Methods and apparatus for deriving and utilizing virtual volumetric structures to predict potential collisions during radiation therapy.

Abstract

A control circuit (101) accesses (401) a tomographic localization film (103) of a patient, the tomographic localization film (103) comprising patient content presented in a portion of the patient beyond the portion presented in a three-dimensional computed tomographic (CT) image of the patient. The control circuit uses those tomographic localization films to derive (403) a virtual volumetric structure representing at least some of the patient content presented in the 3D CT image beyond the aforementioned portion of the patient. This virtual volumetric structure can then be used (404) to predict potential collisions when evaluating a radiotherapy plan for a patient utilizing the aforementioned radiotherapy platform. By one method, the tomographic localization film comprises at least two substantially orthogonal views of the aforementioned patient content.

Description

[0001] Related applications

[0002] This application relates to two jointly pending and jointly owned U.S. patent applications entitled “Automatically Planned Radiation-Based Treatment” (Attorney’s File No. 8632-144155-US (2018-078)) and “Automatically Registered Patient Fixation Device Images” (Attorney’s File No. 8632-144156-US (2018-061)), both filed on the same date as this application, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] These teachings generally concern the use of radiation as a method of treatment, and more specifically, the avoidance of collisions during the administration of radiation for treatment. Background Technology

[0004] The use of radiation to treat medical conditions encompasses known areas of effort in current technology. For example, radiation therapy includes several important components of treatments aimed at reducing or eliminating harmful tumors. Unfortunately, the radiation applied cannot inherently distinguish harmful substances from adjacent tissues, organs, or similar substances desired or even critical for prolonging a patient's survival. As a result, radiation is typically applied in a cautious manner to at least attempt to confine the radiation to a given target volume. So-called radiation therapy planning is used in the aforementioned contexts.

[0005] A treatment plan is used to specify any number of operational parameters relevant to the implementation of the treatment for a given patient and a specific real-world physical radiotherapy platform. This treatment plan is often optimized before use. (As used herein, "optimization" will be understood as referring to improvements on candidate treatment plans without ensuring that the optimized result is actually a uniquely optimal solution.) Multiple optimization methods employ an auto-incrementing methodology that uses various automatically modified (i.e., "incremental") treatment optimization parameters to sequentially calculate and test various optimization results.

[0006] Radiation therapy planning workflows typically involve multiple manual and iterative steps. This is especially true when using backward programming-based techniques such as IMRT and VMAT. Each of the decisions / choices made in a series of steps can often influence later stages. This factor can lead to overly complex planning workflows when only a simple 3-D plan is required (e.g., developing a treatment plan for breast cancer). In particular, physicians must often manually delineate target structures and organs of risk through a time-consuming process, which is also susceptible to observer variability. This variability is exacerbated by the manually defined domain setup and appropriate target definition for the optimizer that must be established before the optimization process begins.

[0007] Because exploring all or most possible combinations often involves a time-consuming and computationally expensive process, in some application settings, users can choose to utilize simpler methods or readily available, previously known practices. However, making this choice can significantly limit the options available and suitable for a particular patient.

[0008] Other limitations have also been found in the prior art. For example, many radiotherapy platform application setups include at least one component that physically moves relative to the patient receiving treatment. This movement, in turn, increases the chance of collisions, such as between the patient and the moving component. Some prior art systems utilize dedicated optical systems (such as calibrated cameras) to scan the surface of the patient's treatment area to obtain images of the patient's surface. These surface images are then registered as planning images (i.e., 3D CT images) to provide information that can be used to predict potential collisions that might occur during the implementation of a specific radiotherapy plan. While useful, this technology requires additional hardware, software, training, and maintenance beyond what is typically expected in radiotherapy platform application setups. Summary of the Invention

[0009] In one aspect, the present invention provides an apparatus according to an embodiment.

[0010] In another aspect, the apparatus provided by the present invention includes: a computed tomography (CT) apparatus configured to provide CT images of a portion of a patient and a tomographic topogram of the patient including patient content other than the portion of the patient; and control circuitry configured to access the tomographic topogram of the patient; use the tomographic topogram to derive a virtual volumetric structure representing at least the patient content other than the portion of the patient; and use the virtual volumetric structure to predict potential collisions when evaluating a radiotherapy plan for the patient utilizing a particular radiotherapy platform.

[0011] The patient's tomographic localization film may also include images of at least one fixation device. The tomographic localization film may include at least a portion of the patient fixation device. Further optional features of the device are defined in the referenced embodiments.

[0012] In a further aspect, the invention provides a method for use with a radiotherapy platform, as defined in the embodiments. Further optional steps of the method are defined in the embodiments. Attached Figure Description

[0013] The above needs are at least partially met by providing a method and apparatus for deriving and utilizing virtual volumetric structures for predicting potential collisions during the radiation administration of the treatments described in the following detailed description, and are particularly studied in conjunction with the accompanying drawings, wherein:

[0014] Figure 1 Including block diagrams configured according to various embodiments of these teachings;

[0015] Figure 2 Includes flowcharts of various embodiments configured according to these teachings;

[0016] Figure 3 Includes flowcharts of various embodiments configured according to these teachings;

[0017] Figure 4 Includes flowcharts of various embodiments configured according to these teachings;

[0018] Figure 5 Includes schematic perspective views of various embodiments configured according to these teachings;

[0019] Figure 6 Includes schematic perspective views of various embodiments configured according to these teachings; and

[0020] Figure 7 Includes flowcharts of various embodiments configured according to these teachings;

[0021] The elements in the figures are shown for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions and / or relative positions of elements relative to other elements may be exaggerated to aid in understanding the various embodiments of this teaching. Furthermore, common but well-understood elements that are useful or necessary in commercially viable embodiments are often not depicted to facilitate a less obstructed view of the various embodiments of this teaching. An action and / or step may be described or depicted in a particular order of occurrence, and those skilled in the art will understand that such particularity in the order is not actually necessary. The terms and expressions used herein have the ordinary technical meaning consistent with those of those skilled in the art, unless otherwise specified herein. Unless otherwise specifically indicated, the word “or” as used herein should be interpreted as having a separating grammatical structure rather than a connecting grammatical structure. Detailed Implementation

