Local dose heterogeneity metric for spatially fractionated radiotherapy

Abstract

The present disclosure relates to a local dose non-uniformity metric for spatially fractionated radiotherapy. A method for planning and administering a spatially fractionated radiotherapy treatment plan is described. The method can include receiving a treatment plan, the treatment plan including a heterogeneous radiotherapy dose having a plurality of local maximum doses and a plurality of local minimum doses. The method can include generating an upper non-uniformity curve based at least in part on the plurality of local maximum doses and generating a lower non-uniformity curve based at least in part on the plurality of local minimum doses. The method can include determining a dose envelope bounded by the upper non-uniformity curve and the lower non-uniformity curve. The method can include generating a local dose contrast based on the upper non-uniformity curve and the lower non-uniformity curve of the dose envelope.

Description

Technical Field

[0001] This application generally relates to the planning and execution of radiotherapy treatments. Background Technology

[0002] Radiation therapy is a widely used cancer treatment that traditionally delivers a homogeneous dose of radiation to a target volume (such as a tumor) while minimizing exposure to surrounding healthy tissue. The effectiveness of traditional radiation therapy depends heavily on precise dose delivery to the target volume while minimizing toxicity to organs at risk.

[0003] Certain radiotherapy treatments, such as spatially fractionated radiotherapy (SFRT), have shown the potential to improve treatment outcomes by delivering non-homogeneous radiation doses to the target volume. Unlike conventional radiotherapy, which delivers homogeneous radiation doses, SFRT introduces non-homogeneous dose radiation with alternating dose peaks and troughs. These approaches improve tissue recovery and reduce the side effects associated with conventional homogeneous radiotherapy. However, current treatment planning methods lack sufficient tools to locally quantify and optimize the dynamic nature of local dose non-homogeneity in SFRT during planning and administration, leading to delayed treatment and inefficient administration. Summary of the Invention

[0004] For the reasons stated above, there is a need for improved methods and systems for planning and administering SFRT, including computationally stable, localized, clinically relevant measures of local dose nonuniformity that enable accurate dose assessment and optimization in SFRT treatment planning and administration. It should be understood that implementations of the systems and methods described herein may meet more, fewer, or different needs than those improvements described above without departing from the scope of this specification.

[0005] Current / conventional methods for planning and administering SFRT face technical challenges due to the lack of accurate and reliable metrics for quantifying and optimizing local dose nonuniformity within the treatment volume. The goal of SFRT is to deliver a heterogeneous dose distribution, using alternating high-dose peaks and low-dose troughs to promote tumor destruction while preserving healthy tissue. However, the conventional peak-to-trough dose ratio has proven insufficient because it becomes numerically unstable when the trough dose approaches zero, leading to inaccurate measurements that hinder treatment planning. This challenge prevents clinicians from optimally adjusting treatment parameters at the local level, resulting in delays, inefficiencies, and potentially compromised treatment outcomes.

[0006] Furthermore, conventional computer models for evaluating radiotherapy treatment plans using traditional methods are computationally inefficient and resource-intensive due to their reliance on intensive dose calculations and global optimization techniques. These methods typically require high-resolution dose simulations across the entire treatment volume, resulting in significant processing time and memory consumption. Additionally, conventional peak-to-trough dose ratio calculations are hampered by numerical instability, particularly in regions where the trough dose is close to zero, requiring additional computational corrections, which further increases processing overhead. Without an optimization framework for local dose-contrast evaluation, these models must iteratively adjust treatment parameters across large datasets, leading to prolonged optimization cycles, resulting in high processing power requirements and delayed output of results.

[0007] The disclosed method and system address these technical inefficiencies by introducing a computationally stable and localized measure of dose nonuniformity, enabling accurate and efficient evaluation of spatially fractionated radiotherapy (SFRT) treatment plans. Instead of relying on global dose calculations or unstable peak-to-trough dose ratios, the system interpolates between local maximum and minimum dose points to generate upper and lower nonuniformity curves, thereby defining a dose envelope capable of rapid analysis for local dose contrast. This approach reduces computational overhead by focusing calculations only on clinically relevant areas, eliminating unnecessary full-volume simulations, resulting in shorter execution times and less computational power required.

[0008] By integrating this local dose-to-contrast ratio into an automated treatment planning system, this invention enables real-time optimization of SFRT parameters, allowing for faster adjustments to treatment plans while maintaining accuracy and minimizing computational resource consumption.

[0009] In at least one embodiment of this specification, a local dose heterogeneity metric for evaluating the local dose contrast between peak and trough doses is used to determine the local heterogeneity of the SFRT plan at local locations within the treatment area. Local dose contrast is generated for a desired location by (i) interpolating a higher dose between local maximum doses, (ii) interpolating a lower dose between local minimum doses, and (iii) determining the contrast between the interpolated higher and lower doses. This process is repeated to determine the local dose contrast for each location within the dose envelope defined by the interpolated higher and lower doses.

[0010] These local dose comparisons were combined to generate a non-uniform thermogram for SFRT treatment planning. This non-uniform thermogram was overlaid on diagnostic images of the tumor and surrounding organs at risk. By overlaying the non-uniform thermogram on diagnostic images, SFRT can be assessed and adjusted more efficiently to improve treatment efficacy while minimizing damage to adjacent tissues. Overlaying the non-uniform thermogram on diagnostic images allows physicians and clinicians to quickly and accurately determine the biological effects on the target tumor and surrounding organs at risk.

[0011] In an embodiment, a method may include: receiving a radiotherapy treatment plan for application to target tissue, the radiotherapy treatment plan having a non-uniform dose, the non-uniform dose including: a plurality of local maximum doses and a plurality of local minimum doses; generating (i) an upper non-uniformity curve at least partially based on the plurality of local maximum doses and (ii) a lower non-uniformity curve at least partially based on the plurality of local minimum doses; determining a dose envelope of the radiotherapy treatment plan defined by the upper non-uniformity curve and the lower non-uniformity curve; and generating local dose comparisons of the dose envelope at locations within the dose envelope.

[0012] In part, in response to local dose contrast exceeding a threshold, treatment parameters in a radiotherapy treatment plan can be adjusted to reduce local dose contrast.

[0013] The method may further include: generating a local dose contrast distribution that includes multiple local dose contrasts; and generating a heat map of the non-uniformity of the radiotherapy treatment plan based on the local dose contrast distribution.

[0014] Local dose comparison can be generated in part based on the upper and lower non-uniformity curves at the location.

[0015] Generating an upper non-uniform curve may include interpolating non-uniform doses between adjacent local maximum doses within multiple local maximum doses.

[0016] The method may include linearly interpolating non-uniform doses between adjacent local maximum doses within a plurality of local maximum doses.

[0017] The method may include: receiving a diagnostic image of a target tissue; displaying the diagnostic image of the target tissue on a display; and overlaying a heatmap onto the diagnostic image of the target tissue.

[0018] The method may include: adjusting one or more operating parameters of a radiotherapy machine according to a radiotherapy treatment plan; and transmitting instructions to the radiotherapy machine by one or more processors to administer the radiotherapy treatment plan to target tissue.

