3D Dose Distributions Research Articles

Integrating plan complexity and dosiomics features with deep learning in patient-specific quality assurance for volumetric modulated arc therapy

PurposeTo investigate the feasibility and performance of deep learning (DL) models combined with plan complexity (PC) and dosiomics features in the patient-specific quality assurance (PSQA) for patients underwent volumetric modulated arc therapy (VMAT).MethodsTotal of 201 VMAT plans with measured PSQA results were retrospectively enrolled and divided into training and testing sets randomly at 7:3. PC metrics were calculated using house-built algorithm based on Matlab. Dosiomics features were extracted and selected using Random Forest (RF) from planning target volume (PTV) and overlap regions with 3D dose distributions. The top 50 dosiomics and 5 PC features were selected based on feature importance screening. A DL DenseNet was adapted and trained for the PSQA prediction.ResultsThe measured average gamma passing rate (GPR) of these VMAT plans was 97.94% ± 1.87%, 94.33% ± 3.22%, and 87.27% ± 4.81% at the criteria of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. Models with PC features alone demonstrated the lowest area under curve (AUC). The AUC and sensitivity of PC and dosiomics (D) combined model at 2%/2 mm were 0.915 and 0.833, respectively. The AUCs of DL models were improved from 0.943, 0.849, 0.841 to 0.948, 0.890, 0.942 in the combined models (PC + D + DL) at 3%/3 mm, 3%/2 mm and 2%/2 mm, respectively. A best AUC of 0.942 with a sensitivity, specificity and accuracy of 100%, 81.8%, and 83.6% was achieved with combined model (PC + D + DL) at 2%/2 mm.ConclusionsIntegrating DL with dosiomics and PC metrics is promising in the prediction of GPRs in PSQA for patients underwent VMAT.

Development of a quasi-3D dosimeter using radiochromic plastic for patient-specific quality assurance.

Patient-specific QA verification ensures patient safety and treatment by verifying radiation delivery and dose calculations in treatment plans for errors. However, a two-dimensional (2D) dose distribution is insufficient for detecting information on the three-dimensional (3D) dose delivered to the patient. In addition, 3D radiochromic plastic dosimeters (RPDs) such as PRESAGE® represent the volume effect in which the dosimeters have different sensitivities according to the size of the dosimeters. Therefore, to solve the volume effect, a Quasi-3D dosimetry system was proposed to perform patient-specific QA using predetermined-sized and multiple RPDs. For patient-specific quality assurance (QA) in radiation treatment, this study aims to assess a quasi-3D dosimetry system using an RPD. Gamma analysis was performed to verify the agreement between the measured and estimated dose distributions of intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). We fabricated cylindrical RPDs and a quasi-3D dosimetry phantom. A practicability test for a pancreatic patient utilized a quasi-3D dosimetry device, an in-house RPD, and a quasi-3D phantom. The dose distribution of the VMAT design dictated the placement of nine RPDs. Moreover, a 2D diode array detector was used for 2D gamma analysis (MapCHECK2). The patient-specific QA was performed for IMRT, VMAT, and stereotactic ablative radiotherapy (SABR) in 20 prostate and head-and-neck patients. For each patient, six RPDs were positioned according to the dose distribution. VMAT SABR and IMRT/VMAT plans employed a 2%/2mm gamma criterion, whereas IMRT/VMAT plans used a 3%/2mm gamma criterion, a 10% threshold value, and a 90% passing rate tolerance. 3D gamma analysis was conducted using the 3D Slicer software. The average gamma passing rates with 2%/2mm and 3%/3 mm criteria for relative dose distribution were 91.6%±1.4% and 99.4%±0.7% for the 3D gamma analysis using the quasi-3D dosimetry system, respectively, and 97.5% and 99.3% for 2D gamma analysis using MapCHECK2, respectively. The 3D gamma analysis for patient-specific QA of 20 patients showed passing rates of over 90% with 2%/2mm, 3%/2mm, and 3%/3mm criteria. The quasi-3D dosimetry system was evaluated by performing patient-specific QAs with RPDs and quasi-3D phantom. The gamma indices for all RPDs showed more than 90% for 2%/2mm, 3%/2mm, and 3%/3mm criteria. We verified the feasibility of a quasi-3D dosimetry system by performing the conventional patient-specific QA with the quasi-3D dosimeters.

