Three-dimensional radiotherapy dose distribution prediction method for proton heavy ion radiotherapy equipment
By establishing reference and standard simulation environments, utilizing gradient weights and variable density sampling, and combining intensity scaling factors and range deviations, the problem of accurately reconstructing the three-dimensional dose distribution of proton and heavy ion radiotherapy equipment was solved, achieving high-precision dose verification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU INST OF METROLOGY
- Filing Date
- 2026-03-24
- Publication Date
- 2026-07-03
AI Technical Summary
Existing technologies cannot accurately reproduce the three-dimensional dose distribution of proton and heavy ion radiotherapy equipment in pure water. Nonlinear measurement distortion introduced by detector materials and information loss caused by sparse sampling seriously affect the accuracy and efficiency of dose verification.
A reference simulation environment containing the response of a two-dimensional matrix detector and a standard simulation environment in pure water medium are established. The observation depth points are obtained by calculating gradient weights and variable density sampling. Spatial translation transformation and global dose correction are performed using intensity scaling factor and range deviation to generate a predicted three-dimensional dose distribution map that eliminates detector interference.
While maintaining measurement efficiency, it accurately and with high resolution reconstructs the true three-dimensional dose distribution of the beam in an ideal medium, significantly improving the accuracy and efficiency of dose verification for radiotherapy equipment.
Smart Images
Figure CN122321358A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of radiotherapy technology, specifically to a three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment. Background Technology
[0002] Proton and heavy ion radiotherapy utilizes the unique physical properties of the Bragg peak to deposit high doses into the tumor target area while maximally protecting surrounding normal tissues. The precision of this dose distribution depends on the stability of the beam energy and output intensity. However, in actual clinical applications, accelerator equipment may experience uncertainties such as beam parameter drift and energy fluctuations, leading to deviations in the actual radiation dose distribution. To ensure treatment accuracy, regular dose validation of the radiotherapy equipment is necessary.
[0003] In dose verification, existing technologies generally employ a two-dimensional matrix detector in conjunction with a water tank for measurement. The detector is composed of multiple layers of non-aqueous equivalent materials. When a low-energy particle beam passes through these materials, its actual range and energy loss characteristics differ significantly from those in pure water. This difference exhibits a nonlinear change as the residual energy of the particles decreases, resulting in non-uniform broadening and peak position shift in the measured Bragg peak waveform compared to the true dose distribution. Furthermore, to improve detection efficiency, a sparse sampling strategy is typically employed. However, under sparse sampling conditions, it is easy to miss the Bragg peak or the maximum gradient point, causing the loss of crucial range information and making it impossible to fully capture the key characteristics of the Bragg peak. Therefore, due to the combined effects of nonlinear measurement distortion introduced by the detector material and information loss caused by sparse sampling, existing technologies struggle to accurately reconstruct the true three-dimensional dose distribution of the beam in pure water, severely limiting the accuracy and efficiency of dose verification for radiotherapy equipment. Summary of the Invention
[0004] To address the aforementioned technical issues, a three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment is provided to resolve existing problems.
[0005] The solution to the technical problem addressed in this application is to provide a three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment, comprising the following steps: A reference simulation environment containing the response of a two-dimensional matrix detector and a standard simulation environment containing a pure water medium were established. Reference dose curves containing the detector response and standard three-dimensional dose distribution maps under ideal media were generated respectively. Based on the variation characteristics of dose values with depth on the reference dose curve, the gradient weight of each depth point is calculated. Based on this, the entire depth range covered by the curve is divided into different sampling intervals, and a variable density sampling strategy is used for differentiated sampling to obtain the observation depth points. The measured dose values collected by the detector at each observation depth point are obtained; based on the gradient weight and the degree of deviation of the measured dose value from the reference dose curve, the sparse observation depth points are matched and mapped to the dense reference dose curve to minimize the cumulative deviation and determine the matching depth point on the curve corresponding to each observation depth point. Based on the measured dose value and the dose value at the matching depth point on the curve, the intensity scaling factor characterizing the beam output intensity is calculated; the range deviation characterizing the beam penetration depth is calculated by observing the depth difference between the depth point and the matching depth point. By utilizing range deviation and intensity scaling factor, a spatial translation transformation and global dose correction are performed on the standard three-dimensional dose distribution map to generate a predicted three-dimensional dose distribution map that eliminates detector interference.
