Method and apparatus for fast influence matrix generation

Abstract

Various embodiments of the present disclosure relate to methods and apparatus for fast impact matrix generation. These teachings are for fast and accurate formation of an impact matrix by generating the impact matrix via integration with Monte Carlo particle transport simulations. The resulting impact matrix can then be utilized in the ordinary way when optimizing a radiation therapy plan. By one approach, the foregoing includes generating an impact matrix via integration with Monte Carlo particle transport simulations on a particle-by-particle basis. For example, for each particle, these teachings can provide identifying the point to which the particle belongs, and then adding a dose deposited by the particle during transport to an impact matrix element corresponding to the point to which the particle belongs and the voxel in which the dose was deposited.

Description

Technical Field

[0001] These teachings generally involve the planned target volume of treating patients with energy according to an energy-based treatment plan, and more specifically involve generating an influence matrix. Background Technology

[0002] The use of energy to treat medical conditions encompasses the known field of existing technology. For example, radiation therapy is a crucial component of many treatment programs used to reduce or eliminate unwanted tumors. Unfortunately, the applied energy cannot inherently distinguish between unwanted substances and adjacent tissues, organs, etc., which are necessary or even essential for the patient's continued survival. Consequently, energy, such as radiation, is often applied in a carefully managed manner, at least attempting to confine the energy to a given target volume. So-called energy-based treatment programs typically function in this regard.

[0003] Energy-based treatment plans, such as radiotherapy plans, typically involve specified values ​​for each of a variety of treatment platform parameters during each of multiple consecutive fields. Treatment plans for radiotherapy sessions are often generated through a process known as optimization. As used herein, "optimization" will be understood as improving candidate treatment plans without necessarily ensuring that the result of optimization is actually a single optimal solution. Such optimization typically involves automatically adjusting one or more treatment parameters (often simultaneously observing one or more corresponding constraints on these aspects) and mathematically calculating possible corresponding treatment outcomes to identify a given set of treatment parameters that represents a good trade-off between desired therapeutic outcomes and avoiding undesirable side effects.

[0004] Influence matrices are useful in at least some optimization techniques, particularly for proton therapy optimization. In proton therapy, protons are typically divided into points, i.e., beams with narrow positions, orientations, and energy distributions. The corresponding transport volume, on the other hand, is discretized into a three-dimensional linear voxel grid. The influence matrix maps the contribution of each point to a dose at each voxel with unit weights. The dose at each voxel can be obtained simply by multiplying the influence matrix by the point weight vector. This ease of use finds readily available applications and uses when optimizing corresponding radiotherapy plans.

[0005] Calculating doses in this way is much faster than many other techniques, but unfortunately, generating the influence matrix in the first case is very computationally intensive and time-consuming. This is because one typically needs to consider billions of contributions ranging from thousands of points to millions of voxels. Attached Figure Description

[0006] The above-mentioned needs are at least partially met by providing the method and apparatus for rapidly generating influence matrices as described in the following detailed description, especially when studied in conjunction with the accompanying drawings, in which:

[0007] Figure 1 Including block diagrams configured according to various embodiments of these teachings;

[0008] Figure 2 Including flowcharts of various embodiments configured according to these teachings; and

[0009] Figure 3 This includes schematic diagrams of various embodiments configured according to these teachings.

[0010] For simplicity and clarity, the elements in the figures are illustrated and are not necessarily drawn to scale. For example, the size and / or relative position of some elements in the figures may be exaggerated relative to other elements to aid in understanding the various embodiments of this teaching. Furthermore, common but well-known elements that are useful or necessary in commercially viable embodiments are generally not depicted to facilitate a less obstructed view of these different embodiments of this teaching. Certain actions and / or steps may be described or depicted in a particular order of occurrence, and those skilled in the art will understand that such specificity regarding the order is not actually necessary. The terms and expressions used herein have the ordinary technical meaning consistent with those given by those skilled in the art, unless otherwise specified herein. Unless specifically indicated, the word “or” as used herein should be interpreted as having a disjunctive structure rather than a conjunctive structure. Detailed Implementation

[0011] Generally speaking, these different implementations help optimize patient treatment plans to deliver therapeutic energy, such as proton beams, to specific patients.

[0012] One method uses these teachings to generate an influence matrix quickly and accurately by integrating it with Monte Carlo particle transport simulations. The resulting influence matrix can then be used in a conventional manner when optimizing radiotherapy plans.

[0013] One method, as described above, involves generating an influence matrix on a particle-by-particle basis via integration with a Monte Carlo particle transport simulation. For example, for each particle, these teachings can provide an identifier of the point to which the particle belongs, and then the dose deposited by that particle during transport is added to an element of the influence matrix, which corresponds to the point to which the particle belongs and the voxel where the dose was deposited.