[0022] In some application setups, the radiotherapy platform includes at least one component that is physically movable relative to the patient and / or patient fixation equipment while implementing a radiotherapy plan. In this case, and by a method, control circuitry can be configured to access tomographic slices of the patient, which include patient content (and also at least a portion of the patient fixation equipment by a method), the patient content being outside of portions of the patient (such as the patient's treatment volume and surrounding tissues) presented in a three-dimensional computed tomographic (CT) image of the patient. The control circuitry uses those tomographic slices to derive a virtual volumetric structure, which at least represents some of the patient content presented in the 3D CT image outside the aforementioned portions of the patient. This virtual volumetric structure can then be used to predict potential collisions when evaluating a radiotherapy plan for a patient using the aforementioned radiotherapy platform.

[0023] By a method, the control circuitry accesses at least two substantially orthogonal views of the aforementioned patient contents. As an illustrative example in these respects, one tomographic localization film may provide an anterior-posterior view of the patient, while another tomographic localization film provides a lateral view of the patient. These teachings will also apply to the use of more than two tomographic localization films in these respects, with a method in which only two tomographic localization films are used for the purposes described.

[0024] This configuration, and through a method, facilitates the prediction of potential collisions when implementing radiotherapy plans for specific patients using a particular radiotherapy platform, even when available 3D CT images for the patient are insufficient to provide sufficient information and the use of imaging devices other than the already available CT equipment is not required. Tomographic localization films are often used for other purposes, and therefore suitable tomographic localization films for these purposes can often be partially or even entirely available, without requiring additional image capture activities. Thus, these teachings can minimize the use of other equipment and / or significant additional image capture activities, while still achieving collision avoidance in even complex application settings.

[0025] In conjunction with or in lieu of the foregoing, these teachings will also help provide automated planning of radiation-based treatment volumes for specific patient treatment volumes. As a particularly significant example in these respects, this could include automated planning of radiation-based treatment volumes within a specific patient's breast tissue. As a useful illustrative example, the use of control circuitry facilitates the implementation of several or all of the activities supported by the foregoing.

[0026] One approach provides access to imaging information for a treatment region, which includes the treatment volume of a specific patient. Control circuitry can then use this imaging information, along with deep learning, to automatically segment at least some breast tissue (and possibly the heart) of a specific patient, and non-deep learning to automatically segment at least parts of some organs of danger (such as, but not limited to, the lungs, parts of the spine, and parts of the chest wall) to provide automatically segmented patient content. Atlas-based and model-based approaches are examples of two non-deep learning methods. Atlas-based segmentation assumes that a given patient image can be segmented by propagating the structure from a manually segmented atlas. One or more deformable image registration algorithms are used to deform the atlas image to match the patient image, and the structure is propagated using a deformable vector field that maps voxels from the atlas image to voxels in the patient image. Model-based structural segmentation depicts the structure by directly detecting edges and points on the patient image. Various image processing techniques are frequently used in these approaches. Manually defining the volume is suitable for the user in many cases. Typically, these methods combine deep learning and density with heuristic search algorithms. The latter is sometimes used in existing treatment planning systems. However, the combination of density-based and heuristic tools with deep learning was previously unknown to the applicant.

[0027] The control circuitry can also use the imaging information to automatically determine a virtual skin volume corresponding to at least a portion of the aforementioned breast tissue (specifically, the skin therein). By means of a method, when automatically optimizing a radiotherapy plan for the aforementioned treatment volume of a particular patient, the control circuitry uses automatically segmented patient content and the virtual skin volume as input to provide an optimized radiotherapy plan for that particular patient.

[0028] In one method, the aforementioned imaging information includes three-dimensional computed tomography (CT) imaging information. In another method, the aforementioned imaging information may further include two-dimensional tomographic localization film imaging information. In this case, when the control circuit uses the imaging information to automatically determine the virtual skin volume corresponding to at least a portion of the breast tissue of a specific patient, this may at least partially include using both three-dimensional CT imaging information and two-dimensional orthogonal tomographic localization film imaging information to determine the virtual skin volume.

[0029] One approach allows the control circuitry to automatically segment cardiac tissue from at least some specific patients using imaging information combined with deep learning (as opposed to non-deep learning). Overall, the inventors have identified some anatomical structures such as the lungs, spinal cord, bones, and eye structures that can be easily and accurately segmented using standard non-deep learning methods, while other structures, especially those without clearly defined boundaries, are more problematic. In the latter case, deep learning-based methods can produce better results faster.

[0030] In various radiotherapy application settings, the patient is supported by a patient support surface (such as, but not limited to, a treatment bed). In some cases, a patient fixation device may also be used in place of or in combination with the aforementioned to spatially fix parts of the patient's body, thereby maintaining the body parts in a relatively stable position / orientation. With this in mind, these teachings may further include, through a method, automatically registering at least one of the patient support surface and the patient fixation device to at least some of the imaging information in the imaging information, to provide registration information and use that registration information as further input when automatically optimizing the aforementioned radiotherapy plan. As a non-limiting illustrative example of these aspects, the foregoing may include automatically registering a model of at least one of the patient support surface and the patient fixation device to at least some of the imaging information in the imaging information.

[0031] By means of a method, these teachings will be further applied to automatically determining the body shape of at least a portion of a patient using the aforementioned imaging information. For example, in this case, control circuitry can use the body shape, the aforementioned virtual skin volume, and the registration information described above as inputs to automatically calculate the trajectory of the radiotherapy platform, collision detection information, and virtual rehearsal information for treatment delivery according to the optimized radiotherapy plan.

[0032] These and other benefits become clearer after a comprehensive review and study of the detailed description below.

