Proton Dose Research Articles

Optimal use of limited proton resources for liver cancer patients in combined proton-photon treatments.

Liver cancer patients may benefit from proton therapy through increase of the tumor control probability (TCP). However, proton therapy is a limited resource and may not be available for all patients. We consider combined proton-photon liver SBRT treatments (CPPT) where only some fractions are delivered with protons. It is investigated how limited proton fractions can be used best for individual patients and optimally allocated within a patient group.

Approach: Photon and proton treatment plans were created for five liver cancer patients. In CPPT, limited proton fractions may be optimally exploited by increasing the fraction dose compared to photon fraction dose. To determine a patient's optimal proton and photon fraction dose, we maximize the target BED while constraining the mean normal liver BED, which leads to an up- or downscaling of the proton and photon plan, respectively. The resulting CPPT balances the benefits of fractionation in the normal liver versus exploiting the superior proton dose distributions. After converting the target BED to TCP, the optimal number of proton fractions per patient is determined by maximizing the overall TCP of the patient group.

Main results: For the individual patient, a CPPT treatment that delivers a higher fraction dose with protons than photons allows for dose escalation in the target compared to delivering the same proton and photon fraction dose. On the level of a patient group, CPPT may allow to distribute limited proton slots over several patients. Through an optimal use and allocation of proton fractions, CPPT may increase the average patient group TCP compared to a proton patient selection strategy where patients receive single-modality proton or photon treatments.

Significance: Limited proton resources can be optimally exploited via CPPT by increasing the target dose in proton fractions and allocating available proton slots to patients with the highest TCP increase.&#xD.

Optical and electrical properties of proton-implanted p-GaSb for electrical isolation

Abstract The effect of proton implantation as solation implant and subsequent annealing on the optical absorption and electrical resistivity of low-bandgap p-GaSb is reported. The measured transmittance spectra indicates that implantation creates a distribution of energy levels extending into the bandgap. Electrical measurements show that the average sheet resistance of the implanted layer increases only by an order of magnitude from its pre-implantation value at a proton dose of ~1013 cm-2 followed by 200°C annealing. It is also shown that annealing reduces the implantation-induced optical absorption while still retaining a high electrical resistivity.

A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

Proton dose calculation with LSTM networks in presence of a magnetic field

Objective.To present a long short-term memory (LSTM) network-based dose calculation method for magnetic resonance (MR)-guided proton therapy.Approach.35 planning computed tomography (CT) images of prostate cancer patients were collected for Monte Carlo (MC) dose calculation under a perpendicular 1.5 T magnetic field. Proton pencil beams (PB) at three energies (150, 175, and 200 MeV) were simulated (7560 PBs at each energy). A 3D relative stopping power cuboid covering the extent of the PB dose was extracted and given as input to the LSTM model, yielding a 3D predicted PB dose. Three single-energy (SE) LSTM models were trained separately on the corresponding 150/175/200 MeV datasets and a multi-energy (ME) LSTM model with an energy embedding layer was trained on either the combined dataset with three energies or a continuous energy (CE) dataset with 1 MeV steps ranging from 125 to 200 MeV. For each model, training and validation involved 25 patients and 10 patients were for testing. Two single field uniform dose prostate treatment plans were optimized and recalculated with MC and the CE model.Results.Test results of all PBs from the three SE models showed a mean gamma passing rate (2%/2 mm, 10% dose cutoff) above 99.9% with an average center-of-mass (COM) discrepancy below 0.4 mm between predicted and simulated trajectories. The ME model showed a mean gamma passing rate exceeding 99.8% and a COM discrepancy of less than 0.5 mm at the three energies. Treatment plan recalculation by the CE model yielded gamma passing rates of 99.6% and 97.9%. The inference time of the models was 9-10 ms per PB.Significance.LSTM models for proton dose calculation in a magnetic field were developed and showed promising accuracy and efficiency for prostate cancer patients.

