WO2026020021A1 - Planification de radiothérapie comprimée - Google Patents

Planification de radiothérapie comprimée

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Publication number
WO2026020021A1
WO2026020021A1 PCT/US2025/038084 US2025038084W WO2026020021A1 WO 2026020021 A1 WO2026020021 A1 WO 2026020021A1 US 2025038084 W US2025038084 W US 2025038084W WO 2026020021 A1 WO2026020021 A1 WO 2026020021A1
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matrix
dose
voxels
optimized
tumor
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Masoud ZAREPISHEH
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Memorial Sloan Kettering Cancer Center
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Memorial Sloan Kettering Cancer Center
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • Embodiments of the present disclosure are related to a system, method, and computer program product for creating an optimized radiotherapy treatment.
  • Radiation therapy is used to treat over half of all cancer patients, using specialized machines to direct high-energy beams at tumors, aiming to damage cancer cells while minimizing harm to nearby healthy tissues.
  • Customizing the shape and intensity of radiation beams for each patient involves solving computationally challenging large-scale optimization problems that need to be solved within a clinical timeframe.
  • CompressRTP A framework to compress radiotherapy treatment planning is needed to enable quick and accurate solutions to optimization problems.
  • This framework (known as “CompressRTP” herein) comprises a set of algorithms and mathematical models designed to provide a compressed and hierarchical representation of radiotherapy data, addressing several critical issues in alternate systems including: 1) the sub-optimality of radiotherapy plans and their outcomes due to using gross approximations for computational efficiencies; 2) underutilization of recent advanced radiotherapy machines due to the gap between software and hardware stemming from the computational challenges; and 3) unnecessary plan complexity and delivery inefficiency due to the lack of global smoothness and explicit constraints in the existing mathematical models.
  • a method for optimizing a radiotherapy treatment comprises discretizing a region of interest of a patient into a plurality of voxels, wherein the plurality of voxels comprise tumor voxels and non-tumor voxels; receiving a prescribed radiation dose for each tumor voxel; discretizing one or more radiation emitters of a radiotherapy machine into a plurality of two-dimensional (2D) beamlets; defining an initial intensity vector for each beamlet; determining a radiation dose delivered to each voxel by each 2D beamlet, the radiation dose based on the initial intensity vector, thereby creating a dose influence matrix; compressing the dose influence matrix; applying an automated planning algorithm to approximate the prescribed radiation dose for tumor voxels and minimize the radiation dosage for non-tumor voxels, thereby creating an optimized intensity vector; and providing an optimized radiotherapy treatment for the patient based on the optimized intensity vector.
  • 2D two-dimensional
  • a system for optimizing a radiotherapy treatment comprise a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising discretizing a region of interest of a patient into a plurality of voxels, wherein the plurality of voxels comprise tumor voxels and non-tumor voxels; receiving a prescribed radiation dose for each tumor voxel; discretizing one or more radiation emitters of a radiotherapy machine into a plurality of two-dimensional (2D) beamlets; defining an initial intensity vector for each beamlet; determining a radiation dose delivered to each voxel by each 2D beamlet, the radiation dose based on the initial intensity vector, thereby creating a dose influence matrix; compressing the dose influence matrix; applying an automated planning algorithm to approximate the prescribed radiation dose for tumor voxels and minimize the radiation dosage for non-tumor voxels,
  • FIG. 1 is a graph illustrating the performance of treatment planning algorithms.
  • FIG. 2 is a block diagram of comparison between a manual and automated workflow, in accordance with one or more embodiments of the present disclosure.
  • FIG. 3 is a flow chart illustrating an exemplary method of optimizing a radiotherapy treatment for a patient, in accordance with one or more embodiments of the present disclosure.
  • FIG. 4A is a graph illustrating the distribution of singular values with exponential decay in a low rank data structure, in accordance with one or more embodiments of the present disclosure.
  • FIG. 4B is an illustration of a sparse plus low rank compression decomposition, in accordance with one or more embodiments of the present disclosure.
  • FIG. 4C is a graph illustrating a dose discrepancy without compression, in accordance with one or more embodiments of the present disclosure.
  • FIG. 4D is a graph illustrating a dose discrepancy with compression, in accordance with one or more embodiments of the present disclosure.
  • FIG. 5B is a graphic illustrating a radiotherapy treatment plan with compression, in accordance with one or more embodiments of the present disclosure.