[0033] However, before describing the foregoing teachings in more detail, it may be helpful to the reader to first provide a general description of an example of a current reverse planning workflow for treating breast cancer. This example will help illustrate at least some current processes that, while relying on a degree of automation, must frequently invoke human intervention, subjective judgment, and supervision.

[0034] The current example of a reverse planning workflow begins with a CT simulation. This simulation can start with a scan including a chest panel (which may include triangulation or leveling of the patient), outlines of the boundaries, and / or a wireframe of the entire breast tissue. Technicians then use CT simulation software to create the necessary 3D images and outlines.

[0035] The technician (or another technician) working on a properly equipped contouring workstation imports the aforementioned CT / body content imaging information and automatically contours it. (Volume-modulated arc radiotherapy (VMAT) users can also manually expand the body image to fit the "skin flash" during optimization.) This contouring may include contouring both the left and right lungs, the heart, the contralateral breast, the spinal cord, the lymph node chain, and the patient target volume (PTV).

[0036] Technicians can also add bolus content if they wish to increase the skin dose or as a workaround for skin flashes. (For VMAT, users can add bolus content during optimization to achieve skin flashes and then remove the bolus content before dose calculation.) The latter activity can include defining both the thickness and shape of the bolus (e.g., by selecting a predefined shape or creating a custom shape, for example, one designed to cover the entire irradiated area). (In radiotherapy, a bolus is a substance that, when irradiated, has properties equivalent to a given tissue such as breast tissue. Bolus content is frequently used to reduce or alter the dose targeted to a radiotherapy goal. For example, bolus content can be used to compensate for missing or irregularly shaped tissue and / or modify the radiation dose at the skin.)

[0037] The results of the profiling process are then transferred from the profiling process to the external beam planning workbench and the corresponding technician. The external beam planning process typically creates a forward plan (i.e., a baseline dose plan) that includes calculated and normalized parameters for multiple treatment areas. In some cases, this activity may also include converting isodose lines into corresponding structures to obtain an optimized patient treatment volume. The specific calculations and steps for the external beam planning will vary in part depending on whether the plan corresponds to an IMRT plan or a VMAT plan. In some cases, the planning process may also include using a skin flash tool to add flash to one or more areas.

[0038] External beam planning processes typically work in conjunction with corresponding optimization processes to iteratively calculate the implemented dose. In any case, the resulting plan is subsequently evaluated by an external beam planning (EB) or plan evaluation (PE) workstation. This evaluation may include assessing the location of the minimum and maximum doses achieved by the plan, the monitoring unit (MU) used (a monitoring unit is a measurement of machine output from a clinical accelerator, such as a linear accelerator, used for radiotherapy).

[0039] Filled with platform delivery and frequent or occasional continuous human involvement, this entire process, while often yielding useful results, is time-consuming, subject to human vulnerability, and inherently fails to realize the potential synergy of its constituent activities.

[0040] Now refer to the attached diagram, especially in Figure 1 In this context, an illustrative device 100 compatible with several of these teachings will now be presented.

[0041] In a particular example, the enabling device 100 includes a control circuit 101. As a “circuit”, the control circuit 101 therefore includes a structure comprising at least one (and typically multiple) conductive paths (e.g., including conductive metals such as copper or silver) that carry electrical power in an ordered manner, and these paths will also typically include corresponding electronic components (appropriately including passive (e.g., resistors and capacitors) and active (e.g., any various semiconductor-based devices)) to allow the circuit to control aspects of these teachings.

[0042] The control circuit 101 may include a fixed-purpose hardware platform (including, but not limited to, an application-specific integrated circuit (ASIC) (a custom integrated circuit designed for a specific purpose rather than intended for general use), a field-programmable gate array (FPGA), and the like), or may include a partially or fully programmable hardware platform (including, but not limited to, a microcontroller, a microprocessor, and the like). These architectural options for this structure are known and understood in the art and need not be described further herein. The control circuit 101 is positioned to (e.g., by means of corresponding programming that will be well understood by those skilled in the art) perform one or more steps, actions, and / or functions described herein.

[0043] In this illustrative example, control circuitry 101 is operatively coupled to memory 102. Memory 102 may be integrated into control circuitry 101 or may be physically separated from control circuitry 101 (whole or part) as needed. Memory 102 may also be local to control circuitry 101 (where, for example, both share a common circuit board, chassis, power supply, and / or housing), or may be remote relative to control circuitry 101 (where, for example, memory 102 is physically located in another facility, metropolitan area, or even country compared to control circuitry 101).

[0044] The memory 102 can be used, for example, to non-transitory store computer instructions that, when executed by the control circuit 101, cause the control circuit 101 to act as described herein. (As used herein, the reference to "non-transitory" will be understood to refer to the non-volatile state of the stored content (and therefore excludes when the stored content consists only of signals and waves), rather than the volatility of the storage medium itself, and therefore includes both non-volatile memories (such as read-only memory (ROM) and volatile memories (such as dynamic random access memory (DRAM)).

[0045] In this example, the memory 102 can also be used to store imaging information 103. This imaging information may include information related to an image (such as the "image" itself) of a treatment area for a patient, including a treatment volume 105 for a specific patient. For illustrative purposes, it will be assumed here that the treatment volume 105 is within the patient's breast tissue 104.

[0046] These teachings are flexible in practice and will be applicable to a variety of imaging information. For example, by one method, imaging information 103 includes three-dimensional computed tomography (CT) imaging information provided by a corresponding CT device 106. As another example, instead of or in combination with the foregoing, imaging information 103 may include two-dimensional tomographic imaging information provided by the CT device 106 or a correspondingly suitable imaging device 107. (Tomographic imaging is well understood in the art and is sometimes referred to as a sunt view or survey view; a tomographic image is a two-dimensional image generated by tomography without being reconstructed as a slice.)