Performance of a BeO-based dosimetry system for proton and electron beam dose measurements

This study aims to evaluate the performance of the BeO-based myOSLchip system (RadPro International GmbH, Remscheid, Germany) for dosimetry of proton and electron radiotherapy beams. Although beryllium oxide (BeO) has been recognised as a promising material for luminescence dosimetry in radiotherapy, this research extends beyond material properties and examines the entire BeO-based dosimetry system, including the detector, holder, and readout components.Packages of myOSLchip dosimeters were irradiated either in a (70−230) MeV proton beam or in a 16 MeV electron beam. The readouts were carried out using the portable myOSLchip reader. In the electron beam, tests on the precision, dose response up to 100 Gy and dose-rate effects of the system were carried out. In the proton beam, the system was tested for its dose response (up to 10 Gy), fading, and linear energy transfer (LET) response.For proton irradiations, the myOSLchip BeO OSLDs exhibited stability within 2% over 135 days, as well as a linear dose response in the tested range, (0.1−10) Gy. The efficiency showed a reduction for proton beams with LET values (for water) above 0.6 keV/μm, with up to 40% loss in efficiency at 4 keV/μm. For the electron irradiations, they showed a linear dose–response up to 20 Gy and dose-rate independence, with a constant response at least up to 2.99 × 105 Gy s−1. Using individual dosimeter sensitivity correction, the precision for a single dosimeter was around 3.5% (standard deviation of the data of all dosimeters) and for a package comprising four dosimeters was 1.7% (standard deviation of the mean of the four dosimeters).These findings suggest the myOSLchip system’s potential as a reliable alternative to existing dosimetry systems in clinical applications.

A comparative study on automatic treatment planning for online adaptive proton therapy of esophageal cancer: which combination of deformable registration and deep learning planning tools performs the best?

Objective.To demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These strategies leverage existing deformable image registration (DIR) algorithms and state-of-the-art deep learning (DL) networks for organ segmentation and proton dose prediction.Approach.Four OAPT strategies featuring automatic segmentation and robust optimization were evaluated on a cohort of 17 patients, each undergoing a repeat CT scan. (1) DEF-INIT combines deformably registered contours with template-based optimization. (2) DL-INIT, (3) DL-DEF, and (4) DL-DL employ a nnU-Net DL network for organ segmentation and a controlling ROIs-guided DIR algorithm for internal clinical target volume (iCTV) segmentation. DL-INIT uses this segmentation alongside template-based optimization, DL-DEF integrates it with a dose-mimicking (DM) step using a reference deformed dose, and DL-DL merges it with DM on a reference DL-predicted dose. All strategies were evaluated on manual contours and contours used for optimization and compared with manually adapted plans. Key dose volume metrics like iCTV D98% are reported.Main results.iCTV D98% was comparable in manually adapted plans and for all strategies in nominal cases but dropped to 20 Gy in worst-case scenarios for a few patients per strategy, highlighting the need to correct segmentation errors in the target volume. Evaluations on optimization contours showed minimal relative error, with some outliers, particularly in template-based strategies (DEF-INIT and DL-INIT). DL-DEF achieves a good trade-off between speed and dosimetric quality, showing a passing rate (iCTV D98% > 94%) of 90% when evaluated against 2, 4 and 5 mm setup error and of 88% when evaluated against 7 mm setup error. While template-based methods are more rigid, DL-DEF and DL-DL have potential for further enhancements with proper DM algorithm tuning.Significance.Among investigated strategies, DL-DEF and DL-DL demonstrated promising within 10 min OAPT implementation results and significant potential for improvements.

Assessing the biological effects of boron neutron capture therapy through cellular DNA damage repair model.

Boron neutron capture therapy (BNCT) is a targeted radiotherapy that relies on the 10B (n, α) 7Li reaction, which produces secondary particles with high linear energy transfer (LET), leading to a high relative biological effectiveness (RBE) in tumors. The biological effectiveness of BNCT is influenced by factors such as boron distribution and concentration, necessitating improved methods for assessing its radiobiological effects and clarifying the sensitivity of the differences in different factors to the biological effects. This paper introduces a method to evaluate the biological effects of BNCT using the cellular repair model. This method aims to overcome some of the limitations of current evaluation approaches. The primary goal is to provide guidance for clinical treatments and the development of boron drugs, as well as to investigate the impact of the synergistic effects of mixed radiation fields in BNCT on treatment outcomes. The approach involves three key steps: first, extending the radial energy deposition distribution of BNCT secondary particles using Geant4-DNA. This allows for the calculation of initial DNA double-strand breaks (DSBs) distributions for a given absorbed dose. Next, the obtained initial DSB distributions are used for DNA damage repair simulations to generate cell survival curves, then thereby quantifying RBE and compound biological effectiveness (CBE). The study also explores the synergistic effects of the mixed radiation fields in BNCT on assessing biological effects were also explored in depth. The results showed that the RBE of boronophenylalanine (BPA) and sodium borocaptate (BSH) drugs at cell survival fraction 0.01 was 2.50 and 2.15, respectively. The CBE of the boron dose component was 3.60 and 0.73, respectively, and the RBE of the proton component was 3.21, demonstrating that BPA has a significantly higher biological impact than BSH due to superior cellular permeability. The proton dose significance in BNCT treatment is also underscored, necessitating consideration in both experimental and clinical contexts. The study demonstrates that synergistic effects between disparate radiation fields lead to increased misrepairs and enhanced biological impact. Additionally, the biological effect diminishes with rising boron concentration, emphasizing the need to account for intercellular DNA damage heterogeneity. This methodology offers valuable insights for the development of new boron compounds and precise assessment of bio-weighted doses in clinical settings and can be adapted to other therapeutic modalities.