  • FIG. 7B is pseudocode for an exemplary randomized R2-Trust algorithm, in accordance with one or more embodiments of the present disclosure.
  • FIG. 8 is pseudocode for an exemplary Randomized Minor-value Rectification (RMR) algorithm, in accordance with one or more embodiments of the present disclosure.
  • FIG. 9 is a series of graphs illustrating the performance of the RMR algorithm, in accordance with one or more embodiments of the present disclosure.
  • FIG. 10A is a series of graphs illustrating the performance of different algorithms for lung patients, in accordance with one or more embodiments of the present disclosure.
  • FIG. 10B is a series of graphs illustrating the discrepancies in Dose Volume Histogram for one lung patients, in accordance with one or more embodiments of the present disclosure.
  • FIG. 11A is a graphic representation of dosimetric data in a 3D tensor form, in accordance with one or more embodiments of the present disclosure.
  • FIG. 11B is a graphic representation of dosimetric data in a 3D tensor form, in accordance with one or more embodiments of the present disclosure.
  • FIG. 12 is an exemplary computing node.
  • the term “exemplary” is used in the sense of “example,” rather than “ideal.” Moreover, the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of one or more of the referenced items.
  • RT Radiation therapy
  • RT is integral to cancer treatment, utilized in approximately half of all cases, either alone or in combination with other treatments like surgery or chemotherapy.
  • RT involves using specialized machines to emit and direct high-energy radiation beams at tumors, with the primary goal of destroying cancer cells while minimizing damage to healthy tissues.
  • This process requires precise optimization of machine parameters, such as beam shapes and angles, tailored to each patient's unique anatomy. Optimization involves solving large-scale, constrained, non-linear optimization problems swiftly within clinical time constraints. The urgency of this task is heightened in modern online adaptive radiotherapy techniques, where a rapid solution is essential since patients remain immobilized on the treatment couch during preparation. Delays not only compromise patient comfort but can also affect treatment outcomes, as any rapid anatomical changes (e.g., bladder filling in prostate cancer) can render treatment plans based on initial anatomy sub-optimal. As such, quickly solving these optimization problems is crucial.
  • Radiotherapy treatment planning is a decision-making challenge focused on balancing tumor control against the risk of radiotoxic complications to nearby organs-at-risk (OARs).
  • OARs organs-at-risk
  • Treatment planning algorithms navigate this search space to select a plan with the most favorable trade-offs.
  • FIG. 1 illustrates this concept: circles 101 represent various treatment options with differing trade-offs, while the arrows indicate the algorithm's route to identify an optimal plan.
  • FIG. 1 depicts an enhancement to this process by introducing improved options (circles 102), generated using precise dosimetry, helping to close the optimality gap in alternative practices.
  • a range of automated planning algorithms are available, from classical techniques (e.g, knowledge-based prioritized optimization) to modem Al-based approaches (deep/reinforcement learning). Although the choice of automated planning algorithm influences the pathway to the optimal plan, the actual treatment options are defined by the specifics of the patient's anatomy and the principles of radiation physics.
  • Embodiments of the present disclosure provide a compact yet accurate data representation, circumventing the need for gross approximation.
  • the data is presented in a hierarchical format, enabling the use of multi-scale algorithms where the more critical parts of the data are prioritized and ingested in treatment planning optimization, reducing computational time and memory consumption.
  • a primary challenge in integrating Al-based dosimetric data into treatment planning optimization is managing the large and dense matrices resulting from accurate dose calculations, including detailed radiation scattering components.
  • dosimetric data matrices used in optimization are -98% sparse (with only 2% non-zero elements), whereas accurate, detailed matrices are completely dense. This denseness demands -50 times more memory and significantly slows down optimization processes, potentially making them thousands of times slower and unsolvable due to the cubic computational complexity of optimization algorithms relative to data size.
  • Accurate dosimetric data matrices render treatment planning optimization impractical, particularly for OART.
  • Alternate correction techniques suffer from a “short memory” problem: each time the system inquires an accurate dose calculation and computes the data discrepancy, it overrides and forgets the previous accurate dose calculations, resulting in more correction steps.
  • the approach in embodiments of the present disclosure adds a correction step in the form of a rank-one update, allowing for the preservation of all previous accurate calculations and reduction of the number of required corrections.
  • This “long-memory” correction strategy not only improves computational efficiency but also enhances plan quality.