[0006] Preferably, the gradient weight is calculated as follows: the derivative of the reference dose curve at each depth point is calculated, and its absolute value is normalized to serve as the gradient weight at each depth point.
[0007] Preferably, the process of dividing the sampling interval is as follows: for the reference dose curve, the continuous depth range where the gradient weight is less than a preset threshold is defined as the low sampling interval; the continuous depth range where the gradient weight is greater than or equal to the preset threshold is defined as the high sampling interval.
[0008] Preferably, obtaining the observation depth point includes: Within the low sampling interval, sampling is performed with a preset first sampling step size, and within the high sampling interval, sampling is performed with a preset second sampling step size to obtain discrete points sampled in the two intervals; wherein, the preset first sampling step size is greater than the preset second sampling step size. The depth corresponding to the peak value on the reference dose curve is taken as the peak point, and the depth corresponding to the preset percentage of the reference dose curve that drops to the peak value is taken as the drop point. All discrete points, peak points, and drop points sampled are defined as observation depth points.
[0009] Preferably, determining the matching depth point corresponding to each observation depth point on the curve includes: For each observation depth point, a preset local depth range is constructed with its depth as the center; all depth points contained in this local depth range are defined as candidate depth points. Based on the gradient weights of each observation depth point and the difference between the measured dose value and the dose value at the candidate depth point on the curve, the local cost between each observation depth point and its corresponding candidate depth point is calculated. Construct a cost matrix A, where rows represent observed depth points and columns represent candidate depth points; for any element in the cost matrix... If candidate depth points Located at the observation depth point Within the local depth range, its value is the local cost; otherwise, its value is assigned to infinity. Search for the path with the minimum cumulative cost in the cost matrix, and define the candidate depth points corresponding to each observed depth point on the path as the matching depth points.
[0010] Preferably, the calculation process of the local cost is as follows: for each candidate depth point corresponding to each observation depth point, the difference between the measured dose value of each observation depth point and the dose value of the reference dose curve at the candidate depth point is calculated as the dose deviation; the product of the gradient weight corresponding to each observation depth point and the dose deviation is used as the local cost between each observation depth point and its corresponding candidate depth point.
[0011] Preferably, the intensity scaling factor is the ratio between the sum of the measured dose values at all observed depth points and the sum of the dose values at all matching depth points on the reference dose curve.
[0012] Preferably, the calculation process of the range deviation is as follows: select observation depth points whose depth is located within the high sampling interval, calculate the depth difference between them and the corresponding matching depth points, and take it as the depth difference; take the median of all depth differences as the range deviation.
[0013] Preferably, the specific process of the spatial translation transformation is as follows: using the range deviation, the spatial coordinates of all voxels in the standard three-dimensional dose distribution map are translated along the Z-axis.
[0014] Preferably, the specific process of global dose correction is as follows: calculate the product of the dose value of each voxel in the standard three-dimensional dose distribution map after translation transformation and the intensity scaling factor, and generate a predicted three-dimensional dose distribution map.
[0015] This application has at least the following beneficial effects: This application establishes two simulation environments to simulate both an environment containing detector response characteristics and an ideal medium environment without detector interference. This fundamentally decouples the inherent distortion of the detector from the ideal dose distribution in pure water, laying a reliable data foundation for subsequent deformation mapping and lossless reconstruction. The gradient weights at each depth point are calculated, and the entire depth range covered by the curve is divided into different sampling intervals. A variable-density sampling strategy is used for differentiated sampling to obtain the observation depth points. The beneficial effect is that the gradient weights quantify the range information carried at each depth, enabling sparse sampling in regions with gradual dose changes (i.e., low gradient weights) to significantly shorten subsequent detection time. In the Bragg peak region, dense sampling is performed on high gradient weights to ensure the capture of more key feature information, fundamentally eliminating the engineering risk of missing peaks due to sparse sampling. Sparse observation depth points are matched and mapped onto a dense reference dose curve to determine the corresponding matching depth point on the curve. The beneficial effect of introducing gradient weights is that it not only forces the algorithm to achieve precise alignment in steep peak regions but also effectively suppresses random drift of the matching path caused by minimal numerical differences in flat regions, ensuring the spatial topological correctness of the mapping from the actual measured observation depth points to the continuous reference dose. Finally, the intensity scaling factor characterizing the beam output intensity and the range characterizing the beam penetration depth are calculated. The beneficial effect of this deviation lies in decoupling complex waveform morphology differences into two parameters with output intensity deviation and physical penetration depth deviation, thereby effectively suppressing the influence of single-point measurement noise and