[0014] Generally, generating an influence matrix through integration with Monte Carlo particle transport simulations may involve calculating both the influence matrix and the applied dose. This may include multiplying the deposited energy value by a pre-calculated inverse of the corresponding point weight, and then adding the corresponding dose contribution to the influence matrix element associated with the corresponding point in the voxel. These teachings may also include multiplying each non-zero influence matrix element by a pre-calculated inverse of the corresponding voxel mass when calculating the influence matrix and the applied dose, and after transport.

[0015] These teachings can dramatically reduce the amount of time and / or computational power typically associated with generating influence matrices, often by orders of magnitude, compared to the amount of time typically associated with at least some radiotherapy planning optimization procedures. Furthermore, by significantly reducing the time / resource requirements for generating influence matrices, these teachings make it feasible to use influence matrices in other environments that were previously impractical.

[0016] These and other benefits will become clearer after a comprehensive review and study of the following detailed explanation. Refer now to the accompanying drawings, especially... Figure 1 First, an illustrative device 100 compatible with many of these teachings will be presented.

[0017] In this particular example, the enabling device 100 includes a control circuit 101. As a “circuit”, the control circuit 101 therefore includes a structure comprising at least one (and typically multiple) conductive paths (such as paths made of conductive metals such as copper or silver) that transmit electricity in an orderly manner, the path(s) typically also including corresponding electrical components (both passive (such as resistors and capacitors) and active (such as any of various semiconductor-based devices, as appropriate)) to allow the circuit to implement the control aspects of these teachings.

[0018] Such control circuitry 101 may include a fixed-purpose hardwired hardware platform (including, but not limited to, application-specific integrated circuits (ASICs) (which are integrated circuits designed for a specific purpose rather than being customized for general purposes), field-programmable gate arrays (FPGAs), etc.), or may include a partially or fully programmable hardware platform (including, but not limited to, microcontrollers, microprocessors, etc.). These architectural options for such structures are well known and understood in the art and need not be described further herein. Control circuitry 101 is configured (e.g., by using corresponding programming that will be well understood by those skilled in the art) to perform one or more steps, actions, and / or functions described herein.

[0019] Control circuitry 101 is operatively coupled to memory 102. Memory 102 may be integrated into control circuitry 101 or physically separated from control circuitry 101 (wholly or partially) as needed. Memory 102 may also be local to control circuitry 101 (where, for example, both share a common circuit board, chassis, power supply, and / or housing), or may be remote to control circuitry 101 (where, for example, memory 102 is physically located in another facility, metropolitan area, or even country compared to control circuitry 101).

[0020] In addition to information such as radiation dose information, the memory 102 can also be used, for example, to non-transitory store computer instructions that, when executed by the control circuit 101, cause the control circuit 101 to operate as described herein. (As used herein, the reference to "non-transitory" will be understood to mean the non-transitory state of the stored content (and thus excludes the case where the stored content constitutes only a signal or wave), rather than the volatile nature of the storage medium itself, and therefore includes both non-volatile memory (e.g., read-only memory (ROM)) and volatile memory (e.g., dynamic random access memory (DRAM)).

[0021] Alternatively, the control circuitry 101 may also be operatively coupled to the user interface 103. The user interface 103 may include any of a variety of user input mechanisms (such as, but not limited to, keyboards and keypads, cursor control devices, touch-sensitive displays, voice recognition interfaces, gesture recognition interfaces, etc.) and / or user output mechanisms (such as, but not limited to, visual displays, audio converters, printers, etc.) to facilitate receiving information and / or instructions from and / or providing information to the user.

[0022] If necessary, the control circuitry 101 can also be operatively coupled to a network interface (not shown). With this configuration, the control circuitry 101 can communicate with other elements (both internal and external to the device 100) via the network interface. Network interfaces, including wireless and non-wireless platforms, are well known in the art and do not require particular description herein.

[0023] By means of a method, computed tomography apparatus 106 and / or other imaging apparatus 107 known in the art can acquire part or all of any desired patient-related imaging information.

[0024] In this illustrative example, control circuitry 101 is configured to ultimately output an optimized energy-based treatment plan 113 (e.g., an optimized radiotherapy plan). This energy-based treatment plan 113 typically comprises specified values ​​for each of various treatment platform parameters during each of multiple consecutive exposure fields. In this case, the energy-based treatment plan 113 is generated through an optimization process. Various automated optimization processes are known in the art, and these processes are specifically configured to generate such energy-based treatment plans. Since this teaching is not overly sensitive to any particular choice of these aspects, further elaboration of these aspects is not provided herein except where particularly relevant to the details of this specification.

[0025] In one method, control circuitry 101 can be operatively coupled to an energy-based therapy platform 114 configured to deliver therapeutic energy 112 to a corresponding patient 104 according to an optimized energy-based therapy plan 113. These teachings are generally applicable to any of a variety of energy-based therapy platforms / devices.