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT

BackgroundCurrent intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively.MethodsWe proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body.ResultsThe proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose.ConclusionWe proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.

Dose distributions of the INTRABEAM electronic brachytherapy system measured with traceable detectors

In this work, a methodology to determine the absorbed dose rate to water distribution from electronic brachytherapy (eBT) units is suggested. It involves measuring directly in a full scatter water tank with small active detectors whose energy response at low X-ray photon energies have been previously characterized. Dose distributions of an INTRABEAM PRS 500 system in two configurations — bare needle and with the spherical applicator of 40 mm diameter — were measured in water using the Exradin A26 ionization chamber and the PTW 60019 microDiamond detector. The continuous energy response of these two detectors in terms of air kerma from 5 keV to 70 keV (eBT energy range) was available from a previous calibration at several X-ray radiation qualities. Here, it is converted to the response in absorbed dose to water using correction factors obtained with Monte Carlo (MC) particle transport simulations. The detectors were modelled using the MCNP code. The air kerma calibration factors were simulated and compared with measurements to refine and validate the models. A series of simulations of the experimental arrangement of the INTRABEAM and the detector were carried out for determining the system specific detector correction factors. These values are distance-dependent due to the steep dose gradient and the remarkable spectral changes around the eBT source in water. The dose rate with distance from the source, the radial and the polar distributions of the INTRABEAM are reported and compared with the manufacturers supplied data. This work is an effort to provide traceability for detectors and measurement procedures for the determination of 3D dose distributions as part of the European joint research project “Primary standards and traceable measurement methods for X-ray emitting electronic brachytherapy devices” (18NRM02 PRISM-eBT).

Visualization and comparison of CT dose distribution between axial and helical acquisitions.

It is challenging to assess the accuracy of volume CT Dose Index (CTDIvol ) when the axial scan modes corresponding to a helical scan protocol are not available. An alternative approach was proposed to directly measure using helical acquisitions and relatively small differences (<20%) from CTDIvol were observed. To visually demonstrate the 3D dose distribution for both axial and helical CT acquisitions and quantitively compare and CTDIvol . 3D dose distribution within the standard CTDI phantoms (16 and 32cm diameter) from a single CT projection, Dp (x,y,z) was first generated using Monte Carlo simulation (GEANT4) with 9×108 photons per combination of tube voltage (80-140kV), collimation width (1-8cm), and z-axis location of the central ray of the x-ray beam, with a spatial resolution of 1 mm3 . These dose distributions from one single projection were analytically ensembled to simulate 3D dose volumes DA (x,y,z) and DH (x,y,z) for axial and helical scans, respectively, with different helical pitches (0.3-2) and scan lengths (100-150mm). 2D planar dose distributions were obtained by integrating the inside 100mm of the dose volumes. CTDIvol and were calculated using the planar dose data at corresponding pencil chamber locations and the percentage differences (PD) were reported. High spatial resolution 3D CT dose volumes were generated and visualized. PDs between and CTDIvol had strong dependency on scan length and peripheral chamber locations, with subtle dependency on collimation width and pitch. PDs were mostly within the range of±3% for a scan length of 150mm with four peripheral chamber locations. With a scan length covering the entire phantom length, directly measured from helical scans can serve as an alternative to CTDIvol only if all four peripheral locations were measured.

A motion model-guided 4D dose reconstruction for pencil beam scanned proton therapy