  • FIG. 2 illustrates a manual workflow 201, using a fast and inaccurate pencil beam dose calculation, and an automated workflow 202, using fast and accurate Al-based dose calculations and data compressions.
  • Each workflow leads to treatment planning optimization 203, comprising optimization and intermediate dose calculation in a correction loop, as well as a final dose calculation.
  • workflow 202 capitalizes on the data compression and accurate Al-based dose to provide a faster process.
  • Modern radiotherapy machines offer more flexibility and degrees of freedom, such as moving the couch while the patient receives treatment, to better modulate the radiation beams and generate more conformal radiation to the tumor. However, leveraging these extra degrees of freedom leads to larger and more computationally complex optimization problems.
  • Embodiments of the present disclosure bridge this gap to fully exploit the capabilities of modem machines.
  • Some embodiments leverage redundancy in data and captures variations among neighboring machine parameters (e.g., neighboring couch positions, iso center locations, beam angles) in a computationally manageable low-rank matrix. This approach is analogous to using low-rank matrices in modern large language models to capture differences between contexts.
  • Robust optimization in radiotherapy treatment planning is crucial for ensuring effective and safe cancer treatment.
  • robust optimization techniques are essential to account for these variables and deliver consistent therapeutic doses to the tumor while minimizing exposure to healthy tissues.
  • robust treatment plans can better withstand the unpredictable nature of real-world conditions, thus improving treatment efficacy and reducing the risk of adverse side effects.
  • robust optimization increases the computational burden and slows down the treatment planning process, often necessitating more gross approximations that could compromise plan quality.
  • several uncertainty scenarios and patient movements are simulated upfront, each resulting in an individual large influence matrix, which are incorporated into treatment planning optimization.
  • Embodiments of the present disclosure leverage the correlation in data between these scenarios and captures the differences among them in the form of a low-rank matrix, allowing for much more computationally efficient optimization.
  • a low-rank matrix captures the variations in dosimetric data when the patient moves, significantly enhancing the optimization process.
  • IMRT Intensity Modulated Radiation Therapy
  • Plan complexity may be reduced by smoothing the intensity map of each beam during fluence map optimization (FMO). This may be achieved by adding an additional “regularization” term to the objective function that measures local variations in adjacent beamlets to discourage fluctuating beamlet intensities.
  • FMO fluence map optimization
  • a significant limitation of this method is its focus on local complexity within each beam, assessing variations in adjacent beamlets but overlooking the global complexity of the plan.
  • Another challenge is that achieving an optimal balance between plan complexity and dosimetric quality necessitates careful fine- tuning of the importance weight associated with the smoothness term in the objective function.
  • CompressRTP addresses this issue by providing a compressed representation of the fluence map where irrelevant fluctuated fluences are explicitly excluded from the decision space. In this case, CompressRTP is intended not for computational efficiency, but to provide a more realistic representation of data and decision variables and to induce local and global smoothness and generate a high-quality plan that can be efficiently delivered.
  • FIG. 3 is a flow chart illustrating an exemplary method 300 of optimizing a radiotherapy treatment for a patient.
  • exemplary method 300 e g., steps 301- 308 may be performed by a processor automatically, or in response to a request from a user.
  • the exemplary method 300 for optimizing a radiotherapy treatment may include one or more of the following steps.
  • the method may include discretizing a region of interest of the patient into a plurality of voxels, wherein the plurality of voxels comprises tumor and non-tumor voxels.
  • the method may include receiving a prescribed radiation dose for each tumor voxel.
  • the method may include discretizing one or more radiation emitters of a radiotherapy machine into a plurality of two-dimensional (2D) beamlets. Each 2D beamlet is indexed
  • the method may include defining an initial intensity vector for each beamlet.
  • the method may include determining a radiation dose delivered to each voxel by each 2D beamlet, the radiation dose based on the initial intensity vector, thereby creating a dose influence matrix.
  • the radiation dose delivered to each voxel i from each beamlet j with unit intensity is precalculated and represented by a t j, forming the matrix A - the dose influence matrix.
  • This matrix is typically large, containing about 100,000 to 1,000,000 rows corresponding to the patient's voxels, and 1,000 to 100,000 columns representing the beamlets/spots of the radiotherapy machine.
  • the method may include compressing the dose influence matrix.