[0047] It should also be understood that imaging information 103 may include one or more organs at risk for a particular patient (in Figure 1 The imaging information is provided by organs from the first dangerous organ 108 to the Nth dangerous organ 109 (where "N" is an integer greater than "1"). A dangerous organ is part or all of a non-target organ that is at risk of radiation injury when the treatment volume 105 is treated with radiation. Examples of such dangerous organs (when the treatment volume 105 is within breast tissue 104) include one or both lungs, part of the spine and part of the chest wall, and the heart.

[0048] Furthermore, it will be understood that the imaging information 103 may also include imaging information for the patient support device 110 (such as a so-called treatment bed) and / or one or more patient fixation devices 111 used to hold parts of the patient's body in a fixed position relative to the radiotherapy beam 112. (The term "patient fixation device" is sometimes used herein to refer to a patient fixation device or simply a fixation device.)

[0049] If desired, the control circuitry 101 can also be operatively coupled to a network interface (not shown). The control circuitry 101, thus configured, can communicate with other elements (both within and outside the device 100) via this network interface. Network interfaces, including both wireless and non-wireless platforms, are well understood in the art and do not require particular detailed description here.

[0050] Alternatively, the control circuit 101 can also be operatively coupled to a user interface (not shown) via another alternative method (instead of or in combination with the foregoing method). This user interface may include any variety of user input mechanisms (such as, but not limited to, keyboards and keypads, cursor control devices, touch-sensitive displays, voice recognition interfaces, gesture recognition interfaces, etc.) and / or user output mechanisms (such as, but not limited to, visual displays, audio transducers, printers, etc.) to facilitate the receipt of information and / or instructions from the user and / or the provision of information to the user.

[0051] As described in more detail below, control circuitry 101 is at least partially configured to optimize radiotherapy plans, thereby producing one or more optimized radiotherapy plans 113. For example, the optimized radiotherapy plan 113 is used to control a radiotherapy platform 114 that may include a radiation source 115, which may (if desired) be operatively coupled to and responsive to control circuitry 101. Radiation sources are well understood in the art and do not need to be further described herein.

[0052] With this configuration, the corresponding radiation beam 116 emitted by the radiation source 115 can be selectively turned on and off by the control circuit 101. These teachings will also apply to enabling the control circuit 101 to control the relative intensity of the radiation beam 116. The radiotherapy platform 114 can also be configured to move the radiation source 115 during the treatment phase, thus delivering radiation from various directions (“fields”). In this case, the control circuit 101 can also be configured to control this movement.

[0053] As an alternative approach, as illustrated herein, the radiotherapy platform 114 may further include one or more beamforming devices 117. For example, this device 117 is used to modify the radiation beam 116 by shaping it and / or otherwise modulating it, thereby producing a corresponding output radiation beam 112 to which the treatment volume 105 is exposed. Examples of known beamforming devices include, but are not limited to, clips, collimators, and multi-leaf collimators.

[0054] For illustrative purposes, this document assumes that the aforementioned control circuit 101, in conjunction with the application settings described above, implements at least some (and possibly all) of the actions, steps, and / or functions described herein. Figure 2 The process presented is consistent with much of what these teachings contain.200

[0055] At box 201, the process 200 provides access to imaging information (such as the aforementioned imaging information 103) for a treatment area including a treatment volume 105 for a specific patient. This illustrative example assumes that the treatment volume 105 is located within the patient's breast tissue 104. Again, for illustrative purposes, this example assumes that the imaging information 103 specifically includes three-dimensional CT imaging information and two-dimensional tomographic imaging information. In addition to the treatment volume 105 itself, the imaging information 103 may include imaging information that includes at least portions of one or more organs of danger 108, 109, such as the patient's lungs, portions of the patient's spine, and / or portions of the patient's chest wall. Furthermore, in suitable application settings, the imaging information 103 may include imaging information for one or more of the patient support device 110 and / or one or more patient fixation devices 111.

[0056] One method allows one or more items of imaging information 103 to be captured at particularly needed times (i.e., when preparing and optimizing a radiotherapy plan for a specific patient) and when the patient is on the radiotherapy platform 114 itself. Another method allows one or more items of imaging information 103 to be captured at an earlier time, and possibly when the patient is not on the radiotherapy platform 114 itself.

[0057] At box 202, the process 200 provides for the automatic segmentation of at least some breast tissue from a specific patient using imaging information 103 and deep learning, as well as the automatic segmentation of at least portions of some organs 108, 109 at risk using non-deep learning, to provide automatically segmented patient content. This process will also be applicable to the automatic segmentation of any high-density artifacts within the patient's body.

[0058] One method for this activity may include defining the patient's body shape. Alternatively, the activity may include automatically detecting and defining the location of lines and / or radiographic markings on the patient's body surface. Based on this detection and definition, the activity may include automatically removing specific content from 3D CT images.

[0059] Segmentation involves well-understood activities and includes the careful identification of specific organs or artifacts (and the outer edges of the structure), thereby allowing one organ (or artifact) to be distinguished from another.

[0060] CT images often exhibit low soft tissue contrast, and the overall appearance of the treated volume and the organs at risk often appears visually similar. The applicant has determined that deep learning techniques can provide useful results to support the automated segmentation of a patient's breast tissue, while non-deep learning techniques can provide useful results to support the automated segmentation of a patient's organs at risk. (That is, the applicant has also determined that utilizing imaging information 103 along with deep learning to automatically segment at least a portion of a specific patient's cardiac tissue is useful.)

[0061] Deep learning (sometimes also referred to as hierarchical learning, deep neural learning, or deep structural learning) is generally limited to a subset of machine learning in artificial intelligence. Deep learning features networks capable of unsupervised learning from unstructured or unlabeled data. That is, deep learning can also be supervised or semi-supervised if desired. Deep learning frameworks include deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks.

[0062] Deep learning uses multiple layers to progressively derive higher-level features from the raw input. In a typical configuration, each layer learns to transform its input data into a more abstract and complex representation. As a simple example, in image recognition applications, the initial raw input might be a matrix of pixels. The first representation layer extracts the pixels and encodes the edges, the second layer composes and encodes the arrangement of those edges, the third layer encodes specific features such as the nose and eyes, and the fourth layer identifies that the image contains a face. Overall, the deep learning process learns on its own which features are best placed in which layers.