[0019] In an embodiment, a system may include: one or more processors; and a computer-readable non-transitory storage medium containing instructions that, when executed by the one or more processors, cause the one or more processors to perform a method comprising: receiving a radiotherapy treatment plan for application to target tissue, the radiotherapy treatment plan having a non-uniform dose, the non-uniform dose including: a plurality of local maximum doses and a plurality of local minimum doses; generating (i) an upper non-uniformity curve at least partially based on the plurality of local maximum doses and (ii) a lower non-uniformity curve at least partially based on the plurality of local minimum doses; determining a dose envelope of the radiotherapy treatment plan defined by the upper non-uniformity curve and the lower non-uniformity curve; and generating local dose comparisons of the dose envelope at locations within the dose envelope.

[0020] In an embodiment, a computer-readable non-transitory storage medium includes instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: receiving a radiotherapy treatment plan for application to target tissue, the radiotherapy treatment plan having a non-uniform dose, the non-uniform dose including: a plurality of local maximum doses and a plurality of local minimum doses; generating (i) an upper non-uniformity curve at least partially based on the plurality of local maximum doses and (ii) a lower non-uniformity curve at least partially based on the plurality of local minimum doses; determining a dose envelope of the radiotherapy treatment plan defined by the upper non-uniformity curve and the lower non-uniformity curve; and generating local dose comparisons of the dose envelope at locations within the dose envelope. Attached Figure Description

[0021] Non-limiting embodiments of the present disclosure are described by way of example with reference to the accompanying drawings, which are schematic and not intended to be drawn to scale. Unless indicated as representing background art, these figures represent aspects of the present disclosure.

[0022] Figure 1 The illustration shows the components of a workflow-oriented radiotherapy system according to an embodiment.

[0023] Figure 2 This is a block diagram of a method for planning and administering radiotherapy treatment according to an embodiment.

[0024] Figure 3 The illustration shows the dose envelope of radiotherapy dose treatment according to an embodiment.

[0025] Figure 4 The illustration shows the dose distribution of a radiotherapy treatment beam within a patient's body according to an embodiment.

[0026] Figure 5The illustration shows various distribution views of a radiotherapy treatment plan according to an embodiment.

[0027] Figure 6 The illustration shows various distribution views of a radiotherapy treatment plan according to an embodiment.

[0028] Figure 7 This is a block diagram of a method for automating radiotherapy treatment planning processing according to an embodiment. Detailed Implementation

[0029] Reference will now be made to some embodiments illustrated in the accompanying drawings, and these embodiments will be described herein using specific language. However, it should be understood that this is not intended to limit the scope of the embodiments of the methods and systems described herein. Changes and further modifications to the features described herein, as well as additional applications of the principles of the embodiments of the methods and systems described herein, as will be conceived by those skilled in the art upon which this disclosure is known, should be considered within the scope of the embodiments, methods, and / or systems described herein.

[0030] Unlike conventional radiotherapy, which delivers a homogeneous dose to tumors or other target tissues, SFRT introduces a non-uniform dose distribution characterized by alternating high-dose (peak) and low-dose (trough) zones. The goal is to maximize tumor destruction while allowing for more effective recovery of normal tissue. Treatment can be delivered using a variety of methods. GRID therapy uses perforated collimators or blocking grids to deliver parallel high-dose beams, creating a checkerboard-like dose pattern, which is useful for treating large tumors. Lattice therapy, a more advanced form of SFRT, optimizes dose placement in three-dimensional space, creating localized high-dose islands against a low-dose background. This allows for precise tumor targeting using techniques such as intensity-modulated radiotherapy (IMRT) or volume-modulated arc therapy (VMAT). Microbeam and small-beam radiotherapy (MRT / MBRT) utilizes extremely narrow beams to achieve a finer dose distribution, demonstrating the potential to improve tumor control with minimal damage to normal tissue, particularly in preclinical studies.

[0031] SFRT utilizes the differential response of tumors and normal tissues to radiation. The high-dose peak region induces direct tumor cell death, triggering an immune response and vascular damage, which further impairs tumor survival. Simultaneously, the low-dose trough region allows healthy tissue to repair and regenerate, thereby reducing overall radiotoxicity and side effects. Additionally, the bystander effect indicates that cells in the low-dose region may still be damaged by signals from adjacent high-dose regions, further promoting tumor destruction.

[0032] To ensure appropriate dose delivery, imaging techniques such as CT, MRI, and PET help define tumor volume and surrounding structures. Treatment planning systems (TPS) optimize beam placement and dose modulation to produce the correct spatial segmentation pattern. Local dose metrics, such as non-uniformity metrics based on local contrast, improve SFRT assessment compared to traditional peak-to-trough dose ratio (PVDR) calculations, which can be unstable and inaccurate.

[0033] SFRT offers several advantages over conventional radiotherapy. It achieves a higher probability of tumor control by allowing dose escalation against drug-resistant tumors while preserving normal tissue. The presence of troughs allows for the recovery of healthy cells, resulting in fewer side effects compared to homogeneous dose therapy. SFRT can also stimulate a systemic anti-tumor immune response, thereby improving overall treatment efficacy. This technique is particularly beneficial for large, hypoxic tumors that are difficult to treat with standard radiation approaches.

[0034] As an innovative approach to radiation therapy, SFRT utilizes spatial dose nonuniformity to enhance cancer treatment efficacy. By integrating modern treatment planning techniques, advanced imaging, and optimized dosimetry, SFRT offers a promising alternative for improving tumor control while reducing side effects, making it a valuable complement to modern radiation oncology.

[0035] The level of non-uniformity at a location (e.g., the difference between a high-dose peak and a low-dose trough at a point) is used to determine the effectiveness of SFRT at that location. This non-uniformity can be measured using local dose contrast measures. Local dose measures can be used during SFRT planning, administration, and review to provide clinicians and physicians with local values ​​of SFRT plan non-uniformity to (i) accelerate SFRT plan administration, (ii) improve SFRT plan outcomes, and / or (iii) increase the efficiency of SFRT plan calculation.

[0036] Being able to implement the methods described in this paper to use Figure 1 The various computing devices / features described herein are used to generate local dose contrast quantities. Therefore, Figure 1 A non-limiting example of a computer environment is described, in which the server is capable of performing the processing / methods described herein, such as acquiring, processing, and presenting radiotherapy treatment data, such as predicted local dose comparisons.

[0037] Figure 1The illustration shows various components of a system 100 for planning and administering SFRT treatment according to an embodiment. System 100 may include an analytics server 110, a medical record database 120, a radiotherapy system 140, and a system administrator computer 150 or workstation. These features can communicate with each other via a network 130. For example, system 100 may present one or more pages / GUIs on a radiotherapy machine 141 via analytics server 110. In another example, system 100 may present a page via analytics server 110 that displays parameters and / or simulation results of the SFRT treatment plan. For example, analytics server 110 may present a heatmap of radiation dose comparison overlaid on a patient's diagnostic images, to which system 100 plans to administer SFRT treatment.

[0038] Network 130 may include wired and / or wireless communications via one or more transmission media according to one or more standards. Communication via network 130 may be based on various communication protocols, such as Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), and Institute of Electrical and Electronics Engineers (IEEE) communication protocols. Network 130 may also include wireless communications based on the Bluetooth specification set or another standard or proprietary wireless communication protocol. Network 130 may also include communications via cellular networks, including, for example, Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), and Global Evolution Enhanced Data Network (EDGE). Examples of network 130 may include, but are not limited to, private or public local area networks (LANs), wireless LANs (WLANs), metropolitan area networks (MANs), wide area networks (WANs), and the Internet.