  • the method may include applying an automated planning algorithm to approximate the prescribed radiation dose for tumor voxels and minimize the radiation dosage for non-tumor voxels, thereby creating an optimized intensity vector.
  • the method may include providing an optimized radiotherapy treatment for the patient based on the optimized intensity vector.
  • the objective is to optimize the intensities of the beamlets, denoted by x, in order to achieve a desired radiation dose, Ax, that is delivered to the patient’s body.
  • Ax a desired radiation dose
  • the aim is to achieve radiation dose that approximates the dose prescribed by a physician in step 302, while for healthy voxels the aim is to minimize the radiation dose as much as possible.
  • the optimization problem can be described in the following general form:
  • Embodiments of the present disclosure include algorithms to compress the large matrix A, resulting in a hierarchical and compact yet accurate representation of the dosimetric data. This approach enhances computational efficiency without compromising data integrity, contrasting with existing gross approximations. It also enables the incorporation of modern Al-based accurate dose calculations, which produce dense dose influence matrices.
  • the hierarchical data structure facilitates the use of multi-scale (also known as multi-resolution or progressive sampling) optimization algorithms, where more critical data parts are prioritized in the optimization process.
  • Embodiments of the present disclosure also include algorithms to compress the fluence map x, resulting in new formulations for /j(x) and h(x) to improve delivery efficiency without compromising dosimetric plan quality.
  • the function /j(x) ensures both local and global smoothness, unlike current techniques that only provide local smoothness.
  • the function /i(x) will exclude overly complex plans from the decision space, addressing the issue of manual fine-tuning of the parameter present in alternate methods.
  • Embodiments of the present disclosure include algorithms for patient discretization into small cubical voxels.
  • An Al-based dose prediction may identify critical areas (e.g., high dose regions, high dose-gradient regions, tumor low dose regions) requiring more voxels.
  • the Sparse Voxel Octree may be employed for importance sampling with a hierarchical voxel data structure. This approach reduces the number of required voxels and, similar to matrix compression, enables the use of multi-scale optimization algorithms by prioritizing more critical data parts.
  • Embodiments of the present disclosure include a low-rank update adding a new term to the objective function of Problem (1) every time an on-demand forward dose calculation is performed.
  • This idea is analogous to using low-rank matrices in modern large language models to capture contextual differences and can be applied to various aspects:
  • Modern radiotherapy machines offer more flexibility and degrees of freedom, which are currently underutilized due to computational challenges.
  • Embodiments of the present disclosure allow simultaneous optimization of many machine parameters, including beam angles, couch positions, iso centers, and collimator rotations, by performing on-demand forward dose calculations for each parameter variation.
  • pre-computing the dose for all potential variations and then applying matrix compression can also be employed. While this approach has a higher upfront calculation cost, it reduces computational costs during downstream optimization.
  • Table 1 summarizes different components of the framework of embodiments of the present disclosure.
  • Compression tools are integral to modem life, enabling the streaming of high- quality movies to mobile devices.
  • most compression techniques require decompression of data, which is both time- and resource-intensive.
  • Embodiments of the present disclosure include matrix compression tools that do not require decompression in the downstream optimization process.
  • S is a sparse matrix containing large-magnitude elements (e.g., > l%max(A))
  • L includes smaller elements, mainly representing scattering dose.
  • a «S approximations
  • FIG. 4A matrix A demonstrates a low-rank, compressible structure, evidenced by the exponential decay in singular values (solid line in Fig. 4A).
  • the scattering matrix L is even more compressible, evidenced by sharper exponential decay in its singular values (dotted lines in FIG. 4A).
  • FIG. 4A illustrates the distribution of singular values with exponential decay, revealing the data compressibility for the original data matrix A (solid line), and even more prominently for the scattering matrix L (dotted line) — this suggests the use of “sparse plus low-rank” compression (also known as low-rank plus sparse), as shown in FIG. 4B
  • FIG. 4B illustrates a sparse plus low-rank decomposition of matrix A.
  • L HW
  • H Singular Value Decomposition
  • NMF Nonnegative Matrix Factorization
  • SVD and NMF provides a simple implementation; however, they require the pre-selection of the rank and do not optimally decompose the matrix A.
  • a new algorithm FIGs. 6A-7B that reads the matrix A and the desired level of accuracy as inputs and outputs the sparse matrix S and low-rank matrices H and W.
