatment position during this time. Preferably, treatment images are acquired of the subject in a position corresponding to the position of the planning image, so that the treatment image will have the same planning envelope volume as the planning image.

[0052] The present invention supports user evaluation of images, making Figure 1 Step 107 in Figure 2 207-209 in . In step 207, the position of one or more clinical volumes on the treatment image is identified, preferably relative to the position of the planning envelope volume. There are various ways to perform the task of step 207, and the present invention is not dependent on a particular method and will work with any technically feasible method. For example, it is performed by determining the geometric relationship between the planning image and the treatment image and mapping the volume position. Another method is to manually or automatically identify the volume from the anatomical structure on the treatment image to locate the clinical volume. In step 208, the packaging metric between one or more clinical volumes on the treatment image and the associated planning envelope volume from the planning image is calculated, which will be further described below. Preferably, the packaging metric of one or more of the clinical volumes defines the degree of packaging of one or more clinical volumes on the treatment image within the planning envelope volume on the treatment image. This is displayed to the user in step 209. This view may take a variety of forms, combining any combination of patient images, clinical volumes and planning envelope volumes, metrics for this treatment session, trends in previous sessions, graphical representations of where problems have been detected, metrics organized by surface angles, or summary information in numerical or textual form. Some of these elements are further illustrated in Figures 700, 800 and 900.

[0053] exist Figure 3Another example of the method of the present invention is shown in FIG. Items 301-306 and 320 are all related to Figure 1 Corresponds to the same item in . Figure 2 As in , the positions of the clinical volumes on the treatment image are determined in step 307, and the encapsulation metric between each of the treatment clinical volumes and the associated planning envelope volume is calculated in step 308. In step 309, a correction to the treatment image position is determined that best encapsulates the clinical volume within the planning envelope volume and therefore minimizes the encapsulation metric. In the event that there are multiple encapsulation metrics to be optimized, any standard method can be used to manage multiple objectives in the optimization problem. For example, by setting priority rules by volume type or by weighting individual metrics and summing them to produce an overall score. In step 310, a separate encapsulation metric is calculated for the optimized treatment position. In step 311, if the best correction is performed, the encapsulation metric for the original patient position is reported to the user together with the potential encapsulation metric.

[0054] Figure 400 illustrates the concept of clinical volume and planning envelope volume in the context of planning and treatment. Figure 4(a) shows the situation in the planning image. We define a coordinate system with the origin at O, item 402. The location 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 machine. The location of the clinical volume in the planning scan is shown as A0, item 403. The planning envelope volume is shown as completely containing the clinical volume with a margin of B, item 404. Figure 4(b) shows the situation in one of the treatment fractions, called i. The period between treatment fractions typically varies between six hours and one week. Since the treatment is defined relative to the machine geometry, the origin, item 406 and the location of the planning envelope volume, item 408 are constant for each treatment fraction. However, the patient will typically not be located or positioned within the machine geometry in exactly the same position as during the planning phase, so the location of the clinical volume will change, as shown in Ai, item 407. In this example of the invention, the clinical volume has been moved relative to the axis to the upper right quadrant, but remains within the boundaries of the planning envelope volume. FIG4( c) shows the situation in different fractions in the treatment fractionation. Again, the origin, item 410, and the planning envelope volume, item 411 remain unchanged. The clinical volume, item 412 in this example has not only been moved relative to the planning envelope volume, but has also been deformed compared to the original shape it had during the planning stage, item 403. The combination of movement and shape change results in a portion of the clinical volume moving out of the space defined by the planning envelope volume, shown by item 413.

[0055] The packing metric used to quantify the extent to which a clinical volume is within an associated planning envelope volume is defined as follows: For one or more points on the clinical volume, the shortest distance to the planning envelope volume is determined. In one example of the invention, if the point on the clinical volume is inside the planning envelope volume, the distance is given a positive sign. If the point on the clinical volume is outside the planning envelope volume, the distance is given a negative sign. The overall packing metric is then the minimum of the one or more signed shortest distances.