[0039] The analysis server 110 can be any computing device capable of performing the actions described herein. For example, the analysis server 110 includes a processing unit and a computer-readable non-transitory storage medium. The processing unit includes a processor and a computer-readable non-transitory storage medium, such as random access memory, coupled to the processor. In some embodiments, the analysis server 110 executes algorithms or computer-executable program instructions stored on the computer-readable non-transitory storage medium, which may be executed by a single processor or multiple processors in a distributed configuration. These instructions allow the processor to implement one or more of the functionalities / methods described herein. The analysis server 110 can be configured to interact with one or more software modules of the same or different types running within system 100.

[0040] Non-limiting examples of the processor may include microprocessors, application-specific integrated circuits (ASICs), and field-programmable object arrays (FPGAs). The analysis server 110 is capable of performing data processing tasks, data analysis tasks, and value assessment tasks. Non-limiting examples of the analysis server 110 may include desktop computers, server computers, laptop computers, tablet computers, etc. For simplicity, Figure 1 A single-server computing device acting as an analytics server 110 is depicted. However, some embodiments may include multiple server computing devices capable of performing the various tasks described herein.

[0041] The implementation of the methods described herein is not limited to Figure 1 The system architecture described herein. In alternative embodiments, the analysis server 110 may be an embedded computing device disposed within the radiotherapy machine 141. In some embodiments, the analysis server 110 may be multiple computing devices operated locally and / or remotely. In various embodiments, the analysis server 110 may operate via cloud services (e.g., a network, the Internet). Cloud services may be performed according to various communication protocols (such as TCP / IP, UDP, and IEEE communication protocols). The analysis server 110 may utilize databases (such as local database 111) to store and / or retrieve various types of data described herein, such as patient diagnostic images (e.g., X-ray images, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound imaging, positron emission tomography (PET) scans, mammograms, fluoroscopy scans, endoscopic scans, etc.) and / or operating parameters of the radiotherapy system 140 (e.g., beam energy, radiation penetration depth, dose rate, field size, gantry angle, collimator angle, monitor unit, treatment time, examination table position, beam on-time, etc.). The page can be displayed on a display screen associated with the radiotherapy system 140.

[0042] In some embodiments, the analysis server 110 displays a page showing the SFRT treatment plan doses (e.g., distribution plots, peak-to-trough dose ratio (PVDR) plots, local dose nonuniformity plots, diagnostic images, etc.). The analysis server 110 may display the various pages described herein on a display screen located anywhere within the planning or treatment room (such as on a wall of the treatment room, on any electronic device within the treatment room, or on any electronic device within the planning room). In some configurations, the analysis server 110 may display the pages on a system administrator computer 150 or a workstation.

[0043] The local database 111 can also store data corresponding to the radiotherapy machine 141 (such as the default position of the radiotherapy machine 141, the current position of the radiotherapy machine 141, such as the orientation of the bed / examination table and the gantry).

[0044] The analysis server 110 can also utilize one or more other databases (such as the medical record database 120) to store and / or retrieve the various data described herein. The databases described herein can be configured to store one or more databases of data, and this disclosure is not intended to be limited to a specific number or location of databases. The analysis server 110 can instruct the medical record database 120 to store patient data (e.g., patient name, patient profile image) associated with a patient identifier (e.g., patient name, patient machine alignment information). The analysis server 110 can then instruct the medical record database 120 to populate a dataset corresponding to the patient and display the patient's profile image. If the analysis server 110 receives a request from the radiotherapy system 140 to display data associated with a patient identifier, then the analysis server 110 can query the medical record database 120 and retrieve the corresponding dataset. The analysis server 110 can then use the methods / systems described herein to dynamically prepare and administer SFRT treatment plans using measures of local dose nonuniformity.

[0045] The medical record database 120 may also include SFRT treatment files associated with a patient identifier. As used herein, a radiotherapy treatment file refers to all data associated with a patient's radiotherapy treatment and is not limited to specific steps or information. Radiotherapy treatment files may also be obtained from the radiotherapy system 140, the medical record database 120, and / or the system administrator computer 150. Radiotherapy treatment files may include treatment data associated with the patient. Radiotherapy treatment files may be associated with a patient identifier and may also be updated after the patient has completed treatment. For example, after a patient has completed a treatment phase, the medical record database 120 may obtain the duration of the treatment phase from the radiotherapy system 140 and update the radiotherapy file to include the duration of the treatment phase. Although aspects of the embodiments described herein discuss radiotherapy treatment as files, radiotherapy treatment files represent a set of patient medical and treatment data that may be stored in different files. For brevity, this disclosure refers to patient data as radiotherapy treatment files.

[0046] Radiation therapy treatment files may include data on treatment plans for one or more patients, with each plan specific to an individual patient. For example, each treatment plan may be uniquely created for each patient and corresponds to the patient's unique attributes (such as the patient's physical attributes and the unique condition to be treated). In some configurations, each treatment plan for each patient may itself include multiple files.

[0047] Analysis server 110 can retrieve patient-associated treatment data stored in the patient's(s) radiotherapy treatment files(s). As described herein, analysis server 110 can analyze the treatment data and can display the various features and graphical components described herein. While medical record database 120 may contain treatment data (radiotherapy treatment files) associated with multiple patients, the methods described herein are implemented such that the various page and graphical features described herein are specific to the particular patient receiving treatment.

[0048] In some configurations, the analysis server 110 can retrieve radiotherapy treatment files associated with multiple patients. The analysis server 110 can then receive a selection of patients to be treated from an operator (e.g., a technician). Therefore, the analysis server 110 identifies the radiotherapy treatment files associated with the selected patients and customizes the graphical components described herein for the selected patients.

[0049] The analytics server 110 can also retrieve patient data associated with a patient from the medical record database 120, the radiotherapy system 140, and / or the system administrator computer 150, and instruct the medical record database 120 to store that patient data. For example, the medical record database 120 may include patient data (e.g., patient data previously populated by the analytics server 110 and / or periodically retrieved from third-party data sources). If a patient is selected, the analytics server 110 can query and retrieve patient data from the medical record database 120 and provide the retrieved patient data.

[0050] Analysis server 110 can receive patient-related treatment data from medical record database 120, which may also include at least a patient identifier. Analysis server can also receive radiation therapy treatment files associated with one or more patients from medical record database 120. Radiation therapy treatment files can refer to files containing data associated with the treatment process by a medical team (e.g., radiation oncologist, radiation therapist, medical physicist, and / or medical radiation dosimeter) planning appropriate external beam radiation therapy or internal brachytherapy treatment techniques for the patient.

[0051] Data in radiation therapy documentation is not limited to external radiation therapy, as other treatments may affect radiation therapy, such as medical oncology treatments (chemotherapy), interventional oncology treatments (e.g., cryotherapy, microwave therapy, or embolization), or other non-therapeutic procedures, such as laboratory and appointments with other healthcare professionals. Radiation therapy treatment documentation may include data specific to one or more patients. This documentation may include patient identifiers, the patient's electronic health record, medical images (e.g., CT scans, 4D CT scans, MRI, and X-ray images), treatment-specific data (e.g., curvature information or treatment type), target organs (e.g., data identifying the specifications and location of the tumor to be eradicated), treatment plans, etc. Additional examples may include non-target organs, dose-related calculations (e.g., radiation dose distribution within the patient's anatomical region), and radiation therapy machine-specific information (e.g., examination table gantry orientation, machine trajectory, control points, dose distribution, and / or curvature information).

