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4D multi-image-based (4DMIB) optimization is a form of robust optimization where different uncertainty scenarios, due to anatomy variations, are considered via multiple image sets (e.g., 4DCT). In this review, we focused on providing an overview of different 4DMIB optimization implementations, introduced various frameworks to evaluate the robustness of scanned particle therapy affected by breathing motion and summarized the existing evidence on the necessity of using 4DMIB optimization clinically. Expected potential benefits of 4DMIB optimization include more robust and/or interplay-effect-resistant doses for the target volume and organs-at-risk for indications affected by anatomical variations (e.g., breathing, peristalsis, etc.). Although considerable literature is available on the research and technical aspects of 4DMIB, clinical studies are rare and often contain methodological limitations, such as, limited patient number, motion amplitude, motion and delivery time structure considerations, number of repeat CTs, etc. Therefore, the data are not conclusive. In addition, multiple studies have found that robust 3D optimized plans result in dose distributions within the set clinical tolerances and, therefore, are suitable for a treatment of moving targets with scanned particle therapy. We, therefore, consider the clinical necessity of 4DMIB optimization, when treating moving targets with scanned particle therapy, as still to be demonstrated., Radiotherapy & Oncology, 169, ISSN:0167-8140, ISSN:1879-0887

Background: Proton radiography (PR) uses highly energetic proton beams to create images where energy loss is the main contrast mechanism. Water-equivalent path length (WEPL) measurements using flat panel PR (FP-PR) have potential for in vivo range verification. However, an accurate WEPL measurement via FP-PR requires irradiation with multiple energy layers, imposing high imaging doses. Purpose: A FP-PR method is proposed for accurate WEPL determination based on a patient-specific imaging field with a reduced number of energies (n) to minimize imaging dose. Methods: Patient-specific FP-PRs were simulated and measured for a head and neck (HN) phantom. An energy selection algorithm estimated spot-wise the lowest energy required to cross the anatomy (Emin) using a water-equivalent thickness map. Starting from Emin, n was restricted to certain values (n = 26, 24, 22, …, 2 for simulations, n = 10 for measurements), resulting in patient-specific FP-PRs. A reference FP-PR with a complete set of energies was compared against patient-specific FP-PRs covering the whole anatomy via mean absolute WEPL differences (MAD), to evaluate the impact of the developed algorithm. WEPL accuracy of patient-specific FP-PRs was assessed using mean relative WEPL errors (MRE) with respect to measured multi-layer ionization chamber PRs (MLIC-PR) in the base of skull, brain, and neck regions. Results: MADs ranged from 2.1 mm (n = 26) to 21.0 mm (n = 2) for simulated FP-PRs, and 7.2 mm for measured FP-PRs (n = 10). WEPL differences below 1 mm were observed across the whole anatomy, except at the phantom surfaces. Measured patient-specific FP-PRs showed good agreement against MLIC-PRs, with MREs of 1.3 ± 2.0%, −0.1 ± 1.0%, and −0.1 ± 0.4% in the three regions of the phantom. Conclusion: A method to obtain accurate WEPL measurements using FP-PR with a reduced number of energies selected for the individual patient anatomy was established in silico and validated experimentally. Patient-specific FP-PRs could provide means of in vivo range verification.

PURPOSE: To ensure target coverage in the treatment of esophageal cancer, a density override to the region of diaphragm motion can be applied in the optimization process. Here, we evaluate the benefit of this approach during robust optimization for intensity modulated proton therapy (IMPT) planning.MATERIALS AND METHODS: For ten esophageal cancer patients, two robustly optimized IMPT plans were created either using (WDO) or not using (NDO) a diaphragm density override of 1.05 g/cm3 during plan optimization. The override was applied to the excursion of the diaphragm between exhale and inhale. Initial robustness evaluation was performed for plan acceptance (setup errors of 8 mm, range errors of ±3%), and subsequently, on all weekly repeated 4DCTs (setup errors of 2 mm, range errors of ±3%). Target coverage and hotspots were analyzed on the resulting voxel-wise minimum (Vwmin ) and voxel-wise maximum (Vwmax ) dose distributions.RESULTS: The nominal dose distributions were similar for both WDO and NDO plans. However, visual inspection of the Vwmax of the WDO plans showed hotspots behind the right diaphragm override region. For one patient, target coverage and hotspots improved by applying the diaphragm override. We found no differences in target coverage in the weekly evaluations between the two approaches.CONCLUSION: The diaphragm override approach did not result in a clinical benefit in terms of planning and interfractional robustness. Therefore, we don't see added value in employing this approach as a default option during robust optimization for IMPT planning in esophageal cancer.