providing a stable and reliable scaling reference for subsequent global intensity correction of the three-dimensional dose distribution. Simultaneously, it can accurately separate the true beam range deviation from the interfered signal, providing a precise offset for subsequent spatial translation correction of the three-dimensional dose distribution. Using the range deviation and intensity scaling factor, spatial translation transformation and global dose correction are performed on the standard three-dimensional dose distribution map to generate a predicted three-dimensional dose distribution map that eliminates detector interference. Its beneficial effect lies in utilizing the two decoupled physical parameters... The method directly drives the ideal water tank model, accurately corrects the range deviation, restores the Bragg peak to the correct position, and uses high-resolution prior information from Monte Carlo simulation to fill the measurement blind zone between sparse sampling points. This allows the output to maintain a complete peak structure under limited sampling conditions, while eliminating nonlinear measurement distortion introduced by the detector material in the final output. Thus, when using the detector for limited point sampling, it accurately decouples and eliminates nonlinear measurement distortion introduced by the detector material itself. While taking into account measurement efficiency, it reconstructs the true three-dimensional dose distribution of the beam in the ideal medium with high precision and high resolution, significantly improving the accuracy and efficiency of dose verification for radiotherapy equipment. Attached Figure Description
[0016] The following section provides a more detailed description of the three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment based on this application, with reference to the accompanying drawings.
[0017] Figure 1 A flowchart illustrating the steps of a three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment provided in this application embodiment; Figure 2 A flowchart illustrating the steps of the method for obtaining observation depth points provided in this application embodiment. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of the three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment, in conjunction with the accompanying drawings and implementation examples, is provided. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0020] Please see Figure 1 The diagram illustrates a flowchart of a three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment according to an embodiment of this application. The method includes the following steps: Step 1: Establish a reference simulation environment containing the response of a two-dimensional matrix detector and a standard simulation environment containing a pure water medium. Generate a reference dose curve containing the detector response and a standard three-dimensional dose distribution map under an ideal medium. Based on the characteristics of dose value variation with depth on the reference dose curve, calculate the gradient weight of each depth point. Based on this, divide the entire depth range contained in the curve into different sampling intervals and use a variable density sampling strategy for differentiated sampling to obtain the observation depth points.
[0021] Radiation therapy is a treatment method that uses various types of radiation, such as X-rays, gamma rays, proton beams, and heavy ion beams, to directly irradiate cancerous tumors, inhibiting and damaging cancer cell growth, causing tumor regression, shrinkage, and even death. Due to the Bragg peak effect of heavy ions—where energy deposition at the end of the beam's range as the particle penetrates a medium, forming a sharp peak before rapidly dropping to zero—radiation therapy can precisely concentrate high doses within the tumor target area while maximally protecting healthy tissue behind the tumor. However, this extremely steep dose gradient also makes the treatment effect highly sensitive to the precision of the particle's range; even small deviations can lead to insufficient dose to the tumor target area or excessive irradiation of surrounding normal tissue. Therefore, regular, high-precision dose verification of radiotherapy equipment is necessary to ensure that the beam energy and depth dose distribution are completely consistent with the treatment plan.
[0022] Based on the above analysis, Monte Carlo particle transport simulation technology was used to construct two independent simulation environments: a reference simulation environment and a standard simulation environment, as follows: Reference simulation environment: Based on the actual physical structure parameters of the two-dimensional matrix detector, a digital model including the detector response is established, and the hadron physics process, electromagnetic interaction and multiple scattering model are enabled to simulate the transport process of particles in the multi-layer structure of the detector, thereby reflecting the energy loss and scattering of particles in the non-uniform medium. Along the beam central axis, the energy deposition in the sensitive volume of the two-dimensional matrix detector, i.e. the dose value, is recorded to generate a reference dose curve containing the detector response. Standard simulation environment: Under the same simulation framework, remove all detector models in the software and set only a uniformly distributed pure water phantom, that is, build a water tank model with a size covering the maximum range to simulate the transport process of particles in pure water medium. Divide the water tank model into very small cubes, and each cube represents a volume pixel, i.e., a voxel. Record the energy deposition of each voxel in the water tank model to generate a standard three-dimensional dose distribution map of pure water medium. In this embodiment, the Monte Carlo particle transport simulation technology uses GEANT4 as the Monte Carlo simulation software. The simulation of Monte Carlo software is a well-known technology and will not be described in detail here. Secondly, when establishing the digital model of the detector, the layered structure of the detector is accurately reproduced, including the high-density printed circuit board layer, the air gap layer and the outer protective layer.