[0052] The analysis server 110 can use a patient identifier within a radiotherapy treatment file to identify a specific patient and retrieve additional information about that patient. For example, the analysis server 110 can query the medical record database 120 to identify medical data associated with the patient. For instance, the analysis server can query data associated with the patient's anatomy, such as body data (e.g., height, weight, and / or body mass index) and / or other health-related data (e.g., blood pressure or other data related to the patient receiving radiotherapy treatment). The analysis server 110 can also retrieve data associated with the patient's current and / or previous medical treatments (e.g., previous treatment portions or data associated with the patient's previous surgeries).

[0053] Analysis server 110 can analyze the received data and generate additional queries accordingly. For example, analysis server 110 can retrieve data associated with one or more medical (or other) devices required by the patient. Analysis server can retrieve data indicating that the patient has a respiratory disease. Therefore, analysis server 110 can generate a query and transmit the query to radiotherapy machine 141 or system administrator computer 150 to identify whether the patient uses / needs a ventilator.

[0054] If needed, the analytics server 110 can also analyze the patient's medical data records to identify desired patient attributes. For example, the analytics server 110 can query a database to identify a patient's BMI. However, because many medical records are not digitized, the analytics server 110 may not use simple query techniques to receive the patient's BMI value. Therefore, the analytics server 110 can acquire the patient's electronic health data and can execute one or more analytics protocols (such as natural language processing) to identify the patient's body mass index. In another example, if the analytics server 110 does not receive tumor data (e.g., endpoints), then the analytics server 110 can execute various image recognition protocols and identify the tumor data.

[0055] Analysis server 110 can use various application programming interfaces (APIs) to communicate with the different features described herein. As used herein, an API refers to a computational interface that uses connector programming code to act as a software intermediary between at least two computational components / features described herein. APIs can automatically and / or periodically transmit various calls, instructions, and / or requests between the different features of system 100. Using different APIs, analysis server 110 can automatically transmit and / or receive calls and instructions.

[0056] Additionally or alternatively, analytics server 110 may use a content delivery network (CDN) to ensure data integrity when communicating with the various features described in system 100. As described herein, a CDN refers to a distributed delivery network of proxy servers / nodes that uses a multi-tiered delivery method / system to transmit data (e.g., Akamai). Analytics server 110 may use a CDN when communicating various calls / instructions with network 130 and / or local database 111.

[0057] The radiotherapy machine 141 may also include an examination table for aligning the patient at a designated location before treatment begins. This designated location may be identified, calculated, and / or retrieved by an analytics server 110. For example, the analytics server 110 may retrieve patient data from a medical record database 120, analyze the data, and use the analyzed data to position the patient on the examination table. In some embodiments, a physician identifies how to align the patient on the examination table to optimize the treatment therapy via a system administrator computer 150. During treatment, the radiotherapy machine 141 directs radiation to a designated location on the patient lying on the examination table. This designated location may be selected by a technician operating the radiotherapy machine 141 and / or the system administrator computer 150. The analytics server 110 may also specify a location on the patient to direct the radiation.

[0058] The radiotherapy machine 141 may also include an X-ray emitter and an X-ray receiver. The X-ray emitter may be mounted on a gantry of the radiotherapy machine 141 and may be configured to deliver X-ray waves (e.g., penetrating forms of high-energy electromagnetic radiation). The X-ray emitter delivers the X-ray waves to a patient positioned on an examination table. The X-ray receiver may be positioned opposite the X-ray emitter. The X-ray waves received by the X-ray receiver generate an imprint (e.g., an image) of the patient's internal anatomy. Although the example embodiment describes the use of X-ray imaging, alternative configurations for the radiotherapy machine may include additional or other medical imaging devices (e.g., fluoroscopy, MRI, etc.).

[0059] The radiotherapy machine 141 may also include a rack that can communicate with the analysis server 110. If the radiotherapy machine 141 is in operation, the rack can rotate relative to the radiotherapy machine 141 to begin treatment of the patient. The analysis server 110 can use real-time feeds from the camera set to control and / or stop the movement of the rack. During operation, the analysis server 110 may instruct the display of the radiotherapy machine 141 to display the real-time feeds from the camera set and / or a specified page. In various embodiments, the analysis server 110 may not instruct the display of the radiotherapy machine 141 to activate the display of the radiotherapy machine 141 during operation. In some embodiments, the display of the radiotherapy machine 141 may be a touchscreen, and pages generated by the analysis server 110 can be controlled via the display of the radiotherapy machine 141.

[0060] The local database 111, medical record database 120, and radiotherapy machine 141 associated with the analysis server 110 can store information in various formats and / or encrypted versions. This information may include data records associated with various patient information, user preferences, sets of prompts (e.g., questions, queries, and inquiries), attributes related to various pages to be generated by the analysis server 110, etc. The medical record database 120 may have a logical structure of data files stored on a non-transitory machine-readable storage medium (such as a hard disk or memory), controlled by software modules of a database program (e.g., Structured Query Language (SQL)) and a database management system that executes code modules (e.g., SQL scripts) for various data query and management functions.

[0061] System administrator computer 150 may represent a computing device operated by a system administrator. System administrator computer 150 may communicate with analysis server 110. System administrator computer 150 may be configured to display various analytical metrics, wherein the system administrator is able to plan and adjust SFRT treatment plans based on determined local dose nonuniformity. For example, system administrator computer 150 may be configured to adjust monitor gantry movement, patient information, and / or various thresholds / rules described herein. Through system administrator computer 150, the system administrator can plan and adjust various operating parameters of radiotherapy system 140, such as beam energy, radiation penetration depth, dose rate, field size, gantry angle, collimator angle, treatment time, examination table position, beam on-time, maximum dose value, minimum dose value, etc. As described herein, in some embodiments, analysis server 110 automatically adjusts one or more operating parameters based on whether determined local dose nonuniformity thresholds are met or not.

[0062] The system administrator computer 150 can be configured to display certain analytical metrics when specific thresholds / rules are exceeded. For example, if local dose nonuniformity exceeds a threshold at an organ at risk, the system administrator computer 150 can be alerted. Alternatively or additionally, if local dose nonuniformity fails to meet a threshold at the target tissue, the system administrator computer 150 can be alerted by the analysis server 110.

[0063] The analytics server 110 can configure the system administrator computer 150 to audit any and / or all reads and / or writes (overwrites) of patient data going to and / or coming from the medical records database 120. For example, before a technician can write (overwrite) patient information collected by the radiotherapy machine 141, the analytics server 110 can first prompt the system administrator computer 150 to audit the patient information before it is stored in the medical records database 120.

[0064] Figure 2 This is a block diagram illustrating an example of an automated heterogeneous radiotherapy treatment planning process 200 that can be performed by one or more components of system 100 (e.g., analysis server 110) according to this disclosure. The automated heterogeneous radiotherapy treatment planning process 200 can be implemented in whole or in part as follows: Figure 1 The software programs, hardware logic, or combinations thereof used on or using the system 100.

[0065] In box 202, a three-dimensional (3D) image of the patient is obtained, and the organs and other structures within the patient's body (patient geometry) are segmented and outlined. In box 204 and path 206, information from box 202 and other information (such as the information mentioned above) are used to develop and evaluate candidate treatment plans.