[0056] An example of an encapsulation metric is shown in Figure 5. Figure 5(a) shows a portion of a clinical volume, item 502, surrounded by a portion of a planning envelope volume, item 503. A representative point on the surface of the clinical volume is represented by item 504. A series of distances from a representative point on the clinical volume 504 to the planning envelope volume 503 are shown as arrows of varying lengths, item 505. The shortest distance from a representative point on the clinical volume 504 to the planning envelope volume 503 is shown by a thick arrow, item 506.

[0057] FIG5( b), 507 shows a situation where the clinical volume 509 is still completely surrounded by the planning envelope volume 508. One or more representative points are specified 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 planning envelope volume is determined, as shown by a thin arrow 510. In one example of the present invention, when one or more representative points on the clinical volume are located within the planning envelope volume, the distance is classified as a positive or negative internal distance, and when one or more representative points on the clinical volume are located outside the planning envelope volume, the distance is an external distance, which is classified with an opposite sign to the internal distance. The encapsulation metric value is determined as: when the internal distance is classified as positive, the minimum value of the signed shortest distance value in the distance of the one or more representative points, or when the internal distance is classified as negative, the maximum value of the signed shortest distance in the distance of the one or more representative points.

[0058] In this case, as shown in FIG5( b ), since the clinical volume is completely surrounded by the planning envelope volume, each distance from the clinical volume to the planning envelope volume will have a positive value, wherein the minimum value, which will be reported as the encapsulation metric, is shown by the thick arrow 511. All other arrows in the figure are longer than the arrow indicated at 511.

[0059] FIG. 5( c ), 512 shows a situation where a portion 513 of the clinical volume has moved beyond the planned envelope volume 514. In this example of the present invention, a portion 520 of the clinical volume is still within the planned envelope volume, and a portion 521 of the clinical volume is now outside the planned envelope volume. For each point on the clinical volume (located outside the planned envelope volume and inside the planned envelope volume), the shortest distance to the planned envelope volume is determined. For those points on the portion 520 of the clinical volume that are still within the planned envelope volume, the distance is assigned a positive value, as shown by the closed arrow head 515. For those points on the area 521 of the clinical volume that fall outside the planned envelope volume, the distance is assigned a negative value, as shown by the open arrow 516. The reported encapsulation metric is the minimum of the signed shortest distances, as shown by the thick arrow 517. In this case, in this example of the present invention, the encapsulation metric will also have a negative value because the shortest distance is between the PEV and a portion of the CV outside the PEV.

[0060] In an example of the invention, after the planning envelope volume has been defined, a distance transform is calculated around the planning envelope volume and this is used to determine the encapsulation metric. Alternatively, the encapsulation metric is determined by simulating a translation of one or more treatment clinical volumes relative to the planning envelope volume. In an embodiment of the invention, the simulated translation is a linear translation.

[0061] An example of a method for efficiently calculating package metrics is shown in Figure 6. Figure 6(a) shows the calculation during the radiation therapy planning stage 601. Once the PEV 602 has been determined (represented by the dotted line), a signed distance transform 603 is calculated, with the legend being 604. The signed distance transform will be a three-dimensional matrix of signed distance values. In this example, the matrix values ​​are represented by a series of 5mm wide radial segments. The segment at the center of the image is +25 to +30mm from the PEV. This is surrounded by radial segments of +20 to +25mm. The image has a series of concentric 5mm segments, from +5mm to 0mm at the inner boundary of the PEV, 0 to -5mm on the outside of the PEV, to -10 to -15mm on the edge of the image farthest from the PEV.

[0062] The encapsulation metric may be determined by calculating a signed distance transform using any method detailed in the scientific literature, such as the method of Borgefors [1]. For any point in the treatment coordinate system, the signed distance transform shows the shortest distance from the clinical volume to the PEV. In one example of the present invention, the value is positive if the point is within the PEV (as shown in FIG5(a)), and negative if the point is outside the PEV (as shown in FIG5(b)).