Development of an algorithm for proton dose calculation in magnetic fields.

The advantages of proton therapy can be further enhanced with online magnetic resonance imaging (MRI) guidance. One of the challenges in the realization of MRI-guided proton therapy (MRPT) is accurately calculating the radiation dose in the presence of magnetic fields. This study aims to develop an efficient and accurate proton dose calculation algorithm adapted to the presence of magnetic fields. An analytical-numerical radiation dose calculation algorithm, Proton and Ion Dose Engine (PRIDE), was developed. The algorithm combines the pencil beam algorithm (PBA) with a novel iterative voxel-based ray-tracing algorithm. The new ray-tracing method uses fewer assumptions and ensures broader applicability for proton beam trajectory prediction in magnetic fields, and has been compared to Wolf's method and Schellhammer's method. The accuracy of PRIDE algorithm was validated on three phantoms and two practical plans (one single-field water plan and one prostate tumor plan) in different magnetic field strengths up to 3.0 T. The validation was performed by comparing the results against the Monte Carlo (MC) simulations, using the global gamma index criteria of 2%/2mm and 3%/3mm with a 10% threshold. PRIDE showed good agreement with MC in homogeneous and slab heterogeneous phantom, achieving gamma passing rates (%GPs) above 99% for 2%/2mm criteria when magnetic field strength is not greater than 1.5 T. Although the agreement decreased for scenarios involving high proton energy (240 MeV) and strong magnetic field (3.0 T), the 2%/2mm %GPs still remained above 98%. In lateral heterogeneous phantom, the accuracy of PRIDE decreased due to the PBA's limitation. For the two practical plans in different magnetic fields, %GPs exceeded 98% and 99% for 2%/2mm and 3%/3mm criteria, respectively. PRIDE can perform efficient and accurate proton dose calculation in magnetic fields up to 3.0 T, and is expected to work as a useful tool for proton dose calculation in MRPT.

Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks

Background and PurposePoor image quality of cone-beam computed tomography (CBCT) images can hinder proton dose calculation to assess the influence of anatomy changes. The aim of this study was to evaluate image quality and proton dose calculation accuracy of synthetic CTs generated from CBCT using unsupervised 3D deep-learning networks. Materials and methodsA total of 102 head-and-neck cancer patients were used to train (N=82) and test (N=20) i) a cycle-consistent generative adversarial network, ii) a contrastive unpaired translation, and iii) a fusion of the two (CycleCUT). For patients in the test set, a repeat CT was deformably registered to a same-day CBCT to create a ground-truth CT for comparison. The proton plan was re-calculated on the ground-truth CT and synthetic CTs. The image quality of the synthetic CTs was evaluated using peak signal-to-noise ratio, structural similarity index measure, mean error, and mean absolute error (MAE). Proton dose calculation accuracy was assessed through 3D gamma analysis and dose-volume-histogram parameters. ResultsAll synthetic CTs accurately preserved the CBCT anatomy (verified by visual inspection) while improving the image quality. The CycleCUT network had slightly improved image quality compared to the other networks (MAE in body: 53 Hounsfield units (HU) vs. 54/55 HU). All networks had similar proton dose calculation accuracy with gamma passing rate above 97%. ConclusionsAll three evaluated networks generated synthetic CT images with dose distributions comparable to those of conventional fan-beam CT. The synthetic CT generation was fast, making all networks feasible for adaptive proton therapy.