[0066] In box 208, if the candidate treatment plan is satisfactory (e.g., if it meets clinical goals, thresholds, limits), then the plan is applied to the patient. If not, then aspects of the treatment plan and / or clinical goals can be iteratively modified until a satisfactory plan is generated, as shown in path 206. For example, one or more treatment parameters of the radiotherapy treatment plan can be adjusted by the analysis server until a satisfactory plan is generated. Treatment parameters may include, but are not limited to, beam energy, depth of penetration, dose rate, field size, gantry angle, collimator angle, monitor unit, treatment time, examination table position, examination table orientation, beam on-time, etc. Although several treatment parameters are presented above, it should be understood that more treatment parameters may exist or more treatment parameters may be adjusted when a satisfactory treatment plan is generated.

[0067] Clinical objectives can be represented, for example, by a set of quality metrics (such as congruence with the treatment target, preservation of critical organs, target-dose contrast, etc.) and corresponding target values ​​for these quality metrics. Local dose contrasts can be generated, plotted, and / or represented to evaluate treatment plans and determine whether they meet clinical objectives. For example, a treatment plan can be considered satisfactory if its local dose contrasts meet specified dose thresholds for a specified percentage of the target volume and / or surrounding tissues.

[0068] At box 208, when the plan is determined to be satisfactory, various controls (e.g., control signals) can be transmitted to one or more devices to facilitate the application of the radiotherapy treatment plan. For example, the analysis server 110 can transmit control signals to the radiotherapy system 140 to more generally adjust one or more operating parameters of the radiotherapy machine 141 or the radiotherapy system 140 (e.g., beam energy, radiation penetration depth, dose rate, field size, gantry angle, collimator angle, monitor unit, treatment time, examination table position, examination table orientation, beam on time, etc.). Once the operating parameters corresponding to the satisfactory radiotherapy treatment plan are set, the analysis server 110 can transmit instructions to apply the radiotherapy treatment plan by operating the radiotherapy machine. In some embodiments, the analysis server 110 receives instructions (e.g., confirmation) from the system administrator computer 150 to initiate the application of the SFRT treatment plan.

[0069] As shown in path 206, the development, evaluation, and / or optimization of radiotherapy treatment plans can be an iterative process. For example, as described in more detail below, analysis server 110 can use the above uptake information to generate local dose contrasts to represent non-uniform doses at local locations (e.g., specific locations) within the patient volume. The generated local dose contrasts can be compared to thresholds to determine the effectiveness of the SFRT treatment plan on the patient and / or damage to tissues surrounding the target volume.

[0070] Turn now Figure 3 The diagram illustrates an SFRT treatment plan 300, which can be used to determine local dose contrast or non-uniform dose. This can be implemented in electronic devices (such as...) Figure 1 The system administrator computer 150 defines and / or plans the SFRT treatment plan 300. In some embodiments, Figure 1 The analysis server 110 can be used to plan, evaluate, and / or optimize SFRT treatment plans 300. SFRT treatment plans 300 may include a non-uniform radiotherapy dose (shown as dose 302) applied to a target volume (e.g., a tumor), which produces a pattern of high-dose zones (e.g., shown as local maximum doses 304, 306) and low-dose zones (e.g., shown as local minimum doses 307, 308). SFRT treatment plans 300 may be administered via one or more SFRT methods, such as GRID therapy, lattice therapy, and / or microbeam / small beam radiotherapy (MRT / MBRT)

[0071] In the SFRT treatment plan 300, which defines GRID therapy, a perforated collimator or blocking grid is used to generate patterns of high-dose and low-dose zones. The pattern generated by the blocking grid mimics a chessboard and allows for substantial dose escalation within the tumor while preserving surrounding healthy tissue, making it particularly useful for large tumors that are difficult to treat with conventional radiotherapy. Lattice therapy extends the aforementioned approach to GRID therapy by using multiple perforated collimators or blocking grids to generate a three-dimensional dose distribution. Using advanced imaging and treatment planning systems, lattice therapy targets specific tumor regions with high radiation doses while exiting the intervention zone with lower doses, enabling personalized treatment planning tailored to the size, shape, and location of the tumor.

[0072] Microbeam radiotherapy (MRT) and small beam radiotherapy (MBRT) utilize extremely narrow, high-dose beams of radiation to maximize tumor control while preserving normal tissue, such as adjacent target tissue. In some embodiments, MRT employs a beam width in the micrometer range (e.g., 50 micrometers), while MBRT uses a slightly larger beam, typically in the 0.5 mm range. These techniques rely on the principle that very small, well-separated high-dose zones allow normal tissue to recover more effectively than in cases of conventional radiation, thus reducing toxicity and side effects. A key advantage of MRT and MBRT is their ability to deliver extremely high peak doses to the tumor while keeping the dose in surrounding tissues low, thus utilizing a biological response known as the "dose-volume effect," where small-scale tissue structures are better able to tolerate high doses when spatially segmented.

[0073] As described in this article, MRT can utilize X-rays generated by a synchrotron, producing highly collimated parallel beams with ultra-high dose rates. These microbeams produce local maximum dose patterns of 304, 306 and local minimum dose patterns of 307, 308, thereby reducing damage to healthy tissues while maintaining effective tumor control. MBRT using slightly wider beams has been explored in clinical applications, particularly in settings requiring high precision, such as brain tumors and pediatric cancers.

[0074] Figure 3 The SFRT treatment plan 300 shown is illustrated as a graph plotting the dose percentage 310 relative to position 312. The dose percentage 310 relates to the percentage of radiation administered (or to be administered) relative to the maximum application (e.g., 100%). Figure 3 As shown, the dose percentage 310 can be quantified within a range of 0% to 100%, where 0% represents no dose and 100% represents the full dose. However, it should be understood that alternative conventions can be used, where the dose percentage 310 can be quantified in different proportions (such as from 0 to 1, from -1 to 1, etc.). Location 312 can identify the location of exposure to dose 302 (e.g., radiation beam) during the application of SFRT treatment plan 300. For example, location 312 can involve locations such as those measured from a collimator, locations such as those measured from external tissues of the patient, etc. Figure 3 As illustrated, position 312 is measured in centimeters (cm); however, it should be understood that position 312 can be measured in any suitable distance metric (e.g., millimeters, micrometers, inches, etc.).

[0075] Due to the dynamic oscillating nature of dose 302, conventional methods for quantifying dose 302 are ineffective in accurately defining and measuring the effect of dose 302 at specific local locations (e.g., location 314). For example, conventional measures used to quantify non-uniform doses (e.g., dose 302) are numerically unstable (e.g., they become ambiguous or unstable when the trough dose or a lower dose is 0), lack local precision, and / or do not account for inconsistent non-uniformity throughout the patient volume. Therefore, local dose comparisons 318 can be generated for the SFRT treatment plan 300 at one or more precise locations to more accurately determine the effects of the SFRT treatment plan 300 on the patient (both positive and negative).

[0076] To generate local dose comparisons 318, the analysis server receives treatment parameters from an SFRT treatment plan 300 for application to a target tissue (e.g., a tumor). These treatment parameters can be obtained from... Figure 1 The system administrator computer 150 receives (e.g., input by a medical practitioner at the system administrator computer 150) and can define non-uniform doses, including maximum and minimum doses for non-uniform doses within a certain distance / volume. SFRT treatment plans may include multiple maximum doses (e.g., local maximum doses 304, 306) and minimum doses of maximum quantity (e.g., local minimum doses 307, 308). Local maximum doses 304, 306 are doses that have a higher value (e.g., dose percentage) within a certain range than all nearby points on dose 302. Local minimum doses 307, 308 are doses that have a lower value (e.g., dose percentage) within a certain range than all nearby points on dose 302.