[0063] The radiotherapy stage is shown in Figure 6 (b). The signed distance transform 603 from the planning stage is directly copied into the treatment space 606. That is, a three-dimensional matrix of signed distance values ​​is mapped to the treatment space, represented in the figure by a series of radial segments 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 present invention, the clinical volume is contained within the planning envelope volume, so for this example of the present invention, the encapsulation metric will have a positive value. As shown, the clinical volume is offset to the upper right quadrant relative to the origin of the original planning envelope volume. In view of this and the position of the clinical volume 607 on the treatment image, the minimum of these signed shortest distance values ​​on the clinical volume boundary can be found by any means. A common method is to perform an exhaustive search along all boundary points of the clinical volume, but more efficient algorithms such as priority queues or sorting algorithms are also feasible. Alternatively, a convention can be applied where the distance transform assigns positive values ​​to the portion of the CV that is outside the PEV and negative values ​​to the portion of the CV that is inside the PEV (the opposite of the convention used in FIG5 ). In this case, the packing metric will be the maximum of the signed shortest distance values ​​on the CV boundary, rather than the minimum. This is illustrated in FIG6( c ).

[0064] The encapsulation metric of the present invention can be calculated using 3D or 2D geometry. In the 3D case, the encapsulation metric will be sensitive to volume changes that occur in any direction. An example of using a 3D case is that the tracked clinical volume is the target clinical target volume and movement in any direction will be clinically significant. In the 2D case, the encapsulation metric will be insensitive to volume changes that occur on one image axis. An example of using a 2D case is that the spinal cord is the tracked clinical volume, but it extends above and below the area of ​​the treatment image. In this case, it may be better if the encapsulation metric is insensitive to changes in the head and tail direction because 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 transform 603. To generate a 3D encapsulation metric, the signed distance transform will describe the distance from the clinical volume to the planning envelope volume in any direction. To generate a 2D encapsulation metric, the signed distance transform will describe the distance from the clinical volume to the planning envelope volume in any direction only in the 2D plane perpendicular to the excluded direction. A further extension of the invention is the clipping region, where the metric is calculated to avoid regions that may produce incorrect results, such as at the very top and bottom of a planning or treatment image sequence, or to remove regions far from the treatment region.

[0065] An alternative but less efficient method of calculating the minimum margin encapsulation metric is to simulate a large number of linear translations of the treatment clinical volume and identify the magnitude of the linear translation required for each clinical volume to break through the associated linked planning envelope volume.

[0066] In an example of the present invention, the planning envelope volume is defined as the clinical volume plus a margin specified by a predetermined protocol. Preferably, the clinical volume is the clinical target volume or the total tumor volume for treatment planning. Alternatively, the clinical volume represents organs at risk to be avoided during treatment.

[0067] A key advantage of the minimum margin encapsulation metric over other metrics previously reported is that encapsulation is signed, so that movement of the clinical volume outside the planning envelope volume (negative values ​​of the encapsulation metric) appears different from movement of the clinical volume inside the planning envelope volume (positive values ​​of the encapsulation metric). In an example of the present invention, the method also includes a step of displaying one or more determined encapsulation metrics to a system user. The principle of displaying encapsulation metrics to a user can be extended to give greater insight and flexibility. Another embodiment of the present invention uses encapsulation metrics to track patient changes. In an example of the present invention, the steps of the method can be repeated over a set time period to monitor changes in encapsulation metrics on treatment images. Preferably, the set time period can vary between 1 day and 3 months. In some examples of the present invention, changes in encapsulation metrics are displayed to the user. To this end, a change tracking view is shown in Figure 700, which plots encapsulation metrics 701, preferably for each day of treatment 703, although other time intervals can also be used. In this figure, a threshold of interest 702 can be displayed, where an encapsulation metric value exceeding a preset threshold indicates an unsafe amount of movement of the subject in the scanner. The thresholds can be global values ​​for each planning envelope volume or separate different values ​​for each planning envelope volume. Each threshold can be set in a variety of ways: as a user input value, as an organ-specific standard value, or determined by an algorithm based on any combination of location, size, radiation dose tolerance or target, treatment technique, and proximity to other clinical volumes or planning envelope volumes. In this example, in this case, point 704 shows a downward trend, where the clinical volume moves out of the planning envelope volume on the sixth day of treatment. Other embodiments of a system that uses package metrics for change tracking may involve using package metrics as input to a decision support algorithm, such as an algorithm that autonomously monitors changes in package metrics based on statistical criteria and makes recommendations when statistically significant changes in behavior occur.