Objective. 4D dose reconstruction in proton therapy with pencil beam scanning (PBS) typically relies on a single pre-treatment 4DCT (p4DCT). However, breathing motion during the fractionated treatment can vary considerably in both amplitude and frequency. We present a novel 4D dose reconstruction method combining delivery log files with patient-specific motion models, to account for the dosimetric effect of intra- and inter-fractional breathing variability. Approach. Correlation between an external breathing surrogate and anatomical deformations of the p4DCT is established using principal component analysis. Using motion trajectories of a surface marker acquired during the dose delivery by an optical tracking system, deformable motion fields are retrospectively reconstructed and used to generate time-resolved synthetic 4DCTs (‘5DCTs’) by warping a reference CT. For three abdominal/thoracic patients, treated with respiratory gating and rescanning, example fraction doses were reconstructed using the resulting 5DCTs and delivery log files. The motion model was validated beforehand using leave-one-out cross-validation (LOOCV) with subsequent 4D dose evaluations. Moreover, besides fractional motion, fractional anatomical changes were incorporated as proof of concept. Main results. For motion model validation, the comparison of 4D dose distributions for the original 4DCT and predicted LOOCV resulted in 3%/3 mm gamma pass rates above 96.2%. Prospective gating simulations on the p4DCT can overestimate the target dose coverage V95% by up to 2.1% compared to 4D dose reconstruction based on observed surrogate trajectories. Nevertheless, for the studied clinical cases treated with respiratory-gating and rescanning, an acceptable target coverage was maintained with V95% remaining above 98.8% for all studied fractions. For these gated treatments, larger dosimetric differences occurred due to CT changes than due to breathing variations. Significance. To gain a better estimate of the delivered dose, a retrospective 4D dose reconstruction workflow based on motion data acquired during PBS proton treatments was implemented and validated, thus considering both intra- and inter-fractional motion and anatomy changes.

Single-Compartment Dose Prescriptions for Ablative 90Y-Radioembolization Segmentectomy.

Yttrium-90 (90Y) radioembolization is increasingly being utilized with curative intent. While single-compartment doses with respect to the perfused volume for the complete pathologic necrosis (CPN) of tumors have been reported, the actual doses delivered to the tumor and at-risk margins that leads to CPN have hitherto not been estimated. We present an ablative dosimetry model that calculates the dose distribution for tumors and at-risk margins based on numerical mm-scale dose modeling and the available clinical CPN evidence and report on the necessary dose metrics needed to achieve CPN following 90Y-radioembolization. Three-dimensional (3D) activity distributions (MBq/voxel) simulating spherical tumors were modeled with a 121 × 121 × 121 mm3 soft tissue volume (1 mm3 voxels). Then, 3D dose distributions (Gy/voxel) were estimated by convolving 3D activity distributions with a 90Y 3D dose kernel (Gy/MBq) sized 61 × 61 × 61 mm3 (1 mm3 voxels). Based on the published data on single-compartment segmental doses for the resected liver samples of HCC tumors showing CPN after radiation segmentectomy, the nominal voxel-based mean tumor dose (DmeanCPN), point dose at tumor rim (DrimCPN), and point dose 2 mm beyond the tumor boundary (D2mmCPN), which are necessary to achieve CPN, were calculated. The single-compartment dose prescriptions to required achieve CPN were then analytically modeled for more general cases of tumors with diameters dt = 2, 3, 4, 5, 6, and 7 cm and with tumor-to-normal-liver uptake ratios T:N = 1:1, 2:1, 3:1, 4:1, and 5:1. The nominal case defined to estimate the doses needed for CPN, based on the previously published clinical data, was a single hyperperfused tumor with a diameter of 2.5 cm and T:N = 3:1, treated with a single-compartment segmental dose of 400 Gy. The voxel-level doses necessary to achieve CPN were 1053 Gy for the mean tumor dose, 860 Gy for the point dose at the tumor boundary, and 561 Gy for the point dose at 2 mm beyond the tumor edge. The single-compartment segmental doses necessary to satisfy the criteria for CPN in terms of the mean tumor dose, point dose at the tumor boundary, and the point dose at 2 mm beyond the tumor edge were tabulated for a range of tumor diameters and tumor-to-normal-liver uptake ratios. The analytical functions that describe the relevant dose metrics for CPN and, more importantly, the single-compartment dose prescriptions for the perfused volume needed to achieve CPN are reported for a large range of conditions in terms of tumor diameters (1-7 cm) and T:N uptake ratios (2:1-5:1).

Implementation of novel measurement-based patient-specific QA for pencil beam scanning proton FLASH radiotherapy.