[0077] Upon receiving multiple local maxima, the analysis server generates an upper non-uniformity curve 320 based at least in part on these local maxima. The analysis server can perform interpolation between adjacent local maxima (e.g., local maxima 304, 306) to generate the upper non-uniformity curve 320. Interpolation can be any number of methods used for interpolation between adjacent local maxima, including but not limited to linear interpolation, cubic spline interpolation, nearest neighbor interpolation, bicubic interpolation, kriging, radial basis function interpolation, Gaussian regression, inverse distance weighted (IDW), B-spline interpolation, and polynomial interpolation. The selected interpolation method can be used to generate a function that includes one or more local maxima (e.g., local maxima 304, 306). It should be noted that the upper non-uniformity curve 320 is based at least in part on the multiple local maxima, but can also be based in part on other functions or parameters (such as the interpolation method used, distance range, etc.).

[0078] Upon receiving multiple local minimum doses, the analysis server generates a lower non-uniformity curve 322 based at least in part on the multiple local minimum doses (including adjacent local minimum doses). The analysis server can perform interpolation between adjacent local minimum doses (e.g., local minimum doses 307, 308) to generate the lower non-uniformity curve 322. Interpolation can be any number of methods used for interpolation between adjacent local minimum doses, including but not limited to linear interpolation, cubic spline interpolation, nearest neighbor interpolation, bicubic interpolation, kriging, radial basis function interpolation, Gaussian regression, inverse distance weighted (IDW), B-spline interpolation, and polynomial interpolation. The selected interpolation method can be used to generate a function that includes one or more local minimum doses (e.g., local minimum doses 307, 308). Note that the lower non-uniformity curve 322 is based at least in part on the multiple local minimum doses, but can also be based in part on other functions or parameters (such as the interpolation method used, distance range, etc.).

[0079] The upper non-uniformity curve 320 and the lower non-uniformity curve 322 provide the upper and lower limits for the dose envelope 316. After generating (or otherwise determining) the upper non-uniformity curve 320 and the lower non-uniformity curve 322, the analysis server determines the dose envelope 316. The dose envelope 316 represents the range of dose variation within the treatment volume, as defined by the upper non-uniformity curve 320 and the lower non-uniformity curve 322. In practice, the dose envelope 316 is defined / bounded at the upper limit by the upper non-uniformity curve 320 and at the lower limit by the lower non-uniformity curve 322. By interpolating between the local maximum dose and the local minimum dose to generate the upper non-uniformity curve 320 and the lower non-uniformity curve 322 (and by extending the dose envelope 316), the analysis server can generate and / or predict local dose contrasts for each location within the dose envelope 316. Therefore, the analysis server can generate local dose comparisons to determine the comparison between the upper non-uniformity curve 320 and the lower non-uniformity curve 322 at any location within the treatment area, thereby enabling graphical and numerical representation / analysis of the dose envelope 316 at any location within the treatment area.

[0080] After determining the upper non-uniformity curve 320 and the lower non-uniformity curve 322, the analysis server generates local dose contrast 318 for one or more locations within the dose envelope 316. This process can be repeated multiple times to generate local dose contrast distributions at multiple (in some cases, each) location points within the treatment volume. The local dose contrast 318 can be graphically illustrated on a slice of the treatment volume, such as... Figures 4 to 6 As shown below, and described in more detail.

[0081] The analysis server generates local dose comparison 318 by calculating the following formula:

[0082] In the above formula, The local dose comparison at the location of interest (e.g., x) is shown in 318. This represents the value of the upper non-uniform curve 320 at the location of interest. This represents the value of the non-uniform curve 322.

[0083] The generated local dose contrast 318 can be used to drive the optimization of SFRT treatment plans 300, such as... Figure 3 Box 204 and path 206 are shown in the diagram. This is when only bixels are optimized. When assigning weights, the goal of the optimization algorithm is to optimize the so-called objective function. Minimize, the objective function is dose The function. Flux bixels can refer to the discrete elements that make up a unit of radiation flux of dose 302. Objective function. The gradient can be written as:

[0084] and Can be expressed as dose Functions:

[0085] in For example, the softmax function (normalized exponential function). and It can be chosen arbitrarily, but if both functions are differentiable, then the local dose contrast 318 (e.g., " The derivative of the flux with respect to the flux can also be calculated analytically and used directly to drive optimization.

[0086] Once the SFRT treatment plan 300 has been optimized, as described in this article, the local dose-to-concentration ratio 318 (or local dose-to-concentration distribution) can be plotted as a heatmap, such as... Figures 4 to 6As shown, the analysis server can generate multiple local dose contrasts to be combined, thereby generating a local dose contrast distribution heatmap. The heatmap of multiple local dose contrasts illustrates the non-uniformity of a radiotherapy treatment plan with heterogeneous radiation doses. The heatmap can be color-coded to quickly and efficiently show dose non-uniformity. For example, higher levels of non-uniformity can be illustrated in red, while lower levels of non-uniformity can be illustrated in blue, with the color gradient spanning between red for higher non-uniformity and blue for lower non-uniformity. Although red and blue are described herein, it should be understood that other colors, shapes, patterns, etc., can also be used to indicate higher and lower levels of dose non-uniformity (e.g., local dose contrast). The heatmap can be overlaid graphically onto diagnostic images of patients who will receive radiotherapy treatment. For example, the heatmap can be overlaid onto a patient's PET scan.

[0087] For example, an analytics server can receive diagnostic images of patients and target tissues. The analytics server can then display these diagnostic images on a screen for users (such as medical practitioners, technicians, patients, etc.) to view.

[0088] Figure 4 The diagram illustrates the dose distribution 406 in the patient's chest 408, which includes an inlet region 402 and an outlet region 404. (As shown in...) Figure 4 As can be seen, when the SFRT treatment plan passes through the patient's chest 408 to reach the target volume 414, the radiation beam (as illustrated in dose distribution 406) is scattered, resulting in greater non-uniformity at the entrance region 402 than at deeper locations within the patient's body (such as at the exit region 404). Figure 4 As shown, the density of points in dose distribution 406 corresponds to dose percentage (e.g., from 0% to 100%), where higher densities (such as those illustrated near inlet region 402) have a higher dose percentage than lower densities (such as those illustrated near outlet region 404).

[0089] Dose distribution 406 allows for the analysis of radiation dose in the SFRT treatment plan to better adjust the radiotherapy plan, thereby minimizing damage to non-target tissues. For example, the local dose contrast distribution 406 at the first region of interest 410 is shown to have a higher local dose than that at the second region of interest 412. However, the local dose contrast between the higher and lower dose values ​​can provide additional useful information for optimizing the SFRT treatment plan.

[0090] Figure 5Three different dose views are illustrated, including a local dose comparison of the SFRT treatment plan. View 502 illustrates the dose distribution 508 of the SFRT treatment plan when applied to the patient's skull. It can be seen that the maximum dose distribution is shown at the target volume 514. View 504 (which shows a diagram of the lower non-uniformity curve 510 of the dose distribution 508) also shows the maximum dose at the target volume 514. View 506 shows a diagram of the upper non-uniformity curve of the SFRT treatment plan, which also shows the maximum dose at the target volume 514. While these three views 502, 504, and 506 show the maximum dose applied to the target volume 514, they do not show the local dose comparison at the target volume 514, which illustrates the effect of the SFRT treatment plan. Views 504 and 506 can be used to generate local dose comparison distribution heatmaps to graphically overlay on the patient's diagnostic images, such as... Figure 6 As shown.