[0063] One approach allows deep learning algorithms to be trained in a supervised learning setting using images and real-world contours of hundreds of patients. For example, useful data can be obtained from healthcare providers worldwide. The images in the training set can be selected to represent a realistic range of common anatomical artifacts. If desired, the real-world contours can be created by human anatomy experts as part of the algorithm development.

[0064] While expert-level performance can be achieved using traditional models and / or atlas-based algorithms for automatic segmentation, this performance is only achievable in a limited number of organs (therefore, further human-based editing is required before it can be clinically acceptable). The inventors have determined that, by comparison, the aforementioned deep learning-based approach can produce significantly better results, especially for structures without well-defined learning settings using images and real contours from hundreds of patients.

[0065] At box 203, the process 200 provides for automatically determining a virtual skin volume corresponding to at least a portion of the breast tissue 104 of a particular patient, also using imaging information 103. This activity may include, in a manner that utilizes at least part of the aforementioned three-dimensional CT imaging information and two-dimensional orthogonal tomographic localization image information, to determine the virtual skin volume. (As used herein, the expression "virtual skin volume" will be understood to refer to the patient volume formed by an external incision around the skin, calculated (and therefore "virtual").)

[0066] In typical radiotherapy application settings, the patient will lie or sit on one or more patient support devices 110, such as a treatment bed, chair, armrest, or similar. In conjunction with or instead of the foregoing, one or more parts of the patient may be held in a fixed position by one or more patient fixation devices 111. Patient fixation devices are used to hold parts of the patient in a fixed position during radiation administration to keep the treatment volume and / or organs at risk in a fixed position relative to the radiation source 115. Patient support devices and patient fixation devices are well understood in the art and do not require further detailed description herein.

[0067] Considering the foregoing, at optional box 204, the process 200 can provide registration information for at least one of the patient support surface 110 and the patient fixation device 111 with at least some of the imaging information in the imaging information 103, to provide corresponding registration information. Automatic registration of the device to the imaging information 103 can include automatically registering a model of at least one of the patient support surface and the patient fixation device to at least some of the imaging information. "Registration" refers to aligning and spatially corresponding items. In this context, a representative model of the artifact in question is aligned and spatially corresponding to one or more features in the imaging information. This latter type of model can be used as further input when automatically optimizing a radiotherapy plan as described below.

[0068] Alternatively, in conjunction with or instead of the foregoing, in optional box 205, process 200 uses imaging information 103 to automatically determine the shape of at least a portion of the patient's body. This body shape is used to represent the patient's outermost physical edge.

[0069] At box 206, the process 200 provides input 207 to the automatically segmented patient content and virtual skin volume as described above, which is used to automatically optimize the radiotherapy plan for the treatment volume 105 for that particular patient, thereby providing an optimized radiotherapy plan 113 for that patient.

[0070] Various methods for optimizing radiotherapy plans are known in the art. However, it can be noted that, as mentioned above, the aforementioned registration information 208 can also be used for further input when automatically optimizing radiotherapy plans.

[0071] At the end of the optimization process, process 200 produces an optimized radiotherapy plan 113 for a specific patient, as indicated in box 209. (As will be described in more detail below, process 200 may actually produce multiple treatment plans, including, for example, three plans that are different from each other regarding their specific radiation delivery methods and techniques.) If desired, and as shown in optional boxes 210 and 211, process 200 may further include using the aforementioned body shape, virtual skin volume, and registration information as input to automatically calculate the trajectory of the radiotherapy platform, collision detection, and virtual rehearsal information for treatment implementation according to the optimized radiotherapy plan 113.

[0072] It should be understood that these plans are readily available and can be used accordingly to administer radiation to patients via the aforementioned platform. In one method, at least one such outcome plan is used to deliver therapeutic radiation to treat the patient's therapeutic volume.

[0073] Now for reference Figure 3 Specific explanations of these teachings will be presented. For illustrative examples, Figure 3 This process 300 is presented in the context of a breast cancer planning workflow (i.e., developing an implementable beam-based radiotherapy to treat a therapeutic volume 105 comprising a cancerous tumor in a specific patient's thymus tissue 104). It is understood that the specific details of this example are intended for illustrative purposes and are not intended to suggest any particular limitations regarding these teachings.

[0074] At box 301, the process 300 begins the workflow with CT images. After properly positioning the patient on the chest plate according to well-understood existing technical practices, the activity includes obtaining at least one tomographic localization film and one CT scan. By one method, the former includes obtaining at least two orthogonal tomographic localization films, the tomographic images including the patient's arms and chest plate, the chest plate being as wide as possible in field given the limitations of available equipment and other application settings. By one method, at least one CT scan includes an upward view including a slice above the patient's shoulders and a downward view below the patient's breast tissue 104, as well as a scan including the treatment volume 105. In typical application settings, these images are in a digital format as previously described.

[0075] Generally speaking, the equipment and processes used to acquire these computed tomography and CT scan images are well understood in the prior art. While the acquisition of specific views and subsequent uses described herein need not necessarily conform to existing art practices, for the sake of brevity, further detailed descriptions of image acquisition itself are unnecessary.

[0076] At box 302, the aforementioned image is imported into an automatic contouring activity. Contouring, of course, includes identifying and / or specifying the shape of individual organs, tissues, or other anatomical structures and patient artifacts, such as, but not limited to, part or all of the patient's breast tissue, the patient's lungs, heart, and / or chest wall. This contouring activity includes, for example, automatically contouring the patient's body shape excluding the sternum and any accompanying lines / conductors that may be present. Alternatively, in place of or in combination with the aforementioned method, this contouring activity includes automatically contouring the sternum and / or the aforementioned lines / conductors that are separate from and distanced from other image content.

[0077] One method may include expanding available CT images outward to include the patient's external body shape. In this configuration, the CT image content can be expanded to include the peripheral edges of the patient's torso, neck, and / or part or all of the patient's arms.