  • FIG. 4C illustrates large dose discrepancies between the accurate dose (Ax: solid lines) and the optimized dose without compression (Sx: dashed lines)
  • FIG. 4D illustrates small dose discrepancies between the accurate dose (Ax: solid lines) and the optimized dose with compression (Sx+HWx: dashed lines).
  • AAA Analytical Anisotropic Algorithm
  • FIG. 5A with its corresponding dose-volume histogram (DVH) represented by lines with triangle marks in FIG. 5C.
  • FIG. 5B an improved plan created using accurate compressed dosimetry is shown in FIG. 5B, with its DVH depicted by lines with square marks in FIG. 5C.
  • FIG. 5A-C highlight the impact of using compressed data on the quality of the final treatment plan.
  • treatment planning with accurate compressed dosimetry significantly reduces radiation exposure to critical organs like the bladder and rectum. This is in contrast to plans based on a gross sparse approximation, depicted in FIG. 5A and indicated by lines with triangle marks in FIG. 5C.
  • Both types of plans are imported into the FDA-approved Eclipse system for final dose calculations and leaf sequencing. Additionally, a correction step can be applied to both plans for enhanced accuracy.
  • FIG. 6A illustrates a proposed “sparse plus low-rank” matrix decomposition algorithm tailored for radiotherapy data, called Recursive Trust-Region (R-Trust).
  • R-Trust Recursive Trust-Region
  • FIG. 7 Two improved versions of the R-Trust algorithm, Rectified R-Trust (R2-Trust) and Randomized R2-Trust (R3-Trust), are presented in FIG. 7.
  • FIG. 6B compares the performance of R3- Trust against well-known algorithms in the literature. On this graph shown in FIG. 6B, the x- axis shows the relative number of non-zero (NNZ) elements, with lower values being preferable, and the y-axis indicates the relative Frobenius norm error.
  • NZ non-zero
  • the lines marked with circles labeled R3-Trust denote a proposed algorithm, with computational times for each point marked on the plot.
  • the proposed algorithm significantly outperforms alternate ones illustrated by lines marked with triangles (labeled VB, GoDec, ADMM) by providing greater compression, lower error, and achieving these results in a much shorter timeframe (on the scale of seconds), thereby optimizing the treatment planning process in radiotherapy.
  • FIGs. 7A-B provides the two more efficient versions of the R-Trust algorithm, called R2-Trust and R3-Trust.
  • the sparse plus low-rank compression in some embodiments of the present disclosure is a powerful tool; however, in some applications of radiotherapy, it might be preferred to use the sparse-only compression technique due to it easier implementation.
  • the key of sparse-only compression is to carefully sample and scale the elements of the original dense matrix A using randomization techniques to create a sparse sketch matrix B that minimizes in the creation of the space sketch matrix B.
  • Matrix sparsification has been studied in the machine learning community for applications such as low-rank approximation and principal component analysis (PCA).
  • FIG. 9 provides detailed comparisons of the new RMR algorithm and the existing algorithms (AHK06, AKL13, DZ11) with respect to the different levels of sparsity in the output sparse matrix for one patient.
  • the standard deviation band is plotted for all metrics (over 5 runs), however, due to the robust performance of the algorithms, the bands are not visible except in the feasibility gap plot.
  • all randomized algorithms surpass the current naive approach in balancing accuracy and sparsity.
  • AHK06 and RMR stand out, exhibiting superior performance.
  • FIG. 10A offers a high-level comparison across 10 patients at a fixed relative sparsity level of 98%, which can also be interpreted as a fixed computational time for the constrained optimization problem. Confirming the results of FIG. 8 for more patients, this figure demonstrates the superior performance of AHK06 in terms of the L2-norm error and algorithm runtime, while highlighting the significant advantage of the RMR algorithm in reducing optimality and feasibility gaps.
  • FIG. 10B showcases three DVH plots: the naive approach (left), representing current practice; the AHK06 algorithm (middle), representing the most competitive existing approach; and the proposed RMR algorithm (right).
  • dashed lines illustrate the approximated radiation dose, BX B , used in the optimization problem
  • solid lines depict the actual radiation dose, Ax B .
  • the gap between the solid and dashed lines indicates the discrepancies resulting from using the approximated matrix B in the optimization problem, with a smaller gap being preferred. It is readily apparent that the gap is much smaller, especially for the tumor, when using the RMR algorithm, where the solid and dashed lines are closely aligned and often overlap, making the dashed lines nearly invisible.