[0026] In a typical application setup, the energy-based treatment platform 114 would include an energy source 115, such as an ionizing radiation source, a microwave energy source, a thermal energy source, etc. For illustrative purposes, it will be assumed here that the energy source 115 is a proton source that provides a proton beam to irradiate the diseased tissue.

[0027] In one method, the energy source 115 can be selectively moved via a bench along an arcuate path (where the path at least partially encompasses the patient during treatment application). The arcuate path may, as needed, comprise a complete or near-complete circle. In another method, control circuitry 101 controls the movement of the energy source 115 along the arcuate path and can accordingly control when the energy source 115 begins to move, stops moving, accelerates, decelerates, and / or the speed at which the energy source 115 travels along the arcuate path.

[0028] A typical energy-based treatment platform 114 may also include one or more support devices 110 (such as a treatment bed) for supporting the patient 104 during treatment, one or more patient fixation devices 111, a bench or other movable mechanism that allows selective movement of the energy source 115, and one or more energy shaping devices 117 (e.g., bundle shaping devices, such as jaws, multi-leaf collimators, etc.) for providing selective energy shaping and / or energy conditioning as needed.

[0029] In a typical application setup, this document assumes that the patient support device 110 is selectively controllable via control circuitry 101 to move in any direction (i.e., any X, Y, or Z direction) during energy-based therapy. Since the foregoing elements and systems are well known in the art, detailed descriptions of these aspects are not provided herein except where relevant to the description.

[0030] Now for reference Figure 2 The process 200, which can be implemented in conjunction with the above application settings (and more specifically via the above control circuit 101), will be described.

[0031] In box 201, the process 200 is used to generate an influence matrix via integration with a Monte Carlo particle transport simulation.

[0032] A brief overview of some details regarding Monte Carlo particle transport simulations may be helpful to some readers. The dose deposited in a medium by ionizing particle radiation (such as photons, electrons, protons, or neutrons) can be solved using Monte Carlo particle transport simulations. This is particularly useful in radiotherapy treatment planning for simulating dose distribution in patient tissues. In Monte Carlo transport, a large number of random particles are sampled, and the initial positions and initial velocity distributions of these particles are matched to the simulated radiation source. This batch of primary particles then propagates through the medium in a progressive manner, where they undergo interactions with the medium that deflect and decelerate them. As the particles decelerate, they deposit energy, i.e., the dose, into the medium. Primary particles can also further deflect secondary particles to be tracked. Compared to, for example, fluence-based dose calculation methods, Monte Carlo transport provides a more accurate estimate of the deposited dose.

[0033] These teachings accelerate influence matrix calculation by integrating it into a low-level Monte Carlo dose calculation algorithm. This may involve, for each particle, identifying the point to which the particle belongs, and, when the particle is transported, adding the dose deposited by the particle during transport to the corresponding influence matrix element of the point to which the particle belongs and the voxel where the dose is deposited. This approach significantly eliminates the time / resource overhead often anticipated when calculating the influence matrix.

[0034] In other words, these teachings provide a record of the point to which each particle belongs, and when particle p is transported and its energy is deposited into the medium, this contribution is added to the influence matrix element Msv, which corresponds to the point s to which the current particle belongs and the voxel v in which the particle resides. This type of contribution is given by the following equation.

[0035]

[0036] Where ΔE p It is the energy of particle deposition, ws It is the weight of the point to which the particle belongs, m v It is determined by its volume V v and local density d v (m v =d v V v The given voxel mass.

[0037] Figure 3 The above situation is illustrated. The depicted grid 301 corresponds to a two-dimensional patient containing 4×4 voxels (two of which are indicated by reference numeral 302). The wedge-shaped element 303 is a proton beam or "point," and the dashed line 304 is the trajectory of a single proton within the patient's body. The star-shaped element (one of which is indicated by reference numeral 305) indicates a collision between a proton and a particle in the medium, where the proton loses and deposits some energy (where the "dose" is equal to the energy divided by the local density).

[0038] Reference numeral 306 denotes the corresponding influence matrix. In this illustrative example, each column of the influence matrix corresponds to a point, and each row corresponds to a voxel. Reference numeral 307 corresponds to the specific voxel currently under consideration. The energy ΔE of the particles in the trajectory indicated by reference numeral 304 deposited onto this specific voxel 307 is added to the influence matrix shown on the right, as illustrated.

[0039] Calculating the influence matrix and dosage may involve the following changes:

[0040] First, during the transmission, the deposited energy ΔE can be... p Multiply by the pre-calculated reciprocal of the point weights, 1 / w s (When using unit point weight (w) s =1) This cost can be avoided when calculating the dose. ) This contribution is added to the influence matrix element M corresponding to point s and voxel v. sv (If necessary, and if the current primary particle initiates a secondary particle during transmission, the point to which the former belongs may also be assigned to the latter.)