[0078] This automated profiling activity utilizes relevant models through a method. For example, relevant models of the thoracic plate and / or patient support treatment bed can be used to assist in these aspects.

[0079] At box 303, and following the aforementioned contouring activity, process 300 provides an external beam planning process. This external beam planning activity may, if desired, include the use of an automated or user-initiated breast cancer planning creation wizard. (It will be understood that the wizard comprises software that automates a complex task by asking the user a series of easily answered questions, answering which then drives the customized execution of the task.)

[0080] Based on the contouring information, box 303 may include automatically selecting whether the depicted treatment target (or multiple targets) represents the left breast, the right breast, or both breasts. This selection may be based on, for example, a structural code assigned during contouring. (If desired, the process 300 will be adapted to provide the technician with the opportunity to overwrite and modify the automatic selection.) Overall, the process also includes automatically defining a specific patient orientation. That is, if desired, the opportunity may be provided for participating technicians to select including lymph nodes.

[0081] The process then provides a template for automatically selecting a specific starting point for breast treatment. This selection can be based on various criteria. One approach includes the patient's clinical goals (including, where appropriate, any established ordering and / or prioritization of these goals). Another approach includes the relevant dose prescription, the identifier of the default linear accelerator, and / or any energy specification (where the latter can be automatically defined, user-specified, or overridden on demand). Yet another approach includes the pill specification (including, for example, the number of any corresponding portions). Yet another approach includes information on the size of the skin flash (e.g., in millimeters or centimeters). And yet another approach includes information specifying a particular RapidPlan™ (RP) model to facilitate the prediction of histogram dose-volume.

[0082] When considering pills, the process may, if desired, include opening a display window on the user interface to facilitate defining the pill. One method would be suitable for defining only a fraction of the pill rather than the entire pill. When only a fraction of the pill is present, a method could be used to automatically create at least six corresponding plans. ("Fragment" refers only to a portion of the total dose represented, such as a dose administered from a particular angle or field.)

[0083] This activity involves selecting a specific imaging template and automatically creating a field with corresponding settings, which can be used as the initial starting point for treatments using a specific accelerator. Examples include, but are not limited to, cone-beam computed tomography (CBCT), megavolts (MV), and kilovolts (kV).

[0084] In this example, the external beam planning includes three plans: generation, optimization, and calculation (or, when using pellets for a small fraction, six plans). These plans include the TO-VMAT (Volume-Modulated Intensity-Modulated Radiotherapy Trajectory Optimizer) plan, the TO-IMRT (Intensity-Modulated Radiotherapy Trajectory Optimizer) plan, and the iComp irregular surface compensation technique plan. These different plans use different techniques depending on the specific circumstances. These teachings will also apply to other techniques such as, for example, hybrid combinations of VMAT and IMRT, or hybrid combinations of open-field and IMRT, if desired.

[0085] The geometry definition of the VMAT arc and IMRT field uses a trajectory optimizer (TO) that supports dynamic or static collimator angle determination, coplanar and non-coplanar fields, energy selection, and isobath placement. (Those skilled in the art will recognize that, as understood in the art, trajectory optimization is an extension of the segmentation method used in radiotherapy.) In particular, the field sequence is adjusted for high-quality planning, collision prediction, and efficient delivery. Additional modifications include rack slowdowns for radians, restricted leaf sequences, and modified control point weights; these teachings will be applied to help improve planning quality.

[0086] If desired, optimizing the leaf sequence and flux can take into account the skin flash margin to handle possible target changes and movements.

[0087] The geometry of an irregular surface compensator program can be defined based on the outlined target volume shape, including iso-depth point placement. Flux optimization can be achieved by minimizing the dose to defined at-risk organs while maintaining coverage of the desired target volume, taking into account physician-prioritized clinical objectives. A method allows for the automatic addition of a predetermined skin flash margin to the flux to accommodate potential target changes or shifts in the program. Once the flux is optimized, blade movement and dose calculations can be performed automatically.

[0088] If desired, automated interactive optimization of volumetric arc-shaped intensity-modulated radiotherapy (IMRT) plans (as understood in the art) can be utilized during the optimization process for some or all of the plans to achieve Pareto optimal dose.

[0089] TO-VMAT program creation

[0090] This process can automatically optimize the radiation source trajectory by limiting the positions of one or more arcs and collimators to a sequence of factors including the breast template and the patient's treatment volume (the latter considering the presence or absence of nodes as needed and / or specified). A method allows collision detection to be run in the background to check that the trajectory is collision-free. A method transforms available clinical objectives into optimization goals, which are then used to guide the iterative optimization process. A method limits the accelerator photon energy.

[0091] After optimization, the process can calculate the dosage to be implemented.

[0092] TO-IMRT program creation

[0093] This process can automatically optimize the radiation source trajectory, at least in part, by limiting the static field to a ranking consideration of one or more organs of risk, namely the breast template and the patient's treatment volume (the latter taking into account the presence or absence of nodes as needed and / or specified). One method allows conflict detection to run in the background to check that the trajectory is conflict-free. Another method utilizes this objective to guide the iterative optimization process. A third method limits the energy.

[0094] After optimization, the process can calculate the blade movement and dosage to be applied when using a multi-blade collimator.

[0095] iComp plans to create

[0096] This process can automatically identify tangent fields based on the patient's treatment volume. These fields are parallel and relative. The number of fields typically depends on the size of the target. Using two fields is usually sufficient, while additional fields can be added. If lymph nodes are included, additional adjacency fields are sometimes added to facilitate node treatment. An additional collision detection can be run in the background to check that the trajectory is collision-free. A method is used to transform the available clinical objective into an optimization objective, which is then used to guide the iterative optimization process. A method is used to limit the energy.

[0097] Collision-free geometry can be calculated based on the patient geometry of the treatment bed and (multiple) fixation devices in an arcuate plane relative to the relevant gantry angle. Collision zones are calculated using a methodological and image-based model. This method allows for the automated search and generation of more complex tracks, which can, in turn, improve program quality and efficacy.