[0023] It should be noted that the horizontal axis of the reference dose curve represents depth, indicating the position of the detector in the beam direction, and the vertical axis represents the dose value at that depth point, indicating the strength of the detector reading at that depth. This curve reflects the theoretical reading that the detector should measure under ideal conditions, and includes the nonlinear distortion characteristics introduced by the detector structure. The standard three-dimensional dose distribution map represents the actual dose pattern of the beam in pure water, including the dose values at each point in the space where the pure water phantom is located.
[0024] The dose values on the reference dose curve and the standard three-dimensional dose distribution map were normalized, specifically as follows: A fixed depth position is selected in the flat region of beam injection as the reference depth, and the dose value at that reference depth on the reference dose curve is used as the first reference value. The ratio of the dose value at each depth point on the reference dose curve to the first reference value is used as the normalized dose value. It should be noted that the beam inrush flat region refers to a region where the dose change is very gradual immediately after the particle beam enters the medium.
[0025] For a standard three-dimensional dose distribution map, the dose value at the reference depth on its central axis along the beam direction is extracted and used as a second reference value. The ratio of the dose value of each voxel in the standard three-dimensional dose distribution map to the second reference value is used as the normalized dose value.
[0026] This leads to the normalized reference dose curve and standard three-dimensional dose distribution map.
[0027] The flowchart of the method for obtaining observation depth points provided in this application embodiment is as follows: Figure 2 As shown.
[0028] Secondly, in order to quantify the range information carried at different depths and guide subsequent variable-density sampling, gradient weights are calculated using a reference dose curve, specifically: Calculate the derivative of the reference dose curve at each depth point, normalize its absolute value, and use it as the gradient weight at each depth point. In this embodiment, the maximum value normalization method is used for normalization. That is, for the absolute value of the derivative at all depth points on the reference dose curve, the maximum absolute value is selected, and the ratio of the absolute value of the derivative at each depth point to the maximum absolute value is used as the gradient weight. The calculation of the derivative is a well-known technique and will not be described in detail here.
[0029] It should be noted that the larger the absolute value of the derivative, the more drastic the dose change with depth, and the closer it is to the Bragg peak region, which is more sensitive to range deviation. Secondly, during the normalization process, to avoid a denominator of 0 (i.e., a horizontal straight line for the reference dose curve), a parameter adjustment factor is added to the denominator. The parameter adjustment factor is set to a value of... As another implementation method, the implementer can set it according to the actual situation.
[0030] For the reference dose curve, the continuous depth range where the gradient weight is less than a preset threshold is defined as the low sampling interval; the continuous depth range where the gradient weight is greater than or equal to the preset threshold is defined as the high sampling interval. In this embodiment, the preset threshold value is 0.05. In other implementation methods, the implementer can set it according to the actual situation.
[0031] It should be noted that the depth contained in the low sampling interval is in a region where the dose change is gradual, and contains less range information, mainly affected by noise; the depth contained in the high sampling interval is in the Bragg peak region, which is highly sensitive to range shift and contains core range information.
[0032] Secondly, in order to capture the morphological features of the Bragg peak to the greatest extent within a limited number of measurement points, a variable density sampling strategy is adopted, using a larger sampling step size in the low sampling interval and a smaller sampling step size in the high sampling interval, in order to capture more range information.
[0033] The variable density sampling strategy is as follows: Within the low sampling interval, sampling is performed with a preset first sampling step size, and within the high sampling interval, sampling is performed with a preset second sampling step size to obtain discrete points sampled in the two intervals; wherein, the preset first sampling step size is greater than the preset second sampling step size. The depth corresponding to the peak value on the reference dose curve is taken as the peak point, and the depth corresponding to the preset percentage of the reference dose curve that drops to the peak value is taken as the drop point. All discrete points, peak points, and drop points sampled are defined as observation depth points; In this embodiment, the preset first sampling step size is 20mm and the preset second sampling step size is 2mm. As other implementation methods, the implementer can set them according to the actual situation. Secondly, the drop point is the depth corresponding to 80% of the peak value on the reference dose curve.
[0034] At this point, all observation depth points have been obtained.