Several studies have shown pencil beam scanning (PBS) proton therapy is a feasible and safe modality to deliver conformal and ultra-high dose rate (UHDR) FLASH radiation therapy. However, it would be challenging and burdensome to conduct the quality assurance (QA) of the dose rate along with conventional patient-specific QA (psQA). To demonstrate a novel measurement-based psQA program for UHDR PBS proton transmission FLASH radiotherapy (FLASH-RT) using a high spatiotemporal resolution 2D strip ionization chamber array (SICA). The SICA is a newly designed open-air strip-segmented parallel plate ionization chamber, which is capable of measuring spot position and profile through 2mm-spacing-strip electrodes at a 20kHz sampling rate (50μs per event) and has been characterized to exhibit excellent dose and dose rate linearity under UHDR conditions. A SICA-based delivery log was collected for each irradiation containing the measured position, size, dwell time, and delivered MU for each planned spot. Such spot-level information was compared with the corresponding quantities in the treatment planning system (TPS). The dose and dose rate distributions were reconstructed on patient CT using the measured SICA log and compared to the planned values in volume histograms and 3D gamma analysis. Furthermore, the 2D dose and dose rate measurements were compared with the TPS calculations of the same depth. In addition, simulations using different machine-delivery uncertainties were performed, and QA tolerances were deduced. A transmission proton plan of 250MeV for a lung lesion was planned and measured in a dedicated ProBeam research beamline (Varian Medical System) with a nozzle beam current between 100 to 215nA. The worst gamma passing rates for dose and dose rate of the 2D SICA measurements (four fields) compared to TPS prediction (3%/3mm criterion) were 96.6% and 98.8%, respectively, whereas the SICA-log reconstructed 3D dose distribution achieved a gamma passing rate of 99.1% (2%/2mm criterion) compared to TPS. The deviations between SICA measured log, and TPS were within 0.3ms for spot dwell time with a mean difference of 0.069±0.11 s, within 0.2mm for spot position with a mean difference of -0.016±0.03mm in the x-direction, and -0.036±0.059mm in the y-direction, and within 3% for delivered spot MUs. Volume histogram metric of dose (D95) and dose rate (V40Gy/s ) showed minimal differences, within less than 1%. This work is the first to describe and validate an all-in-one measurement-based psQA framework that can fulfill the goals of validating the dose rate accuracy in addition to dosimetric accuracy for proton PBS transmission FLASH-RT. The successful implementation of this novel QA program can provide future clinical practice with more confidence in the FLASH application.

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms.

Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratorymotion. We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathingcycle. Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dosecalculations. Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reportedphantoms. A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcagemotion.

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.

In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics. A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices. The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92Gy (p=0.51) for attention Res U-Net, 0.94Gy (p=0.40) for Res U-Net, 2.94Gy (p=0.09) for attention U-Net, and 3.51Gy (p=0.08) for U-Net. For the OARs, the values for the and indices were 2.72Gy (p<0.01) for attention Res U-Net, 2.94Gy (p<0.01) for Res U-Net, 1.10Gy (p<0.01) for attention U-Net, 0.84Gy (p<0.29) for U-Net. All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.

Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods.

Gamma passing rate (GPR) predictions using machine learning methods have been explored for treatment verification of radiotherapy plans. However, these methods presented datasets with unbalanced number of plans having different treatment conditions (heterogeneous datasets), such as anatomical sites or dose per fractions, leading to lower model interpretability and prediction performance. We investigated the impact of the dataset composition on GPR binary classification (pass/fail) using random forest (RF), XG-boost, and neural network (NN) models. 945 plans were used to create one reference dataset (randomly assembled) and 24 customized datasets that considered four heterogeneity factors independently (anatomical region, number of arcs, dose per fraction, and treatment unit). 309 predictor features were extracted and calculated from plan parameters, modulation complexity metrics, and radiomic analysis (leave-trajectory maps, 3D dose distributions, and portal dosimetry images). The models' performances were measured using the area under the curve from the receiver operating characteristic (ROC-AUC). Radiomics features for reference models increased ROC-AUC values up to 13%, 15%, and 5% for RF, XG-Boost, and NN, respectively. The datasets with higher heterogeneous conditions presented the lower ROC-AUC values (RF: 0.72 ± 0.11, XG-Boost: 0.67 ± 0.1, NN: 0.89 ± 0.05) compared to models with less heterogeneous treatment conditions (RF: 0.88 ± 0.06, XG-Boost: 0.89 ± 0.07, NN: 0.98 ± 0.01). The ten most important features for each heterogeneity dataset group demonstrated their correlation with the treatments' physical aspects and GPR prediction. Improvements in data generalization and model performances can be associated with datasets having similar treatment conditions. This analysis might be implemented to evaluate the dataset quality and model consistency of further ML applications in radiotherapy. Dataset heterogeneities decrease ML model performance and reliability.