[0091] Figure 6 Nine different views of the SFRT treatment plan are illustrated, including three dose distribution views (views 602, 604, and 606), which show the dose distribution in the first (view 602), second (view 604), and third (view 606) sessions. Figure 6 The diagram illustrates the peak to trough dose ratios in the first field (view 608), the second field (view 610), and the third field (view 612). Figure 6 The diagram also illustrates local dose comparisons in the first (view 614), second (view 616), and third (view 618) sessions. It can be seen that all nine views 602, 604, 606, 608, 610, 614, 616, and 618 illustrate the same SFRT treatment plan; however, the information provided by the different views differs. While view 606 shows the maximum dose distribution at target volume 620, view 618 (which shows local dose comparisons) does not show local dose comparisons at all. This could lead to treatment failure or ineffectiveness. Similarly, peak-to-trough dose ratio views (e.g., views 608, 610, 612) can illustrate the peak-to-trough dose ratio (e.g., calculated by dividing the lower non-uniformity curve by the upper non-uniformity curve), but fail to provide accurate dose information when either the upper or lower non-uniformity curve equals 0.

[0092] Figure 7 This is a flowchart of an example method 700 for planning and administering SFRT treatment. Although several steps are shown, it should be understood that method 700 may include more than [previous steps]. Figure 7 The steps shown may be fewer, more, or different from those shown. Figure 7 The sequence of steps in method 700 is illustrated for illustrative purposes only. It should be understood that the steps can be interpreted in the same way as... Figure 7 The steps of method 700 are executed in different orders as shown.

[0093] At step 710, one or more processors receive a radiotherapy treatment plan for application to target tissue, the radiotherapy treatment plan having a non-uniform dose, which includes multiple local maximum doses and multiple local minimum doses. The radiotherapy treatment plan may include various treatment parameters (e.g., dose distribution, beam morphology, beam energy, fractionation scheme, grid or lattice mode, dose prescription, treatment planning system (TPS) parameters, collimation technique, treatment field size, isocentric positioning, patient fixation, and image guidance protocol) and operating parameters of the radiotherapy machine (e.g., beam energy, dose rate, gantry angle, collimator angle, field size, monitor unit (MU), leaf sequence pattern in multi-leaf collimator (MLC), beam on-time, pulse repetition frequency, examination table position, movement, real-time imaging parameters, calibration settings, quality assurance checks, and system software parameters). The radiotherapy treatment plan may also include patient information (e.g., patient ID, name, date of birth, age, sex, diagnosis, tumor location, treatment plan ID, prescribed dose, fractionation schedule, imaging data, prior treatment, comorbidities, allergies, and medications).

[0094] At step 720, one or more processors generate (i) an upper non-uniformity curve at least partially based on a plurality of local maximum doses and (ii) a lower non-uniformity curve at least partially based on a plurality of local minimum doses. In various embodiments, the upper non-uniformity curve is generated by interpolation between adjacent maximum doses. In at least one embodiment, the interpolation is linear interpolation, but any interpolation method can be used. Similarly, the lower non-uniformity curve is generated by interpolation between adjacent minimum doses. By doing so, an upper limit (e.g., the upper non-uniformity curve) and a lower limit (e.g., the lower non-uniformity curve) are generated to define the dose envelope of the radiotherapy treatment plan. At step 730, one or more processors determine the dose envelope of the radiotherapy treatment plan defined by the upper and lower non-uniformity curves.

[0095] At step 740, one or more processors generate local dose contrasts (or multiple local dose contrasts) of the dose envelope at locations within the dose envelope. As described herein, a local dose contrast can represent the contrast between the upper and lower limits of the dose envelope at a specific location. Various methods can be used to determine local dose contrasts. In one embodiment, local dose contrasts are determined by dividing the difference between the upper and lower limits at a location by the sum of the upper and lower limits at that location. As described above, several local dose contrasts can be generated for various locations within the defined dose envelope, and these local dose contrasts can be used to generate a heatmap of the local dose contrast distribution of multiple local dose contrasts.

[0096] After generating a local dose comparison of the dose envelope, one or more processors can adjust one or more treatment parameters of the spatially fractionated radiotherapy treatment plan. Treatment parameters may include dose parameters associated with the treatment therapy, which may include, but are not limited to, dose, dose amplitude, beam morphology, beam energy, dose prescription, fractionation scheme, treatment field size, isocenter positioning, collimation technique, multi-leaf collimator (MLC) configuration, grid or lattice mode, peak-to-trough dose ratio (PVDR), treatment planning system (TPS) optimization settings, image guidance protocol, patient fixation method, beam delivery angle, and / or beam angle. In some embodiments, treatment parameters include physical operating parameters of the radiotherapy machine administering the spatially fractionated radiotherapy treatment plan. The physical operating parameters of a radiotherapy machine may include, but are not limited to, beam energy, dose rate, gantry angle, collimator angle, field size, multi-leaf collimator (MLC) blade position, beam on-time, pulse repetition frequency, source-to-axis distance (SAD), source-to-skin distance (SSD), treatment table position, table rotation, imaging system settings, mechanical isocenter accuracy, accelerator magnet current, cooling system status, and / or power supply settings. Adjusting one or more treatment parameters may include transmitting one or more control signals to the radiotherapy machine, which causes actuation of one or more actuators of the radiotherapy machine.

[0097] In some embodiments, local dose contrast at a location can trigger one or more actions. For example, a target threshold for local dose contrast can set a lower limit for local dose contrast at a target tissue (e.g., a tumor). In such embodiments, if the target volume at a location does not have a local dose contrast that meets (e.g., exceeds) the target threshold, the analysis server can adjust one or more operational or treatment parameters to increase the local dose contrast at the target tissue. Similarly, a threshold for non-target tissues can set an upper limit for local dose contrast at non-target tissues (e.g., adjacent tissues). In response to a local dose contrast at a non-target tissue exceeding the threshold, the analysis server can adjust one or more operational or treatment parameters to decrease the local dose contrast.

[0098] The foregoing method descriptions and process flowcharts are provided as illustrative examples only and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. The steps in the foregoing embodiments can be performed in any order. Words such as "then," "next," etc., are not intended to limit the order of steps; these words are only used to guide the reader through the description of the method. Although the process flowcharts may describe operations as sequential processes, many operations can be performed in parallel or simultaneously. Furthermore, the order of operations can be rearranged. A process can correspond to a method, function, procedure, subroutine, subroutine, etc. When a process corresponds to a function, the termination of the process can correspond to the function returning to the calling function or the main function.

[0099] The various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been generally described above with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the described functionality in different ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of the invention.

[0100] Implementations using computer software can be implemented using software, firmware, middleware, microcode, hardware description languages, or any combination thereof. Code segments or machine-executable instructions can represent procedures, functions, subroutines, programs, routines, subroutines, modules, software packages, classes, or any combination of instructions, data structures, or program statements. Code segments can be coupled to other code segments or hardware circuitry by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc., can be passed, forwarded, or transmitted via any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0101] The actual software code or dedicated control hardware used to implement these systems and methods does not limit the invention. Therefore, since the operation and behavior of the systems and methods are described without reference to specific software code, it should be understood that the software and control hardware can be designed to implement the systems and methods based on the descriptions herein.