[0068] In one example of the present invention, while global package metrics are helpful to aid in immediate decision making and error tracking, other views can also help the user understand what is happening with the patient case. In yet another embodiment of the present invention, Figure 8(a) and 8(b)Two alternative 3D surface rendering views of a clinical volume 801 are shown. In FIG. 8( a), each cube 801 represents a portion of the clinical volume surface. The surface can be allocated according to a grid or net, preferably set by the user or set by an algorithm based on any combination of the patient's geometry, surface curvature and dose grid or image parameters. When the allocation is fine enough, the surface appears smooth and continuous to the user (see FIG. 8( b)). An orientation label 802 enables the user to relate the 3D rendered surface view to the patient's anatomy. In this example of the present invention, the encapsulation metric is determined using the minimum signed distance, but it is also applicable to the encapsulation metric determined as the maximum distance as described above. The representation of each element of the surface varies in various ways, such as color, transparency, texture or label, according to the shortest distance to the planned envelope volume at that location on the clinical volume surface as shown in the legend 803 (or the maximum distance if an alternative method of calculating the metric is used). An example of such a representation is a heat map, in which low values ​​of the metric are displayed hotter. Users can configure any other color scheme they find more useful. This allows the user to quickly identify the area of ​​most concern 804. FIG8( b ) shows a 3D surface image of the volume, rather than a cubic version.

[0069] The only tool the user has to immediately correct any patient position issues as highlighted by determining the package metrics is to move the patient table, which may facilitate only linear translation. In examples of the present invention, the subject may be in a specified position in the scanner for planning and treatment images, and the system user may adjust the subject's position within the scanner based on one or more determined package metrics.

[0070] Therefore, another embodiment calculates the minimum margin metric by direction and displays this information to the user. One method to achieve this is to assign each point on the clinical volume surface 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 based on the signed distance transform 603. There are other standard methods in computer graphics to determine the 3D angle of the surface orientation. The continuous 3D angle values ​​for all points on the surface are then binned into discrete orientations based on predetermined limits. In calculating the package metric, the points on the CV surface that fall into each orientation bin are processed independently of each other, thereby producing a separate package metric value for each orientation.

[0071] Another way to implement encapsulation metrics by direction is by simulating the patient position in each direction and reporting the distance moved in each direction before the clinical volume breaks through the planned envelope volume. This will suggest to the user whether the examination table movement can be achieved in any direction without causing the clinical volume to move out of the planned envelope volume. This directional minimum margin metric can be displayed to the user in a variety of forms, such as a simple table using 2D or 3D directional arrows with a length equal to the metric value, or a 2D or 3D representation using a CV shape with an indicator on the edge corresponding to each direction. An example of how this information can be displayed is shown in Figure 900. A 3D view is shown with an indicator 901 showing the minimum margin metric in each direction. The direction is provided by the orientation label 902. As before, the color of the indicator shows the minimum margin in each direction, as described in the legend 903. In this case, the user sees that the margin metric in the backward direction 904 is slightly worse than in the forward direction 905, and the situation can be improved with a forward movement of the examination table. [1] Borgefors, On the three-dimensional digital distance transform, Computer Vision and Image Understanding, 1996, 64(3); 368-376

[0072] Other image-guided interventional procedures involve tracking the patient's anatomy over time.

[0073] Examples of the present invention may be applied to any or all of the following: picture archiving and communication systems (PACS); advanced visualization workstations; imaging acquisition workstations; network-based or cloud-based medical information and imaging systems; radiation therapy planning systems (TPS); radiation therapy linear accelerator consoles; radiation therapy mass beam consoles.

[0074] The present invention has been described with reference to the accompanying drawings. However, it should be understood that the present invention is not limited to the specific examples described herein and shown in the accompanying drawings. In addition, since the embodiments shown in the present invention can be implemented using electronic components and circuits known to those skilled in the art for the most part, in order to understand and appreciate the underlying concepts of the present invention, and in order not to confuse or deviate from the teachings of the present invention, the details will not be explained to a greater extent than that shown above that is deemed necessary.

[0075] The present invention can be implemented as a computer program running on a computer system, which computer program includes at least a code portion for executing the steps of the method according to the present invention or enabling the programmable device to perform the functions of the apparatus or system according to the present invention when running on a programmable device, such as a computer system.