[0041] After transmission, each non-zero influence matrix element M sv It can be multiplied by the reciprocal of the pre-calculated body mass 1 / m v (It should be understood that performing this step after transmission, rather than earlier, means that each non-zero element only needs to be multiplied by 1 / m.) v Once (compared to each deposition of any one of the many particles belonging to point s to voxel v).

[0042] Many (and often most) influence matrix elements are zero or negligible. This is because many voxels correspond to air, and many voxels are not near a specific point. In some cases, using a compressed data structure for the influence matrix that does not waste memory for a large number of unnecessary elements allows for the use of slower, cheaper storage devices. One candidate data structure is a sparse matrix.

[0043] These teachings will support the use of distributed therapy if needed. For example, Monte Carlo transfers, influence matrix calculations, and subsequent optimizations or other steps (one or more) can be distributed among multiple remote servers. For instance, each point of the target and its corresponding narrow slice can be processed by different devices. In this case, each server can update the influence matrix elements based on the points it has allocated.

[0044] Those skilled in the art will recognize that various modifications, alterations, and combinations can be made to the above embodiments without departing from the scope of the invention. Therefore, such modifications, alterations, and combinations are considered to be within the scope of the concept of the invention.

Claims

1. A method for rapidly influencing matrix generation, comprising: Control circuit: An influence matrix is ​​generated on a particle-by-particle basis through integration with Monte Carlo particle transport simulations; The generation of the influence matrix via integration with Monte Carlo particle transport simulations includes, for each particle: Identify the point to which the particle belongs; The dose deposited by the particle during transmission is added to an influence matrix element, which corresponds to the point to which the particle belongs and the voxel where the dose was deposited.

2. The method according to claim 1, further comprising: The radiotherapy plan is optimized based on the influence matrix.

3. The method of claim 2, wherein the radiotherapy plan includes a radiotherapy plan for proton therapy.

4. A method for rapidly influencing matrix generation, comprising: Control circuit: An influence matrix is ​​generated through integration with Monte Carlo particle transport simulations; Generating the influence matrix via integration with Monte Carlo particle transport simulations includes: calculating the influence matrix and calculating the administered dose; wherein calculating the influence matrix and calculating the administered dose includes: Multiply the deposition energy value by the reciprocal of the pre-calculated weight of the corresponding point.

5. The method of claim 4, wherein calculating the influence matrix and calculating the application dose further comprises: The corresponding dose contribution is added to the influence matrix elements associated with the corresponding point and voxel.

6. The method of claim 5, wherein calculating the influence matrix and calculating the applied dose further comprises, when the current particle interacts with the secondary particle during transmission, also assigning a point corresponding to the current particle to the secondary particle.

7. The method of claim 4, wherein calculating the influence matrix and calculating the applied dose further comprises, after transmission, multiplying each non-zero influence matrix element by the reciprocal of the pre-calculated corresponding body mass.

8. An apparatus for rapidly influencing matrix generation, comprising: The control circuit is configured as follows: An influence matrix is ​​generated on a particle-by-particle basis through integration with Monte Carlo particle transport simulations; The generation of the influence matrix via integration with Monte Carlo particle transport simulations includes, for each particle: Identify the point to which the particle belongs; The dose deposited by the particle during transmission is added to an influence matrix element, which corresponds to the point to which the particle belongs and the voxel where the dose was deposited.

9. The apparatus of claim 8, wherein the control circuit is further configured to: The radiotherapy plan is optimized based on the influence matrix.

10. The apparatus of claim 9, wherein the radiotherapy plan includes a radiotherapy plan for proton therapy.

11. An apparatus for rapidly influencing matrix generation, comprising: The control circuit is configured as follows: An influence matrix is ​​generated through integration with Monte Carlo particle transport simulations; The control circuitry is configured to generate the influence matrix via integration with Monte Carlo particle transport simulations in the following manner: Calculate the influence matrix and the dosage; and The control circuit is configured to calculate the influence matrix and the applied dose by multiplying the deposited energy value by the reciprocal of the pre-calculated corresponding point weight.

12. The apparatus of claim 11, wherein the control circuitry is further configured to calculate the influence matrix and the applied dose by adding the corresponding dose contribution to the influence matrix elements associated with the corresponding point and voxel.

13. The apparatus of claim 12, wherein the control circuit is further configured to calculate the influence matrix and calculate the applied dose by assigning a point corresponding to the current particle to the secondary particle when the current particle interacts with the secondary particle during transmission.

14. The apparatus of claim 11, wherein the control circuit is further configured to calculate the influence matrix and the application dose by multiplying each non-zero influence matrix element by the reciprocal of a pre-calculated corresponding body mass after transmission.

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