[0098] After optimization, the process can calculate the blade movement and dosage used when using a multi-blade collimator.

[0099] At box 304, process 300 provides an evaluation of one or more of the aforementioned plans. Similarly, in a typical application setup, the aforementioned process provides three separate plans (or six, when selecting pills for a small subset). By a method, no results are presented for the technician to consider until all plans are ready.

[0100] The assessment includes presenting the calculated results of all three breast plans when calculating results representing physician intent and other metrics of interest corresponding to priority ranking, such as evenness index (HI), gradient index (GI), monitoring units, intensity-modulated complexity, and treatment time. When presenting dual plans indicating the presence of pills, plans are created for each plan with and without pills. In any case, all three comprehensive breast plans can be presented as scores based on corresponding clinical objectives and other metrics of interest. For example, the scores may include pass / fail indicators for each clinical objective.

[0101] With this configuration, technicians can review the proposed plans and indicate the optimal plan for the patient. This plan, in turn, can lead to the approval of a specific plan for that particular patient.

[0102] If desired, these teachings will apply to virtual rehearsals that allow technicians to deliver treatment via a defined 3D animated treatment field. This virtual rehearsal may include a representation of the treatment delivery in the dimensions of actual treatment machines, treatment beds, fixation devices, and the patient. If desired, a mountain-shaped view indicates the spacing between the patient and the application-set machines and / or between each machine during treatment delivery.

[0103] As described above, the radiotherapy platform 114 may have one or more components that physically move relative to the patient during radiotherapy planning. This involves the patient and / or patient fixation equipment 111, and collisions with such moving components are therefore possible. Collision avoidance plans can mitigate the risk of this event, but typically require information about the subjects that could potentially collide with another subject. Unfortunately, 3D CT images for a given patient tend to include only portions of particular interest to the patient, such as the patient's treatment volume 105 and some adjacent surrounding tissues 104. As a result, 3D CT images typically do not include other patient content and / or any portion or all of the patient fixation equipment 111 that may be present.

[0104] Now for reference Figure 4 A process 400 that facilitates the prediction (and thus avoidance) of potential collisions will be described, which avoids the use of techniques other than the CT device 106 itself, such as the imaging device 107. For illustrative purposes, it is assumed that the control circuit 101 described above performs this process 400.

[0105] In block 401, control circuitry 101 accesses patient tomographic slices 402 (e.g., by accessing the aforementioned memory 102). These tomographic slices 402 include patient content beyond portions appearing in the planned 3D CT images, which are used to formulate a radiotherapy plan 113 for the patient. In a typical application setup, each tomographic slice 402 includes this patient content. When the application setup includes one or more patient fixation devices 111, these tomographic slices 402 may also each include an image of at least a portion of at least one fixation device 111. (As described above, but for clarity in these respects, these tomographic images are captured by the CT apparatus 106 described above and an imaging device 107 not described above.)

[0106] In typical application settings, this procedure 400 assumes access to at least two of these tomographic localization slices 402 (in... Figure 4 These are referred to as first and second fault localization pieces 402. By one method, the process 400 provides access to only two of these fault localization pieces 402. However, if desired, one or more additional fault localization pieces 402 may also be accessed (e.g., Figure 4 The Nth tomographic localization piece 402 is represented by an optional Nth tomographic localization piece (where N is an integer). For the remainder of this illustrative example, it is assumed that the control circuit 101 accesses only two such tomographic localization pieces.

[0107] In this example, two tomographic localization films 402 provide a basic orthogonal view of the patient's contents. (Temporary reference) Figure 5 The figure presents a schematic representation of a prone patient 501 and a patient fixation device 111. It also shows field views for the first tomographic localization piece 502, including a top / front view / frontal view of the patient 501, and field views for the second tomographic localization piece 503, including a side view / lateral view of the patient 501. In this illustrative example, the two tomographic localization pieces 502 and 503 have a larger overall dimension than is required for multiple application settings; this excessive scale is intended to help illustrate the substantially orthogonal relationship between the two tomographic localization pieces 502 and 503.

[0108] As used in this article, the expression “substantially orthogonal” will be understood to refer to a range that extends from fully orthogonal to positive or negative 1 degree, 2 degrees, 3 degrees, 4 degrees, 5 degrees or other values ​​adapted to the specific application settings.

[0109] It will be understood that the bottom view / rear view may be used in place of the top view / front view / rear view shown. As used in this article, the expression "front-rear" view will be understood to identify one or more of these views.

[0110] It will also be understood that at block 401, the tomographic localization slice 402 accessed by the control circuitry is a digitized representation / content. In modern application settings, the CT device 106 can be expected to provide digitized content in a first instance without requiring interventional conversion from analog to digital format.

[0111] By means of a method, for example, tomographic imaging sharing the same scale and relative distance from a fictitious centerline of patient 501. When this is not the case, these teachings will be applied to normalize one or both tomographic localization films 402 to be used, thereby sharing the same scale and relative distance.

[0112] At box 403, control circuit 101 uses accessed tomographic localization piece 402 to derive a virtual volume structure that represents patient content other than the portion of the patient that appears in the corresponding three-dimensional CT image of the patient. Figure 6 Simple illustrative examples of these aspects are provided. The volume indicated by reference numeral 601 corresponds to a portion of the patient represented in a three-dimensional CT image of the patient. On the other hand, the volume indicated by reference numeral 602 is a virtual volume structure derived from the accessed tomographic localization slice 402.

[0113] The aforementioned activities may include, through a method, automatically segmenting two-dimensional patient anatomy (and fixation devices, when present) outside the planned CT scan area of ​​the patient. A three-dimensional virtual volumetric structure can then be constructed based on the segmented two-dimensional contours on those tomographic localization images. If desired, additional patient anatomy missing from the tomographic localization images can be inferred based on existing knowledge and similarly used in the construction of the three-dimensional virtual volumetric structure. (Those skilled in the art will also understand that a 3-D model of the patient fixation device 111 can also be used when the virtual volumetric structure includes the fixation device 111.)