  • dosimetric data is represented as a 2-dimensional (2D) matrix, but inherently, it is a 6D entity combining 3D spatial voxel coordinates, 2D beamlet locations, and ID gantry angle. Additional dimensions can be added corresponding to other machine parameters, including couch position, iso center location, and collimator angle.
  • This data in its natural tensor form, a more compact representation can be achieved because the inherent redundancies and correlations between data components become more apparent than in a collapsed 2D matrix format. For example, by segregating data matrices for different gantry angles and stacking them into a 3D tensor (as shown in FIG. 11 A), and then applying tensor-train decomposition, the shared and distinct information among beams can be effectively captured and compressed.
  • This technique can also facilitate robust optimization by segregating data matrices for different uncertainty scenarios (as shown in FIG. 11B). Results show that transitioning from 2D to 3D leads to a significant increase in data compression, from approximately 98% to 99%. Enhancing the compression rate from 98% to 99% effectively reduces the dosimetric data and memory requirements by half. More importantly, an enhanced compression rate could result in an approximately 8-fold reduction in treatment planning optimization time, due to the cubic complexity inherent in optimization algorithms. This method marks a substantial improvement in making treatment planning more efficient and feasible, especially when adopting higher-dimensional tensor representation of data to enable optimization of many machine parameters for modern linear accelerators. [0085] Fluence Compression
  • Fluence compression is intended not for computational efficiency, but to provide a more realistic representation of data and decision variables to generate a high-quality plan that can be efficiently delivered.
  • a smoothing term /)(%)
  • is added in Problem (1), where the matrix P is the total variation matrix, measuring the local intensity variations in the neighboring beamlets.
  • a new matrix P incorporates global smoothness, constructed from the following components:
  • P [H;2V;4D]
  • H, V, and D represent horizontal, vertical, and diagonal components in the wavelet basis.
  • 1-dimensional wavelets sort them according to their frequencies, remove low-frequency components (removing fewer components in the direction of leaf movement), and then perform a tensor product.
  • the total variation matrix used in current practice is effectively the discrete differential matrix corresponding to the first derivative.
  • differential matrices of higher derivative orders can be applied.
  • SVD can be performed on the influence matrix, inverting the singular values, then adding the resulting matrix into the P matrix.
  • a similar approach can be used when the simultaneous optimization of many machine parameters (e.g., iso center, beam angle, couch position) is desirable. In such cases, either a very approximate upfront calculation is performed with some occasional forward dose calculations or only occasional forward dose calculations are afforded. This method allows for efficient and accurate optimization even with limited computational resources.
  • machine parameters e.g., iso center, beam angle, couch position
  • a Sparse Voxel Octree is a data structure used in computer graphics and 3D rendering to efficiently represent and manage volumetric data. It leverages an octree, a tree structure where each node has up to eight children, to recursively divide a three-dimensional space into smaller regions or voxels.
  • the SVO has a hierarchical data structure that enhances its efficiency and functionality in managing volumetric data.
  • Embodiments of the present disclosure use deep learning to predict the 3D dose distribution and then applying SVO to discretize the patient’s body. This contrasts with the practice of using regular grids or point clouds.
  • Multi-scale optimization also known as multi-resolution or progressive optimization
  • multi-resolution or progressive optimization is an approach in computational optimization that tackles problems by solving them at various levels of detail or resolution.
  • This technique leverages the idea that solving a coarse-grained, simplified version of a problem can provide valuable insights and a good starting point for addressing finer, more detailed versions.
  • Optimization algorithms utilizing this approach often start with a rough approximation of the problem, where computational costs are lower, and progressively refine the solution by increasing the resolution or adding complexity in stages.
  • the hierarchical data structure provided in the proposed matrix compression and sparse voxel octree enable the use of these techniques.
  • FIG. 12 a schematic of an example of a computing node is shown.
  • Computing node 10 is only one example of a suitable computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments described herein. Regardless, computing node 10 is capable of being implemented and/or performing any of the functionality set forth hereinabove.
  • computing node 10 there is a computer system/server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations.
  • Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.
  • Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system.
  • program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types.
  • Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer system storage media including memory storage devices.
  • computer system/server 12 in computing node 10 is shown in the form of a general-purpose computing device.
  • the components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.
  • Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures.