Impact of respiratory motion on proton pencil beam scanning FLASH radiotherapy: an in silico and phantom measurement study

Objective. To investigate the effects of respiratory motion on the delivered dose in the context of proton pencil beam scanning (PBS) transmission FLASH radiotherapy (FLASH-RT) by simulation and phantom measurements. Approach. An in-house simulation code was employed to perform in silico simulation of 2D dose distributions for clinically relevant proton PBS transmission FLASH-RT treatments. A moving simulation grid was introduced to investigate the impacts of various respiratory motion and treatment delivery parameters on the dynamic PBS dose delivery. A strip-ionization chamber array detector and an IROC motion platform were employed to perform phantom measurements of the 2D dose distribution for treatment fields similar to those used for simulation. Main results. Clinically relevant respiratory motion and treatment delivery parameters resulted in degradation of the delivered dose compared to the static delivery as translation and distortion. Simulation showed that the gamma passing rates (2 mm/2% criterion) and target coverage could drop below 50% and 80%, respectively, for certain scenarios if no mitigation strategy was used. The gamma passing rates and target coverage could be restored to more than 95% and 98%, respectively, for short beams delivered at the maximal inhalation or exhalation phase. The simulation results were qualitatively confirmed in phantom measurements with the motion platform. Significance. Respiratory motion could cause dose quality degradation in a clinically relevant proton PBS transmission FLASH-RT treatment if no mitigation strategy is employed, or if an adequate margin is not given to the target. Besides breath-hold, gated delivery can be an alternative motion management strategy to ensure high consistency of the delivered dose while maintaining minimal dose to the surrounding normal tissues. To the best of our knowledge, this is the first study on motion impacts in the context of proton transmission FLASH radiotherapy.

Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry.

Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry.

Toxicity Prediction in Pelvic Radiotherapy Using Multiple Instance Learning and Cascaded Attention Layers.

Modern radiotherapy delivers treatment plans optimised on an individual patient level, using CT-based 3D models of patient anatomy. This optimisation is fundamentally based on simple assumptions about the relationship between radiation dose delivered to the cancer (increased dose will increase cancer control) and normal tissue (increased dose will increase rate of side effects). The details of these relationships are still not well understood, especially for radiation-induced toxicity. We propose a convolutional neural network based on multiple instance learning to analyse toxicity relationships for patients receiving pelvic radiotherapy. A dataset comprising of 315 patients were included in this study; with 3D dose distributions, pre-treatment CT scans with annotated abdominal structures, and patient-reported toxicity scores provided for each participant. In addition, we propose a novel mechanism for segregating the attentions over space and dose/imaging features independently for a better understanding of the anatomical distribution of toxicity. Quantitative and qualitative experiments were performed to evaluate the network performance. The proposed network could predict toxicity with 80% accuracy. Attention analysis over space demonstrated that there was a significant association between radiation dose to the anterior and right iliac of the abdomen and patient-reported toxicity. Experimental results showed that the proposed network had outstanding performance for toxicity prediction, localisation and explanation with the ability of generalisation for an unseen dataset.

Feasibility study of hybrid inverse planning with transmission beams and single-energy spread-out Bragg peaks for proton FLASH radiotherapy.