Impact of Proton Irradiation Depending on Breast Cancer Subtype in Patient-Derived Cell Lines

Research on different types of ionizing radiation’s effects has been ongoing for years, revealing its efficacy in damaging cancer cells. Solid tumors comprise diverse cell types, each being able to respond differently to radiation. This study evaluated the radiobiological response of established (MDA-MB-231 (Triple negative breast cancer, TNBC), MCF-7 (Luminal A)) and patient-derived malignant cell lines, cancer-associated fibroblasts, and skin fibroblasts following proton IRR. All cell line types were irradiated with the proton dose of 2, 4, and 6 Gy. The radiobiological response was assessed using clonogenic assay, γH2AX, and p53 staining. It was noticeable that breast cancer lines of different molecular subtypes displayed no significant variations in their response to proton IRR. In terms of cancer-associated fibroblasts extracted from the tumor tissue, the line derived from a TNBC subtype tumor demonstrated higher resistance to ionizing radiation compared to lines isolated from luminal A tumors. Fibroblasts extracted from patients’ skin responded identically to all doses of proton radiation. This study emphasizes that tumor response is not exclusively determined by the elimination of breast cancer cells, but also takes into account tumor microenvironmental variables and skin reactions.

Binary proton therapy of Ehrlich carcinoma using targeted gold nanoparticles

Proton therapy can treat tumors located in radiation-sensitive tissues. This article demonstrates the possibility of enhancing the proton therapy with targeted gold nanoparticles that selectively recognize tumor cells. Au-PEG nanoparticles at concentrations above 25 mg/L and 4 Gy proton dose caused complete death of EMT6/P cells in vitro. Binary proton therapy using targeted Au-PEG-FA nanoparticles caused an 80% tumor growth inhibition effect in vivo. The use of targeted gold nanoparticles is promising for enhancing the proton irradiation effect on tumor cells and requires further research to increase the therapeutic index of the approach.

Discordance in acute gastrointestinal toxicity between synchrotron-based proton and linac-based electron ultra-high dose rate irradiation.

Proton FLASH has been investigated using cyclotron and synchrocyclotron beamlines but not synchrotron beamlines. We evaluated the impact of dose rate (ultra-high [UHDR] vs. conventional [CONV]) and beam configuration (shoot-through [ST] vs. spread-out-Bragg-peak [SOBP]) on acute radiation-induced gastrointestinal toxicity (RIGIT) in mice. We also compared RIGIT between synchrotron-based protons and linac-based electrons with matched mean dose rates. We administered abdominal irradiation (12-14 Gy single fraction) to female C57BL/6J mice with an 87 MeV synchrotron-based proton beamline (2 cm diameter field size as a lateral beam). Dose rates were 0.2 Gy/s (S-T pCONV), 0.3 Gy/s (SOBP pCONV), 150 Gy/s (S-T pFLASH), and 230 Gy/s (SOBP pFLASH). RIGIT was assessed by the jejunal regenerating crypt assay and survival. We also compared responses to proton [pFLASH and pCONV] with responses to electron CONV (eCONV, 0.4 Gy/s) and electron FLASH (eFLASH, 188-205 Gy/s). The number of regenerating jejunal crypts at each matched dose was lowest for pFLASH (similar between S-T and SOBP), greater and similar between pCONV (S-T and SOBP) and eCONV, and greatest for eFLASH. Correspondingly, mice that received pFLASH SOBP had the lowest survival rates (50% at 50 days), followed by pFLASH S-T (80%), and pCONV SOBP (90%), but 100% of mice receiving pCONV S-T survived (log-rank P = 0.047 for the four groups). Our findings are consistent with an increase in RIGIT after synchrotron-based pFLASH versus pCONV. This negative proton-specific FLASH effect versus linac-based electron irradiation underscores the importance of understanding the physical and biological factors that will allow safe and effective clinical translation.

Feasibility of the Compton Suppressed Gamma-ray spectrometry for proton therapy applications