  • bus architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, Peripheral Component Interconnect (PCI) bus, Peripheral Component Interconnect Express (PCIe), and Advanced Microcontroller Bus Architecture (AMBA).
  • Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and nonremovable media.
  • System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32.
  • Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media.
  • storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a "hard drive").
  • a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk")
  • an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media
  • each can be connected to bus 18 by one or more data media interfaces.
  • memory 28 may include at least one program product having a set (e.g.. at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.
  • Program/utility 40 having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment.
  • Program modules 42 generally carry out the functions and/or methodologies of embodiments as described herein.
  • Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (VO) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18.
  • LAN local area network
  • WAN wide area network
  • public network e.g., the Internet
  • the present disclosure may be embodied as a system, a method, and/or a computer program product.
  • the computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.
  • the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
  • the computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • a non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD- ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • SRAM static random access memory
  • CD- ROM compact disc read-only memory
  • DVD digital versatile disk
  • memory stick a floppy disk
  • a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon
  • a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiberoptic cable), or electrical signals transmitted through a wire.
  • Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.
  • the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
  • Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
  • These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the fimctions/acts specified in the flowchart and/or block diagram block or blocks.
  • These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block may occur out of the order noted in the figures.
  • two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

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  • General Health & Medical Sciences (AREA)
  • Epidemiology (AREA)
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  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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  • Radiation-Therapy Devices (AREA)

Abstract

L'invention concerne des systèmes et des procédés pour optimiser un traitement par radiothérapie pour un patient. Un procédé d'optimisation du traitement par radiothérapie consiste à discrétiser une région d'intérêt du patient en une pluralité de voxels, les voxels comprenant des voxels tumoraux et des voxels non tumoraux ; recevoir une dose de rayonnement prescrite pour chaque voxel de tumeur ; discrétiser un ou plusieurs émetteurs de rayonnement d'une machine de radiothérapie en une pluralité de petits faisceaux bidimensionnels ; déterminer une dose de rayonnement délivrée à chaque voxel par chaque petit faisceau 2D, la dose de rayonnement sur la base du vecteur d'intensité initial, créant ainsi une matrice d'influence de dose ; comprimer de la matrice d'influence de dose ; appliquer un algorithme de planification automatisé pour approximer la dose de rayonnement prescrite pour des voxels tumoraux et réduire au minimum le dosage de rayonnement pour des voxels non tumoraux, créant ainsi un vecteur d'intensité optimisé ; et fournir un traitement de radiothérapie optimisé pour le patient sur la base du vecteur d'intensité optimisé.
PCT/US2025/038084 2024-07-18 2025-07-17 Planification de radiothérapie comprimée Pending WO2026020021A1 (fr)

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US63/672,890 2024-07-18

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Citations (5)

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Publication number Priority date Publication date Assignee Title
US20150202464A1 (en) * 2014-01-23 2015-07-23 Mitsubis Multi-Criteria Optimization in Particle Beam Dose Optimization
US20170014642A1 (en) * 2015-07-13 2017-01-19 Yu An System and method for novel chance-constrained optimization in intensity-modulated proton therapy planning to account for range and patient setup uncertainties
US20190001152A1 (en) * 2016-03-09 2019-01-03 Reflexion Medical, Inc. Fluence map generation methods for radiotherapy
US20200289848A1 (en) * 2016-04-18 2020-09-17 Koninklijke Philips N.V. Fractionation selection tool in radiotherapy planning
US20230191150A1 (en) * 2021-12-20 2023-06-22 Siemens Healthineers International Ag Method and apparatus for fast influence matrix generation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150202464A1 (en) * 2014-01-23 2015-07-23 Mitsubis Multi-Criteria Optimization in Particle Beam Dose Optimization
US20170014642A1 (en) * 2015-07-13 2017-01-19 Yu An System and method for novel chance-constrained optimization in intensity-modulated proton therapy planning to account for range and patient setup uncertainties
US20190001152A1 (en) * 2016-03-09 2019-01-03 Reflexion Medical, Inc. Fluence map generation methods for radiotherapy
US20200289848A1 (en) * 2016-04-18 2020-09-17 Koninklijke Philips N.V. Fractionation selection tool in radiotherapy planning
US20230191150A1 (en) * 2021-12-20 2023-06-22 Siemens Healthineers International Ag Method and apparatus for fast influence matrix generation

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