Ultra-high dose rate (FLASH) proton planning with only transmission beams (TBs) has limitations in normal tissue sparing. The single-energy spread-out Bragg peaks (SESOBPs) of the FLASH dose rate have been demonstrated feasible for proton FLASH planning. To investigate the feasibility of combining TBs and SESOBPs for proton FLASH treatment. A hybrid inverse optimization method was developed to combine the TBs and SESOBPs (TB-SESOBP) for FLASH planning. The SESOBPs were generated field-by-field from spreading out the BPs by pre-designed general bar ridge filters (RFs) and placed at the central target by range shifters (RSs) to obtain a uniform dose within the target. The SESOBPs and TBs were fully placed field-by-field allowing automatic spot selection and weighting in the optimization process. A spot reduction strategy was conducted in the optimization process to push up the minimum MU/spot assuring the plan deliverability at beam current of 165 nA. The TB-SESOBP plans were validated in comparison with the TB only (TB-only) plans and the plans with the combination of TBs and BPs (TB-BP plans) regarding 3D dose and dose rate (dose-averaged dose rate) distributions for five lung cases. The FLASH dose rate coverage (V40Gy/s ) was evaluated in the structure volume receiving>10% of the prescription dose. Compared to the TB-only plans, the mean spinal cord D1.2cc drastically reduced by 41% (P<0.05), the mean lung V7Gy and V7.4Gy moderately reduced by up to 17% (P<0.05), and the target dose homogeneity slightly increased in the TB-SESOBP plans. Comparable dose homogeneity was achieved in both TB-SESOBP and TB-BP plans. Besides, prominent improvements were achieved in lung sparing for the cases of relatively large targets by the TB-SESOBP plans compared to the TB-BP plans. The targets and the skin were fully covered with the FLASH dose rate in all three plans. For the OARs, V40Gy/s =100% was achieved by the TB-only plans while V40Gy/s >85% was obtained by the other two plans. We have demonstrated that the hybrid TB-SESOBP planning was feasible to achieve FLASH dose rate for proton therapy. With pre-designed general bar RFs, the hybrid TB-SESOBP planning could be implemented for proton adaptive FLASH radiotherapy. As an alternative FLASH planning approach to TB-only planning, the hybrid TB-SESOBP planning has great potential in dosimetrically improving OAR sparing while maintaining high target dose homogeneity.

Monte Carlo based continuous aperture optimization for VMAT on conventional and MR-linacs: A proof of concept.

Currently, the commercial treatment planning systems for magnetic-resonance guided linear accelerators (MR-linacs) only support step-and-shoot intensity-modulated radiation therapy (IMRT). However, recent studies have shown the feasibility of delivering arc therapy on MR-linacs, which is expected to improve dose distributions and delivery speed. By accurately accounting for the electron return effect in the presence of a magnetic field, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of thistechnique. We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT), including applications to VMAT on MR-linacs and trajectory-based VMAT. A unique feature of MCCAO is that the continuous character of gantry rotation and multileaf collimator (MLC) motion is accounted for at every stage of theoptimization. The optimization process uses a multistage simulation of 4D dose distribution. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit (MU) weights are randomly changed, while respecting the linac mechanical limits. Due to the continuous nature of the leaf motion, such changes affect not only a single control point, but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used, which includes the MC statisticaluncertainty. We applied our optimization technique to various treatment sites, using standard and flattening-filter-free (FFF) 6 MV beam models, with and without a 1.5 T magnetic field. MCCAO generates deliverable plans, whose dose distributions are in good agreement with measurements on ArcCHECK and stereotactic radiosurgery End-To-EndPhantom. We show that the novel MCCAO method generates VMAT plans that meet clinical objectives for both conventional andMR-linacs.

Rapid estimation of patient-specific organ doses using a deep learning network.

Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization and patient management. Current dose estimation techniques mainly rely on time-consuming Monte Carlo methods or/and generalized anthropomorphic phantoms. We proposed a proof-of-concept rapid workflow based on deep learning networks to estimate organ doses for individuals following thorax Computed Tomography (CT) examinations METHODS: CT scan data from 95 individuals undergoing thorax CT examinations were used. Monte Carlo simulations were performed and three-dimensional (3D) dose distributions for each patient were obtained. A fully connected sequential deep learning network model was constructed and trained for each organ considered in this study. Water-equivalent diameter (WED), scan length and tube current were the independent variables. Organ doses for heart, lungs, esophagus, and bones were calculated from the Monte Carlo 3D distribution and used to train the deep learning networks. Organ dose predictions from each network were evaluated using an independent data set of 19 patients. The trained networks provided organ dose predictions within a second. There was very good agreement between the deep learning network predictions and reference organ dose values calculated from Monte Carlo simulations. The average difference was -1.5% for heart, -1.6% for esophagus, -1.0% for lungs, and -0.4% for bones in the 95 patients dataset, and -5.1%, 4.3%, 0.9%, and 1.4% respectively in the 19 patients test dataset. The proposed workflow demonstrated that patient specific organ-doses can be estimated in nearly real-time using deep learning networks. The workflow can be readily implemented and requires a small set of representative data for training. This article is protected by copyright. All rights reserved.