[0114] Refer again Figure 4 At box 404, when evaluating a radiotherapy plan for a patient utilizing this particular radiotherapy platform 114, control circuitry 101 uses the aforementioned virtual volume structure to predict potential collisions. Various known methods exist for detecting possible or potential collisions in these respects. As this teaching is not overly sensitive to any particular choice of method in these respects, further detailed descriptions are not provided here for the sake of brevity.

[0115] If desired, and as indicated in optional box 405, the process 400 will be applied to a radiation therapy plan 113 optimized for use with the results, which has been carefully examined for collision avoidance in order to administer radiation therapy to patients using this particular radiation therapy plan 114.

[0116] Now for reference Figure 7 A slightly more specific example than those described above will be described. It will be understood that the specific details of process 700 are intended for illustrative purposes and are not intended to suggest any particular limitations in these respects. Furthermore, it will be assumed that the aforementioned control circuit 101 performs the described activities.

[0117] At box 701, control circuit 101 obtains the aforementioned orthogonal tomographic localization film. At box 702, control circuit 101 obtains the planned CT image, and at box 703, it creates a body contour for the patient from the planned CT image, which is well understood in the prior art.

[0118] At box 704, control circuit 101 expands the planned CT image based on the contents of the tomographic localization film, and at box 705 segments the patient's anatomical structures that appear in the tomographic localization film outside the scan area captured by the planned CT image.

[0119] At box 706, control circuit 101 determines whether critical patient anatomical structures are missing from the tomographic localization image. (In this context, a patient anatomical structure can be considered critical when it is potentially located in a position where collision could occur during radiotherapy.) When this is not the case, at box 708, control circuit 101 constructs a three-dimensional virtual volumetric structure on the expanded CT image. However, when the aforementioned determination is true, control circuit 101 (at box 707) infers at least a partial missing portion based on prior knowledge of the patient's anatomy and / or prior two-dimensional segmentation information, followed by the construction of the three-dimensional virtual volumetric structure at box 708.

[0120] This configuration, by utilizing a three-dimensional virtual volumetric structure derived solely from information provided by CT equipment (including 3D planning CT images and 2D tomography), and without the aid of additional scanning or image capture devices, potentially eliminates the need for supplemental equipment in the application setup. This helps predict potential collisions in specific radiotherapy plans corresponding to the context of a particular radiotherapy platform. By avoiding the need for such supplemental equipment, the technical requirements of the application setup are reduced, thereby reducing capital expenditures and operating and maintenance costs. These teachings also help avoid the need for additional training of application setup technicians and / or the presence of application setup technicians skilled in operating such supplemental scanning / image capture equipment and methods.

[0121] Those skilled in the art will recognize that various modifications, alterations, and combinations can be made with respect to the embodiments described above without departing from the scope of the invention, and that such modifications, alterations, and combinations are considered to be within the scope of the concept of the invention.

Claims

1. A device for treatment, comprising: A computed tomography (CT) device is configured to provide partial CT images of a patient; The control circuit is configured as follows: - Access the patient's tomographic images, which include patient content other than the aforementioned portion of the patient; - Using the tomographic localization film of the patient to derive a virtual volume structure, the virtual volume structure representing at least the patient content other than the said portion of the patient; - The virtual volume structure is used to predict potential collisions when evaluating a radiotherapy plan for the patient using a radiotherapy platform.

2. The apparatus of claim 1, wherein the tomographic localization slice comprises two at least substantially orthogonal views of the patient contents.

3. The apparatus of claim 2, wherein the two at least substantially orthogonal views comprise a front-rear view and a side view.

4. The apparatus of claim 1, 2 or 3, wherein the tomographic localization slice comprises only two at least substantially orthogonal views of the patient contents.

5. The apparatus according to claim 1, 2 or 3, wherein the tomographic localization film of the patient further includes an image of at least one fixation device or a portion thereof.

6. The apparatus according to any one of claims 1 to 3, wherein the CT apparatus is configured to provide the tomographic localization film of the patient, including patient contents other than the portion of the patient.

7. The apparatus of claim 5, wherein the control circuitry is further configured to: use the tomographic positioning piece to derive a virtual volume structure representing the patient content other than the portion of the patient by using the tomographic positioning piece to derive a virtual volume structure representing at least the patient content other than the fixation device and the patient.

8. The apparatus of claim 7, wherein the control circuitry is further configured to predict potential collisions by using the virtual volume structure when evaluating a radiotherapy plan for the patient utilizing the radiotherapy platform and the fixation device.

9. The apparatus of claim 5, wherein the radiotherapy platform includes at least one component that is physically movable relative to the patient during radiotherapy planning, such that collisions may potentially occur between the patient and / or the fixation device and the radiotherapy platform during radiation therapy for the patient.

10. A method for use with a radiotherapy platform having at least one component and a computed tomography (CT) apparatus, wherein the component is physically moved relative to a patient during radiotherapy planning, and the CT apparatus is configured to provide CT images of portions of the patient, the method comprising: Control circuit: Access the patient's computed tomography images, which include patient content other than portions of the patient captured in the patient's computed tomography images; The tomographic localization film of the patient is used to derive a virtual volume structure, which represents at least the patient content other than the portion of the patient. The virtual volume structure is used to predict potential collisions when evaluating a radiotherapy plan for the patient using the radiotherapy platform.

11. The method of claim 10, wherein the tomographic localization slice comprises two at least substantially orthogonal views of the patient content.

12. The method of claim 11, wherein the two at least substantially orthogonal views comprise a front-rear view and a side view.

13. The method of claim 10, 11 or 12, wherein the tomographic localization film comprises only two at least substantially orthogonal views of the patient content.

14. The method according to any one of claims 10 to 12, wherein the tomographic localization film of the patient further includes an image from at least one fixation device.

Images