[0076] A computer program is a sequence of instructions, such as a specific application and / or operating system. A computer program may, for example, include one or more of the following: a subroutine, a function, a procedure, an object method, an object implementation, an executable application, an applet, a service program, source code, object code, a shared library / dynamically loaded library, and / or other sequence of instructions designed for execution on a computer system. Thus, some examples describe a non-transitory computer program product having executable program code stored therein for automatically outlining a cone beam CT image.

[0077] The computer program may be stored internally on a tangible and non-transitory computer-readable storage medium or transmitted to a computer system via a computer-readable transmission medium. All or some of the computer program may be provided on a computer-readable medium that is permanently, removably or remotely connected to an information processing system. Tangible and non-transitory computer-readable media may include, for example but not limited to, any number of the following media: magnetic storage media, including disk and tape storage media; optical storage media, such as optical disk media (e.g., CDROM, CDR, etc.) and digital video disk storage media; non-volatile memory storage media, including semiconductor-based memory cells, such as FLASH memory, EEPROM, EPROM, ROM; ferromagnetic digital memory; MRAM; volatile storage media, including registers, buffers or caches, main memory, RAM, etc.

[0078] A computer process typically consists of a program or portion of a program being executed (running), current program values ​​and state information, and resources used by an 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 an interface for accessing those resources. An operating system processes system data and user input, and responds as a service to the users and programs of the system by allocating and managing tasks and internal system resources.

[0079] A computer system may, for example, include at least one processing unit, associated memory, and a plurality of input / output (I / O) devices. When executing a computer program, the computer system processes information according to the computer program and generates resultant output information via the I / O devices.

[0080] In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. However, it will be apparent that various modifications and changes may be made to the invention without departing from the scope set forth in the appended claims, and the claims are not limited to the specific examples described above.

[0081] 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 depicted herein are merely exemplary, and that in fact many other architectures that achieve the same functionality may be implemented.

[0082] Any arrangement of components for achieving the same function is effectively "associated" together so that the desired function is achieved. Thus, any two components combined to achieve a particular function herein may be considered "associated" with each other so that the desired function is achieved, regardless of the architecture or intermediate components. Likewise, any two components so associated may also be considered "operably connected" or "operably coupled" to each other so as to achieve the desired function.

[0083] Furthermore, those skilled in the art will recognize that the boundaries between the above operations are merely illustrative. Multiple operations may be combined into a single operation, a single operation may be distributed in additional operations, and operations may be performed at least partially overlapping in time. Furthermore, alternative embodiments may include multiple instances of a particular operation, and in various other embodiments, the order of the operations may change.

[0084] However, other modifications, changes, and substitutions are possible. Accordingly, the specification and drawings are to be regarded as illustrative rather than restrictive.

[0085] In the claims, any reference symbols placed in brackets shall not be interpreted as limiting the claims. The word "comprising" does not exclude the presence of other elements or steps other than those listed in the claims. In addition, the term "one" or "an" as used herein is defined as one or more. Moreover, the use of introductory phrases in the claims, such as "at least one" and "one or more", should not be interpreted as implying that any particular claim containing such introduced claim elements is limited to an invention containing only one such element by introducing another claim element by the indefinite article "one" or "an", even when the same claim includes the introductory phrase "one or more" or "at least one" and an indefinite article, such as "one" or "an". The same is true for the use of definite articles. Unless otherwise specified, terms such as "first" and "second" are used to arbitrarily distinguish the elements described by such terms. Therefore, these terms are not necessarily intended to indicate the time or other priority of these elements. The fact that certain measures are described in mutually different claims does not indicate that the combination of these measures cannot be used to highlight the advantages.

Claims

1. A method for determining changes between a planning image and a treatment image of a subject, comprising the steps of: defining one of a plurality of clinical volumes on the planning image of the subject and defining a planning envelope volume for the planning image around the clinical volume; acquiring, from the subject, a treatment image for a location corresponding to the location of the planning image; wherein the treatment image will have the same planning envelope volume as the planning image; determining a position of the one or more clinical volumes on the treatment image relative to the planning envelope volume; A packing metric is determined for one or more of the clinical volumes, the packing metric defining a degree of packing of the clinical volume on the treatment image within the planning envelope volume on the treatment image.