[0102] When implemented in software, these functions may be stored as one or more instructions or code on a computer-readable non-transitory storage medium or a processor-readable storage medium. The steps of the methods or algorithms disclosed herein may be embodied in a processor-executable software module that may reside on a computer-readable or processor-readable storage medium. Non-transitory computer-readable or processor-readable media include both computer storage media and tangible storage media that facilitate the transfer of a computer program from one place to another. Non-transitory processor-readable storage media may be any available medium accessible to a computer. By way of example and not limitation, such non-transitory processor-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, or any other tangible storage medium that may be used to store desired program code in the form of instructions or data structures and that may be accessible to a computer or processor. As used herein, disks and optical discs include optical discs (CDs), laser discs, optical discs, digital versatile optical discs (DVDs), floppy disks, and Blu-ray discs, wherein disks typically copy data magnetically, while optical discs use lasers to copy data optically. Combinations of the above should also be included within the scope of computer-readable media. Additionally, operations of a method or algorithm may reside as one or any combination or set of code and / or instructions on a non-transitory processor-readable medium and / or computer-readable medium, which may be incorporated into a computer program product.

[0103] The foregoing description of the disclosed embodiments is intended to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the appended claims and the principles and novel features disclosed herein.

[0104] While various aspects and embodiments have been disclosed, other aspects and embodiments are contemplated. The various aspects and embodiments disclosed are for illustrative purposes and are not intended to limit the true scope and spirit indicated by the appended claims.

Claims

1. A method for adjusting a spatially fractionated radiotherapy treatment plan, the method comprising: A radiotherapy treatment plan for application to target tissue is received by one or more processors, the radiotherapy treatment plan having a non-uniform dose, the non-uniform dose comprising: Multiple local maximum doses, and Multiple local minimum doses; The processor generates (i) an upper non-uniformity curve based at least in part on the plurality of local maximum doses and (ii) a lower non-uniformity curve based at least in part on the plurality of local minimum doses; The one or more processors determine the dose envelope of the radiotherapy treatment plan defined by the upper non-uniformity curve and the lower non-uniformity curve; and Local dose comparisons of the dose envelope are generated at locations within the dose envelope by the one or more processors, wherein the one or more processors are configured to adjust treatment parameters of the radiotherapy treatment plan based at least in part on the generated local dose comparisons.

2. The method according to claim 1, further comprising: In part, in response to the local dose contrast exceeding a threshold, the treatment parameters of the radiotherapy treatment plan are adjusted by the one or more processors to reduce the local dose contrast.

3. The method according to claim 1, further comprising: The one or more processors generate a local dose contrast distribution that includes multiple local dose contrasts; as well as The one or more processors generate a heatmap of the non-uniformity of the radiotherapy treatment plan based on the local dose contrast distribution.

4. The method of claim 1, wherein the local dose comparison is generated in part based on the upper non-uniformity curve and the lower non-uniformity curve at the location.

5. The method of claim 1, wherein generating the upper non-uniform curve comprises: The non-uniform dose is interpolated between adjacent local maximum doses within the plurality of local maximum doses by the one or more processors.

6. The method according to claim 5, further comprising: The non-uniform dose is linearly interpolated between adjacent local maximum doses within the plurality of local maximum doses by the one or more processors.

7. The method according to claim 3, further comprising: The diagnostic images of the target tissue are received by the one or more processors; The diagnostic image of the target tissue is displayed on a display by the one or more processors; as well as The heatmap is overlaid on the diagnostic image of the target tissue by the one or more processors.

8. The method according to claim 1, further comprising: The one or more processors adjust one or more operating parameters of the radiotherapy machine according to the radiotherapy treatment plan; as well as The one or more processors transmit instructions to the radiotherapy machine to administer the radiotherapy treatment plan to the target tissue.

9. A system for adjusting a spatially fractionated radiotherapy treatment plan, the system comprising: One or more processors; as well as A computer-readable non-transitory storage medium includes instructions that, when executed by the one or more processors, cause the one or more processors to perform a method comprising: Receive a radiotherapy treatment plan for application to target tissue, the radiotherapy treatment plan having a non-uniform dose, the non-uniform dose comprising: Multiple local maximum doses, and Multiple local minimum doses; Generate (i) an upper non-uniformity curve based at least in part on the plurality of local maximum doses and (ii) a lower non-uniformity curve based at least in part on the plurality of local minimum doses; Determine the dose envelope of the radiotherapy treatment plan defined by the upper non-uniformity curve and the lower non-uniformity curve; and A local dose comparison of the dose envelope is generated at a location within the dose envelope, wherein one or more processors are configured to adjust treatment parameters of a radiotherapy treatment plan based at least in part on the generated local dose comparison.

10. The system of claim 9, wherein the method further comprises: In part, in response to the local dose contrast exceeding a threshold, the treatment parameters of the radiotherapy treatment plan are adjusted to reduce the local dose contrast.

11. The system of claim 9, wherein the method further comprises: Generate local dose contrast distributions that include multiple local dose contrasts; as well as A heatmap of the non-uniformity of the radiotherapy treatment plan is generated based on the local dose contrast distribution.

12. The system of claim 9, wherein the local dose comparison is generated in part based on the upper non-uniformity curve and the lower non-uniformity curve at the location.

13. The system of claim 9, wherein generating the upper non-uniform curve comprises: The non-uniform dose is interpolated between adjacent local maximum doses within the plurality of local maximum doses.

14. The system of claim 13, wherein the method further comprises: The non-uniform dose is linearly interpolated between adjacent local maximum doses within the plurality of local maximum doses.

15. The system of claim 11, wherein the method further comprises: Receive diagnostic images of the target tissue; The diagnostic image of the target tissue is displayed on the display; as well as The heatmap is overlaid on the diagnostic image of the target tissue.

16. The system of claim 9, wherein the method further comprises: Adjust one or more operating parameters of the radiotherapy machine according to the radiotherapy treatment plan; as well as Instructions are transmitted to the radiotherapy machine to administer the radiotherapy treatment plan to the target tissue.

17. A computer-readable non-transitory storage medium comprising instructions for adjusting a spatially segmented radiotherapy treatment plan, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform a method comprising: Receive a radiotherapy treatment plan for application to target tissue, the radiotherapy treatment plan having a non-uniform dose, the non-uniform dose comprising: Multiple local maximum doses, and Multiple local minimum doses; Generate (i) an upper non-uniformity curve based at least in part on the plurality of local maximum doses and (ii) a lower non-uniformity curve based at least in part on the plurality of local minimum doses; Determine the dose envelope of the radiotherapy treatment plan defined by the upper non-uniformity curve and the lower non-uniformity curve; and A local dose comparison of the dose envelope is generated at a location within the dose envelope, wherein one or more processors are configured to adjust treatment parameters of a radiotherapy treatment plan based at least in part on the generated local dose comparison.

18. The computer-readable non-transitory storage medium of claim 17, wherein the method further comprises: In part, in response to the local dose contrast exceeding a threshold, the treatment parameters of the radiotherapy treatment plan are adjusted to reduce the local dose contrast.

19. The computer-readable non-transitory storage medium of claim 17, wherein the method further comprises: Generate local dose contrast distributions that include multiple local dose contrasts; as well as A heatmap of the non-uniformity of the radiotherapy treatment plan is generated based on the local dose contrast distribution.

20. The computer-readable non-transitory storage medium of claim 17, wherein the local dose comparison is generated in part based on the upper non-uniformity curve and the lower non-uniformity curve at the location.

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