The presence of the Bragg peak and its use in proton therapy cause less damage to healthy tissues. For optimal use of the proton beam in proton therapy, it is necessary to monitor the deviation of its range during treatment. The prompt gamma rays originated from the nuclear reactions of the proton beam with tissue elements can be applied for this purpose. The spatial distribution of prompt gamma rays can be used to estimate the range of the incident charged particles. Detecting a wide energy range of gamma rays, up to 10 MeV, is the most challenging step of the proton dose verification by using prompt gamma spectroscopy. Compton suppression is a system that is used to reduce the contribution of scattered gamma rays. This technique will cause better detection of the peaks of low-energy prompt gamma rays. In this research, the performance of a Compton suppression system has been investigated in two fields for proton therapy and for elemental analysis of materials using isotopes by a wide range of gamma energies. At first, by using the simulated Compton suppression system, the calculations of the prompt gamma ray spectra emitted from the brain phantom during the radiation of 150 MeV protons were presented. The results indicated that the low energy elements peaks such as 40Ca (1.37 MeV) with CSF equal to 1.138 appeared in the suppressed spectrum. Also, it was possible to detect 14N (2.31 MeV) and 16O (6.13 MeV) peaks equivalent to the CSF 2.449 and 2.067 respectively. In the next step, it was possible to detect the low energy peaks of 152Eu and 155Eu isotopes in the suppressed spectrum. For this purpose, the minimum relative intensity required to detect all 152Eu and 155Eu peaks was equivalent to 0.001 and 0.002 respectively. CSF for the lowest energies such as 105 keV and 344 keV was equal to 1.169 and 1.184. The simulation was done using the GEANT4 Monte-Carlo toolkit.

Image plate multi-scan response to fusion protons in the range of 1-14 MeV.

Image plates (IPs) are a quickly recoverable and reusable radiation detector often used to measure proton and x-ray fluence in laser-driven experiments. Recently, IPs have been used in a proton radiography detector stack on the OMEGA laser, a diagnostic historically implemented with CR-39, or radiochromic film. The IPs used in this and other diagnostics detect charged particles, neutrons, and x-rays indiscriminately. IPs detect radiation using a photo-stimulated luminescence (PSL) material, often phosphor, in which electrons are excited to metastable states by ionizing radiation. Protons at MeV energies deposit energy deeper into the IP compared with x rays below ∼20keV due to the Bragg peak present for protons. This property is exploited to discriminate between radiation types. Doses of mono-energetic protons between 1.7 and 14MeV are applied to IPs using the MIT linear electrostatic ion accelerator. This paper presents the results from consecutive scans of IPs irradiated with different proton energies. The PSL ratios between subsequent scans are shown to depend on proton energy, with higher energy protons having lower PSL ratios for each scan. This finding is separate from the known energy dependence in the absolute sensitivity of IPs. The results can be compared to complimentary work on x rays, showing a difference between protons and x rays, forging a path to discriminate between proton and x-ray fluence in mixed radiation environments.

Analytical parameterization of Bragg curves for proton beams in muscle, bone, and polymethylmethacrylate.

Proton dose calculation in media other than water may be of interest for either research purposes or clinical practice. Current study aims to quantify the required parameters for analytical proton dosimetry in muscle, bone, and PMMA. Required analytical dosimetry parameters were extracted from ICRU-49 report and Janni study. Geant4 Toolkit was also used for Bragg curve simulation inside the investigated media at different proton energies. Calculated and simulated dosimetry data were compared using gamma analysis. Simulated and calculated Bragg curves are consistent, a fact that confirms the validity of reported parameters for analytical proton dosimetry inside considered media. Furthermore, derived analytical parameters for these media are different from those of water. Listed parameters can be reliably utilized for analytical proton dosimetry inside muscle, bone, and PMMA. Furthermore, accurate proton dosimetry inside each medium demands dedicated analytical parameters and one is not allowed to use the water coefficients for non-water media.

2190: NTCP modeling for rectal complication based on variable RBE-weighted proton dose for prostate cancer

2190: NTCP modeling for rectal complication based on variable RBE-weighted proton dose for prostate cancer

Total Ionizing Dose (TID) Effects on the 1.2 kV SiC MOSFETs under Proton Irradiation

In this paper, the effects of various proton irradiation energies and doses on the electrical characteristics of SiC MOSFETs have been evaluated and characterized using a proton accelerator. The devices under test were designed, fabricated and packaged using 1.2 kV/0.6 µm-tech SiC MOSFET processes. The results demonstrate that the threshold voltage (Vth) of the irradiated devices shifted towards negative values due to the radiation-induced positive oxide trapped charges. Moreover, this negative shift in Vth and positive trapped charges of field limiting ring (FLR) oxide led to an increase in output currents and a reduction in the breakdown voltage values.

TEAM: Triangular-mEsh Adaptive and Multiscale proton spot generation method.