2. The method of claim 1 , wherein determining the packaging metric comprises the steps of: specifying one or more representative points on a surface of the clinical volume; determining the shortest distance from the one or more representative points of the clinical volume to the planning envelope volume; wherein when the representative point on the clinical volume is within the planning envelope volume, the shortest distance is classified as a positive or negative internal distance, and when the representative point on the clinical volume is outside the planning envelope volume, the shortest distance is an external distance, which is classified with an opposite sign to the internal distance; And the encapsulation metric value is determined as: when the internal distance is classified as positive, the minimum value of the signed shortest distance among the distances of the one or more representative points, or when the internal distance is classified as negative, the maximum value of the signed shortest distance among the distances of the one or more representative points.

3. The method of claim 1 or claim 2, wherein the planning image and the treatment image are 3D images.

4. The method of claim 3, wherein the planning images and treatment images are CT, PET, SPECT or MRI images.

5. A method according to any preceding claim, wherein after the planning envelope volume has been defined, a distance transform is calculated around the planning envelope volume and this is used to determine the packaging metric.

6. The method of any one of claims 1 to 4, wherein the encapsulation metric is determined by simulating a translation of the one or more treatment clinical volumes relative to the planning envelope volume. The method of claim 6 , wherein the simulated translation is a linear translation.

8. A method according to any preceding claim, wherein determining the position of the one or more clinical volumes on the treatment image relative to the planning envelope volume comprises one or more of the following: determining a geometric relationship between the planning image and the treatment image and mapping the clinical volume positions to the treatment image; identifying anatomical features on the treatment image to locate the clinical volumes.

9. A method according to any preceding claim, wherein the planning envelope volume is defined as the clinical volume plus a margin specified by a predetermined protocol.

10. The method according to any preceding claim, wherein the clinical volume is a clinical target volume or a total tumor volume for treatment planning.

11. The method according to any one of claims 1 to 9, wherein the clinical volume represents an organ at risk to be avoided in treatment.

12. A method according to any preceding claim, wherein the steps are repeated over a set period of time to monitor changes in the encapsulation metric across the treatment image.

13. The method according to claim 12, wherein the set time period varies between 6 hours and 3 months.

14. A method according to claim 12 or 13, wherein said change in said packaging metric over time is displayed to a user.

15. A method according to any preceding claim, wherein the subject is in a specified position in a scanner for the planning image and the treatment image, and the system user can adjust the position of the subject based on the one or more determined packaging metrics.

16. A method according to claim 15, when dependent on claim 14, wherein the change in the packaging metric over time is displayed to a user, wherein the display also shows when the packaging metric exceeds a preset threshold, exceeding the preset threshold indicating unsafe movement of the subject in the scanner.

17. A method according to any preceding claim, further comprising the step of displaying the one or more determined packaging metrics to a system user.

18. A method according to any preceding claim, further comprising the steps of: The packaging metrics are used to determine a 3D representation that displays a movement margin showing how a subject may move in a scanner while maintaining or improving the position of the clinical volume relative to the position of the planning envelope volume.

19. A method according to any preceding claim, wherein the clinical volume is displayed as a 3D rendered surface, wherein the encapsulation metric for the surface is indicated as a heat map on the surface.

20. A system for analyzing medical images to determine changes between planning images and treatment images of a subject, comprising: A processor, the processor being configured to: determining one or more clinical volumes on a planning image of the subject and defining a planning envelope volume for the planning image around the clinical volumes; analyzing a treatment image from the subject for a location corresponding to the location of the planning image; wherein the treatment image will have the same planning envelope volume as the planning image; determining a position of the one or more clinical volumes on the treatment image relative to the planning envelope volume; A packing metric is determined for one or more of the clinical volumes, the packing metric defining a degree of packing of the clinical volume on the treatment image within the planning envelope volume on the treatment image.

21. The system of claim 20, wherein the processor is further configured to perform the method steps of any one of claims 2 to 19.

22. The system of claim 21 or 22, further comprising a display for displaying at least one of: the planning image, the treatment image, the one or more clinical volumes, and a planning envelope volume.

23. A computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 19.

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