[0035] Step 2: Obtain the measured dose values collected by the detector at each observation depth point; based on the gradient weight and the degree to which the measured dose values deviate from the reference dose curve, match and map the sparse observation depth points onto the dense reference dose curve to minimize the cumulative deviation, and determine the matching depth point corresponding to each observation depth point on the curve; based on the measured dose values and the dose values at the matching depth points on the curve, calculate the intensity scaling factor characterizing the beam output intensity; through the depth difference between the observation depth points and the matching depth points, calculate the range deviation characterizing the beam penetration depth.
[0036] Furthermore, in the actual verification phase, the dose values measured at the observation depth points were specifically as follows: The two-dimensional matrix detector is controlled to move sequentially to the positions corresponding to each observation depth point, and the ionization chamber readings on the central axis are collected to obtain the measured dose values at all observation depth points; It should be noted that the ionization chamber reading reflects the measured total charge, representing the actual radiation dose absorbed at that observation depth.
[0037] The measured dose values were normalized, and the specific processing steps were as follows: All observation depth points are sorted in ascending order according to their corresponding depths. The measured dose value at the first observation depth point is selected as the benchmark value. The ratio of the measured dose value at each observation depth point to the benchmark value is calculated as the normalized measured dose value. It should be noted that the normalized measured dose value represents the relative strength of the dose at each observation depth point relative to the dose at the flat area at the entrance. Secondly, if the reference value is less than the preset noise value, it is determined that the radiotherapy equipment has not emitted a beam or the detector is faulty, the process is immediately terminated and an alarm is triggered; otherwise, the measured dose value is normalized for subsequent calculations. The preset noise value is 0.01, which can be set by the implementer according to the actual situation as an alternative implementation method.
[0038] Secondly, due to the nonlinear response of the detector material, the measured dose value will exhibit a non-uniform morphological change compared to the reference dose curve, such as peak broadening. To find the true physical correspondence of each observation depth point on the reference dose curve, the measured dose value at each observation depth point is matched with the reference dose curve, specifically as follows: For each observation depth point, a preset local depth range is constructed with its depth as the center; In this embodiment, the length of the local interval is 10mm, that is, if the depth of each observation depth point is z, then the range of the local interval is... ,in, The value is 5mm. As for other implementation methods, the implementer can set it according to the actual situation.
[0039] Define all depth points contained within this local depth range as candidate depth points; For each candidate depth point corresponding to each observation depth point, the difference between the measured dose value at each observation depth point and the dose value of the reference dose curve at that candidate depth point is calculated as the dose deviation; In this embodiment, the square of the difference between the measured dose value at each observation depth point and the dose value at each candidate depth point on the reference dose curve is calculated as the dose deviation. In other embodiments, the implementer may also use the absolute value of the difference between the measured dose value at each observation depth point and the dose value at each candidate depth point on the reference dose curve as the dose deviation.
[0040] The product of the gradient weight corresponding to each observation depth point and the dose bias is used as the local cost between each observation depth point and its corresponding candidate depth point. It should be noted that the smaller the dose deviation, the closer the dose patterns of the two points are; the gradient weight reflects the degree of dose change at that depth point. Therefore, in the region where the dose change is gradual, i.e. the gradient weight is small, the impact of dose deviation on the total cost is reduced, so that subsequent optimization can tolerate random measurement noise in the gradual region and prevent mismatch; while in the Bragg peak region, the impact of dose deviation is amplified, forcing the algorithm to prioritize finding the corresponding point with the most consistent peak shape features, thereby locking in the range information.
[0041] Construct a cost matrix A, where rows represent observed depth points and columns represent candidate depth points; It should be noted that in the cost matrix, the columns represent all candidate depth points corresponding to all observed depth points. For each row, the calculation is only performed within the candidate depth points within its corresponding local depth range. The remaining positions are assigned infinity to indicate that they are not a match.
[0042] For any element in the cost matrix If candidate depth points Located at the observation depth point Within the local depth range, its value is the local cost; otherwise, its value is assigned to infinity. A dynamic programming algorithm is used to search for a path with the minimum cumulative cost in the cost matrix, and the candidate depth points corresponding to each observation depth point on this path are defined as matching depth points. It should be noted that dynamic programming is a well-known technique and will not be elaborated upon here.