Proton therapy is preferred for its dose conformality to spare normal tissues and organs-at-risk (OAR) via Bragg peaks with negligible exit dose. However, proton dose conformality can be further optimized: (1) the spot placement is based on the structured (e.g., Cartesian) grid, which may not offer conformal shaping to complex tumor targets; (2) the spot sampling pattern is uniform, which may be insufficient at the tumor boundary to provide the sharp dose falloff, and at the same time may be redundant at the tumor interior to provide the uniform dose coverage, for example, due to multiple Coulomb scattering (MCS); and (3) the lateral spot penumbra increases with respect to the depth due to MCS, which blurs the lateral dose falloff. On the other hand, while (1) the deliverable spots are subject to the minimum-monitor-unit (MMU) constraint, and (2) the dose rate is proportional to the MMU threshold, the current spot sampling method is sensitive to the MMU threshold and can fail to provide satisfactory plan quality for a large MMU threshold (i.e., high-dose-rate delivery). This work will develop a novel Triangular-mEsh-based Adaptive and Multiscale (TEAM) proton spot generation method to address these issues for optimizing proton dose conformality and plan delivery efficiency. Compared to the standard clinically-used spot placement method, three key elements of TEAM are as follows: (1) a triangular mesh instead of a structured grid: the triangular mesh is geometrically more conformal to complex target shapes and therefore more efficient and accurate for dose shaping inside and around the target; (2) adaptive sampling instead of uniform sampling: the adaptive sampling consists of relatively dense sampling at the tumor boundary to create the sharp dose falloff, which is more accurate, and coarse sampling at the tumor interior to uniformly cover the target, which is more efficient; and (3) depth-dependent sampling instead of depth-independent sampling: the depth-dependent sampling is used to compensate for MCS, that is, with increasingly dense sampling at the tumor boundary to improve dose shaping accuracy, and increasingly coarse sampling at the tumor interior to improve dose shaping efficiency, as the depth increases. In the TEAM method the spot locations are generated for each energy layer and layer-by-layer in the multiscale fashion; and then the spot weights are derived by solving the IMPT problem of dose-volume planning objectives, MMU constraints, and robustness optimization with respect to range and setup uncertainties. Compared to the standard clinically-used spot placement method UNIFORM, TEAM achieved (1) better plan quality using <60% number of spots of UNIFORM; (2) better robustness to the number of spots; (3) better robustness to a large MMU threshold. Furthermore, TEAM provided better plan quality with fewer spots than other adaptive methods (Cartesian-grid or triangular-mesh). A novel triangular-mesh-based proton spot placement method called TEAM is proposed, and it is demonstrated to improve plan quality, robustness to the number of spots, and robustness to the MMU threshold, compared to the clinically-used spot placement method and other adaptive methods.

Proton dose deposition matrix prediction using multi-source feature driven deep learning approach

Proton dose deposition results are influenced by various factors, such as irradiation angle, beamlet energy and other parameters. The calculation of the proton dose deposition matrix (DDM) can be highly complex but is crucial in intensity-modulated proton therapy (IMPT). In this work, we present a novel deep learning (DL) approach using multi-source features for proton DDM prediction. The DL5 proton DDM prediction method involves five input features containing beamlet geometry, dosimetry and treatment machine information like patient CT data, beamlet energy, distance from voxel to beamlet axis, distance from voxel to body surface, and pencil beam (PB) dose. The dose calculated by Monte Carlo (MC) method was used as the ground truth dose label. A total of 40 000 features, corresponding to 8000 beamlets, were obtained from head patient datasets and used for the training data. Additionally, seventeen head patients not included in the training process were utilized as testing cases. The DL5 method demonstrates high proton beamlet dose prediction accuracy, with an average determination coefficient R 2 of 0.93 when compared to the MC dose. Accurate beamlet dose estimation can be achieved in as little as 1.5 milliseconds for an individual proton beamlet. For IMPT plan dose comparisons to the dose calculated by the MC method, the DL5 method exhibited gamma pass rates of γ(2 mm, 2%) and γ(3 mm, 3%) ranging from 98.15% to 99.89% and 98.80% to 99.98%, respectively, across all 17 testing cases. On average, the DL5 method increased the gamma pass rates to γ(2 mm, 2%) from 82.97% to 99.23% and to γ(3 mm, 3%) from 85.27% to 99.75% when compared with the PB method. The proposed DL5 model enables rapid and precise dose calculation in IMPT plan, which has the potential to significantly enhance the efficiency and quality of proton radiation therapy.

Proton spot dose estimation based on positron activity distributions with neural network.

Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.