Dosimetric evaluation of an intraoperative radiotherapy system: a measurement-based and Monte-Carlo modelling investigation

Intraoperative radiotherapy (IORT) is a specialised subset of radiotherapy, where a high radiation dose is delivered to a surgically exposed tumour bed in order to eradicate any remaining cancer cells. The aim of this study was to examine the dose characteristics of the Zeiss Intrabeam IORT device which provides near-isotropic emission of up to 50 kV X-rays. The EGSnrc Monte Carlo (MC) code system was used to simulate the device and percentage depth dose (PDD) data measured with a soft X-ray parallel-plate ionisation chamber were used for model verification. The model provided energy spectra, isodose curves and mean photon energies. In addition, EBT3 Gafchromic film was used to verify the MC model by examining PDDs and 2D dose distributions for various applicators. The differences between MC model and ionisation chamber measurements were within 3% for most points, with a maximum deviation of ~ 9%. Most of the simulated PDD points were within 5% of the film-measured data, with a maximum deviation of ~ 10%. The mean energy of the bare probe was found to be 21.19 keV. The mean photon energy from applicators ranged from 29.00 to 30.85 keV. Results of this study may be useful for future work on creating a system for treatment planning.

End-to-End QA with Polymer Gel Dosimeter for Photon Beam Radiation Therapy.

With the complexity and high demands on quality assurance (QA) of photon beam radiation therapy, end-to-end (E2E) QA is necessary to validate the entire treatment workflow from pre-treatment imaging to beam delivery. A polymer gel dosimeter is a promising tool for three-dimensional (3D) dose distribution measurement. The purpose of this study is to design a fast "one delivery" polymethyl methacrylate (PMMA) phantom with a polymer gel dosimeter for the E2E QA test of the photon beam. The one delivery phantom is composed of ten calibration cuvettes for the calibration curve measurement, two 10 cm gel dosimeter inserts for the dose distribution measurement, and three 5.5 cm gel dosimeters for the square field measurement. The one delivery phantom holder is comparable in size and shape to that of a human thorax and abdomen. In addition, an anthropomorphic head phantom was employed to measure the patient-specific dose distribution of a VMAT plan. The E2E dosimetry was verified by undertaking the whole RT procedure (immobilization, CT simulation, treatment planning, phantom set-up, imaged-guided registration, and beam delivery). The calibration curve, field size, and patient-specific dose were measured with a polymer gel dosimeter. The positioning error can be mitigated with the one-delivery PMMA phantom holder. The delivered dose measured with a polymer gel dosimeter was compared with the planned dose. The gamma passing rate is 86.64% with the MAGAT-f gel dosimeter. The results ascertain the feasibility of the one delivery phantom with a polymer gel dosimeter for a photon beam in E2E QA. The QA time can be reduced with the designed one delivery phantom.

Noise Considerations for Tomographic Reconstruction of Single-Projection Digital Holographic Interferometry-Based Radiation Dosimetry

Optical Calorimetry (OC) is a 2D Digital Holographic Interferometry (DHI)-based measurement technique with potential applications for the 3D dosimetry of ultra-high dose rate (FLASH) radiation therapy beams through tomographic reconstruction. This application requires accurate measurements of DHI signals in environments with low signal-to-noise ratios (SNRs) in order to accurately measure absorbed energy to a medium per unit mass (Dose). However, tomographic reconstruction accuracy is sensitive to noise in the measurements. In this study, a virtual model of an OC dosimeter was used to characterize and model major sources of noise within a DHI setup, allowing for the modelled noise sources to be selectively reduced. The tomographic reconstruction of the 3D dose distribution was achieved using the inverse Abel transform. Reducing the noise contribution from atmospheric turbulence and mechanical vibration by one half improved the central axis reconstruction error from 6.5% to 1.3% and 1.1%, respectively, and the mean dose difference from 2.9% to 0.4% and 0.3%, respectively. This indicates the potential of the tomographic DHI-based 3D OC dosimeter to reconstruct accurate 3D dose distributions from a single projection if the specified sources of noise can be reduced to acceptable levels. The used methodology is applicable to any application of tomographic DHI where reconstruction quality is highly sensitive to noise.