[0043] The ratio between the sum of the measured dose values at all observation depth points and the sum of the dose values at all matching depth points on the reference dose curve is calculated and used as the intensity scaling factor. It should be noted that when calculating the intensity scaling factor, the measured dose values at the observation depth points are unnormalized data, and the dose values at the matching depth points are also unnormalized data. Among them, the sum of the measured dose values at all observation depth points represents the total actual output charge, the sum of the dose values at all matching depth points represents the total dose under the reference state, and the intensity scaling factor reflects the multiple of the current beam output intensity relative to the reference state, which is used to perform overall intensity correction on the dose in the standard three-dimensional dose distribution map.
[0044] Secondly, based on the mapping relationship established between the observed depth point and the matched depth point, the range deviation of the beam's actual penetration depth in pure water medium relative to the nominal depth is calculated, specifically as follows: Filter observation depth points whose depth is within the high sampling interval, calculate the depth difference between them and the corresponding matching depth points, and take the depth difference as the depth difference; take the median of all depth differences as the range deviation. It should be noted that only observation depth points within the high sampling interval are counted, because only these points carry valid range information. The median is used because the median is robust to outliers. Therefore, this range deviation reflects the systematic shift of the actual penetration depth of the beam relative to the reference range. A positive range deviation indicates that the actual range is deeper than the nominal range; conversely, a negative range deviation indicates that the range has been reduced.
[0045] Thus, the intensity scaling factor and range deviation are obtained.
[0046] Step 3: Using the range deviation and intensity scaling factor, perform spatial translation transformation and global dose correction on the standard three-dimensional dose distribution map to generate a predicted three-dimensional dose distribution map that eliminates detector interference.
[0047] Furthermore, based on the decoupled beam state parameters, namely the intensity scaling factor and range deviation, the standard three-dimensional dose distribution map is spatially and intensift-corrected to directly output an ideal three-dimensional dose distribution map free from detector interference while filling in the sparse measurement blind zone. Specifically: Using range deviation, the spatial coordinates of all voxels in the standard three-dimensional dose distribution map are translated along the Z-axis. Specifically, let the standard three-dimensional dose distribution map be denoted as D, and iterate through the spatial coordinates of each voxel in the standard three-dimensional dose distribution map D. ,make ,in, Due to range deviation, Spatial coordinates in the standard three-dimensional dose distribution map after translation transformation The dose value at the location; Spatial coordinates in a standard three-dimensional dose distribution map The dose value at the location; It should be noted that, Only added to depth coordinates Up, if The Bragg peak in the standard three-dimensional dose distribution map will shift deeper; if If it shrinks to a shallower area, then it will retreat; secondly, if the spatial coordinates after translation... If the dose value is less than 0 or exceeds the depth index range of D, the dose value at that point is assigned to 0; additionally, under small energy deviations in normal scenarios, the lateral optical properties of the beam remain constant, therefore, no... The coordinate transformation avoids the logical pitfall that single-dimensional measurements cannot correct three-dimensional lateral deformation, ensuring the accuracy of the reconstruction results in the main error direction, namely the depth direction.
[0048] The product of the dose value of each voxel in the standard three-dimensional dose distribution map after translation transformation and the intensity scaling factor is used to generate a predicted three-dimensional dose distribution map that eliminates detector interference. It should be noted that the intensity scaling factor is used to adjust the overall amplitude of the dose values in the standard three-dimensional dose distribution map after coordinate transformation in order to correct the output intensity error. The intensity scaling factor is calculated based on the ratio of the measured total charge to the reference total dose. By multiplying the ratio, the relative intensity of the standard three-dimensional dose distribution map is adjusted to be consistent with the current measured beam. Thus, although the measured data is sparse, the predicted three-dimensional dose distribution map retains the fine spatial resolution of the Monte Carlo simulation, perfectly fills the gaps between the actual measured observation depth points, and completely reconstructs the details of the Bragg peak. At the same time, using a pure water phantom as a substrate, the nonlinear distortion introduced by the detector material is eliminated.
[0049] The predicted 3D dose distribution map is compared with the reference dose distribution map exported by the external treatment planning system using Gamma pass rate analysis. If the deviation exceeds the clinical tolerance, i.e., the 3% / 3mm standard, an alarm is generated to indicate accelerator hardware adjustments.
[0050] It should be noted that Gamma pass rate analysis is a well-known technique and will not be elaborated upon here.
[0051] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0052] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0053] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.
Claims
1. A method for predicting three-dimensional radiotherapy dose distribution for proton and heavy ion radiotherapy equipment, characterized in that, The method includes the following steps: A reference simulation environment containing the response of a two-dimensional matrix detector and a standard simulation environment containing a pure water medium were established. Reference dose curves containing the detector response and standard three-dimensional dose distribution maps under ideal media were generated respectively. Based on the variation characteristics of dose values with depth on the reference dose curve, the gradient weight of each depth point is calculated. Based on this, the entire depth range covered by the curve is divided into different sampling intervals, and a variable density sampling strategy is used for differentiated sampling to obtain the observation depth points. The measured dose values collected by the detector at each observation depth point are obtained; based on the gradient weight and the degree of deviation of the measured dose value from the reference dose curve, the sparse observation depth points are matched and mapped to the dense reference dose curve to minimize the cumulative deviation and determine the matching depth point on the curve corresponding to each observation depth point. Based on the measured dose value and the dose value at the matching depth point on the curve, the intensity scaling factor characterizing the beam output intensity is calculated; the range deviation characterizing the beam penetration depth is calculated by observing the depth difference between the depth point and the matching depth point. By utilizing range deviation and intensity scaling factor, a spatial translation transformation and global dose correction are performed on the standard three-dimensional dose distribution map to generate a predicted three-dimensional dose distribution map that eliminates detector interference.
2. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 1, characterized in that, The gradient weights are calculated as follows: the derivative of the reference dose curve at each depth point is calculated, and its absolute value is normalized to serve as the gradient weights at each depth point.
3. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 1, characterized in that, The process of dividing the sampling interval is as follows: for the reference dose curve, the continuous depth range where the gradient weight is less than a preset threshold is defined as the low sampling interval; the continuous depth range where the gradient weight is greater than or equal to the preset threshold is defined as the high sampling interval.
4. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 3, characterized in that, The acquisition of the observation depth point includes: Within the low sampling interval, sampling is performed with a preset first sampling step size, and within the high sampling interval, sampling is performed with a preset second sampling step size to obtain discrete points sampled in the two intervals; wherein, the preset first sampling step size is greater than the preset second sampling step size. The depth corresponding to the peak value on the reference dose curve is taken as the peak point, and the depth corresponding to the preset percentage of the reference dose curve that drops to the peak value is taken as the drop point. All discrete points, peak points, and drop points sampled are defined as observation depth points.
5. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 1, characterized in that, Determining the matching depth point on the curve corresponding to each observation depth point includes: For each observation depth point, a preset local depth range is constructed with its depth as the center; all depth points contained in this local depth range are defined as candidate depth points. Based on the gradient weights of each observation depth point and the difference between the measured dose value and the dose value at the candidate depth point on the curve, the local cost between each observation depth point and its corresponding candidate depth point is calculated. Construct a cost matrix A, where rows represent observed depth points and columns represent candidate depth points; for any element in the cost matrix... If candidate depth points Located at the observation depth point Within the local depth range, its value is the local cost; otherwise, its value is assigned to infinity. Search for the path with the minimum cumulative cost in the cost matrix, and define the candidate depth points corresponding to each observed depth point on the path as the matching depth points.
6. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 5, characterized in that, The calculation process of the local cost is as follows: for each candidate depth point corresponding to each observation depth point, the difference between the measured dose value of each observation depth point and the dose value of the reference dose curve at the candidate depth point is calculated as the dose deviation; the product of the gradient weight corresponding to each observation depth point and the dose deviation is used as the local cost between each observation depth point and its corresponding candidate depth point.
7. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 1, characterized in that, The intensity scaling factor is the ratio between the sum of the measured dose values at all observed depth points and the sum of the dose values at all matching depth points on the reference dose curve.
8. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 4, characterized in that, The calculation process for the range deviation is as follows: select observation depth points whose depth is located within the high sampling interval, calculate the depth difference between them and the corresponding matching depth points, and take the depth difference as the depth difference; take the median of all depth differences as the range deviation.
9. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 1, characterized in that, The specific process of the spatial translation transformation is as follows: using the range deviation, the spatial coordinates of all voxels in the standard three-dimensional dose distribution map are translated along the Z-axis.
10. The three-dimensional radiotherapy dose distribution prediction method for proton and heavy ion radiotherapy equipment as described in claim 9, characterized in that, The specific process of global dose correction is as follows: calculate the product of the dose value of each voxel in the standard three-dimensional dose distribution map after translation transformation and the intensity scaling factor to generate the predicted three-dimensional dose distribution map.