PURPOSE: The capability of proton therapy to provide highly conformal dose distributions is impaired by range uncertainties. The aim of this work is to apply range probing (RP), a form of a proton radiography-based quality control (QC) procedure for range accuracy assessment in head and neck cancer (HNC) patients in a clinical setting.METHODS AND MATERIALS: This study included seven HNC patients. RP acquisition was performed using a multi-layer ionization chamber (MLIC). Per patient, two RP frames were acquired within the first two weeks of treatment, on days when a repeated CT scan was obtained. Per RP frame, integral depth dose (IDD) curves of 81 spots around the treatment isocentre were acquired. Range errors are determined as a discrepancy between calculated IDDs in the treatment planning system and measured residual ranges by the MLIC. Range errors are presented relative to the water equivalent path length of individual proton spots. In addition to reporting results for complete measurement frames, an analysis, excluding range error contributions due to anatomical changes, is presented.RESULTS: Discrepancies between measured and calculated ranges are smaller when performing RP calculations on the day-specific patient anatomy rather than the planning CT. The patient-specific range evaluation shows an agreement between calculated and measured ranges for spots in anatomically consistent areas within 3% (1.5 standard deviation).CONCLUSIONS: The results of a RP-based QC procedure implemented in the clinical practice for HNC patients have been demonstrated. The agreement of measured and simulated proton ranges confirms the 3% uncertainty margin for robust optimization. Anatomical variations show a predominant effect on range accuracy, motivating efforts towards the implementation of adaptive radiotherapy.

The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of moving targets with scanned particle beams, started in 2009 and since then has been organized annually. The mission of the workshop is to create an informal ground for clinical medical physicists, medical physics researchers and medical doctors interested in the development of the 4D technology, protocols and their translation into clinical practice. The 10th and 11th editions of the workshop took place in Sapporo, Japan in 2018 and Krakow, Poland in 2019, respectively. This review report from the Sapporo and Krakow workshops is structured in two parts, according to the workshop programs. The first part comprises clinicians and physicists review of the status of 4D clinical implementations. Corresponding talks were given by speakers from five centers around the world: Maastro Clinic (The Netherlands), University Medical Center Groningen (The Netherlands), MD Anderson Cancer Center (United States), University of Pennsylvania (United States) and The Proton Beam Therapy Center of Hokkaido University Hospital (Japan). The second part is dedicated to novelties in 4D research, i.e. motion modelling, artificial intelligence and new technologies which are currently being investigated in the radiotherapy field.

OBJECTIVE: To establish optimal robust optimization uncertainty settings for clinical head and neck cancer (HNC) patients undergoing 3D image-guided pencil beam scanning (PBS) proton therapy.METHODS: We analyzed ten consecutive HNC patients treated with 70 and 54.25 GyRBE to the primary and prophylactic clinical target volumes (CTV) respectively using intensity-modulated proton therapy (IMPT). Clinical plans were generated using robust optimization with 5 mm/3% setup/range uncertainties (RayStation v6.1). Additional plans were created for 4, 3, 2 and 1 mm setup and 3% range uncertainty and for 3 mm setup and 3%, 2% and 1% range uncertainty. Systematic and random error distributions were determined for setup and range uncertainties based on our quality assurance program. From these, 25 treatment scenarios were sampled for each plan, each consisting of a systematic setup and range error and daily random setup errors. Fraction doses were calculated on the weekly verification CT closest to the date of treatment as this was considered representative of the daily patient anatomy.RESULTS: Plans with a 2 mm/3% setup/range uncertainty setting adequately covered the primary and prophylactic CTV (V95≥ 99% in 98.8% and 90.8% of the treatment scenarios respectively). The average organ-at-risk dose decreased with 1.1 GyRBE/mm setup uncertainty reduction and 0.5 GyRBE/1% range uncertainty reduction. Normal tissue complication probabilities decreased by 2.0%/mm setup uncertainty reduction and by 0.9%/1% range uncertainty reduction.CONCLUSION: The results of this study indicate that margin reduction below 3 mm/3% is possible but requires a larger cohort to substantiate clinical introduction.

Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we compared sCTs based on CBCTs and MRs for head and neck (H&N) cancer patients in terms of image quality and proton dose calculation accuracy. A dataset of 27 H&N-patients, treated with proton therapy (PT), containing planning CTs (pCTs), repeat CTs, CBCTs and MRs were used to train two neural networks to convert either CBCTs or MRs into sCTs. Image quality was quantified by calculating mean absolute error (MAE), mean error (ME) and Dice similarity coefficient (DSC) for bones. The dose evaluation consisted of a systematic non-clinical analysis and a clinical recalculation of actually used proton treatment plans. Gamma analysis was performed for non-clinical and clinical treatment plans. For clinical treatment plans also dose to targets and organs at risk (OARs) and normal tissue complication probabilities (NTCP) were compared. CBCT-based sCTs resulted in higher image quality with an average MAE of 40 ± 4 HU and a DSC of 0.95, while for MR-based sCTs a MAE of 65 ± 4 HU and a DSC of 0.89 was observed. Also in clinical proton dose calculations, sCTCBCT achieved higher average gamma pass ratios (2%/2 mm criteria) than sCTMR (96.1% vs. 93.3%). Dose-volume histograms for selected OARs and NTCP-values showed a very small difference between sCTCBCT and sCTMR and a high agreement with the reference pCT. CBCT- and MR-based sCTs have the potential to enable accurate proton dose calculations valuable for daily adaptive PT. Significant image quality differences were observed but did not affect proton dose calculation accuracy in a similar manner. Especially the recalculation of clinical treatment plans showed high agreement with the pCT for both sCTCBCT and sCTMR.

Purpose: For locally advanced-stage non-small cell lung cancer (NSCLC), inter-fraction target motion variations during the whole time span of a fractionated treatment course are assessed in a large and representative patient cohort. The primary objective is to develop a suitable motion monitoring strategy for pencil beam scanning proton therapy (PBS-PT) treatments of NSCLC patients during free breathing. Methods: Weekly 4D computed tomography (4DCT; 41 patients) and daily 4D cone beam computed tomography (4DCBCT; 10 of 41 patients) scans were analyzed for a fully fractionated treatment course. Gross tumor volumes (GTVs) were contoured and the 3D displacement vectors of the centroid positions were compared for all scans. Furthermore, motion amplitude variations in different lung segments were statistically analyzed. The dosimetric impact of target motion variations and target motion assessment was investigated in exemplary patient cases. Results: The median observed centroid motion was 3.4 mm (range: 0.2–12.4 mm) with an average variation of 2.2 mm (range: 0.1–8.8 mm). Ten of 32 patients (31.3%) with an initial motion 2 mm, 4DCBCT should be considered for motion monitoring due to substantial motion variations observed.

For radiation therapy, it is crucial to ensure that the delivered dose matches the planned dose. Errors in the dose calculations done in the treatment planning system (TPS), treatment delivery errors, other software bugs or data corruption during transfer might lead to significant differences between predicted and delivered doses. As such, patient specific quality assurance (QA) of dose distributions, through experimental validation of individual fields, is necessary. These measurement based approaches, however, are performed with 2D detectors, with limited resolution and in a water phantom. Moreover, they are work intensive and often impose a bottleneck to treatment efficiency. In this work, we investigated the potential to replace measurement-based approach with a simulation-based patient specific QA using a Monte Carlo (MC) code as independent dose calculation engine in combination with treatment log files. Our developed QA platform is composed of a web interface, servers and computation scripts, and is capable to autonomously launch simulations, identify and report dosimetric inconsistencies. To validate the beam model of independent MC engine, in-water simulations of mono-energetic layers and 30 SOBP-type dose distributions were performed. Average Gamma passing ratio 99 ± 0.5% for criteria 2%/2 mm was observed. To demonstrate feasibility of the proposed approach, 10 clinical cases such as head and neck, intracranial indications and craniospinal axis, were retrospectively evaluated via the QA platform. The results obtained via QA platform were compared to QA results obtained by measurement-based approach. This comparison demonstrated consistency between the methods, while the proposed approach significantly reduced in-room time required for QA procedures.

Background Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. Purpose In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. Methods A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. Results 4D-sCTs resulted in average MAEs of 48.1 +/- 6.5 HU (single phase) and 37.7 +/- 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% +/- 3.2% (single phase) and 94.4% +/- 2.1% (average). The clinical target volume showed high agreement in D-98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. Conclusion In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

4D multi-image-based (4D

Objective: Proton range uncertainties can compromise the effectiveness of proton therapy treatments. Water equivalent path length (WEPL) assessment by flat panel detector proton radiography (FP-PR) can provide means of range uncertainty detection. Since WEPL accuracy intrinsically relies on the FP-PR calibration parameters, the purpose of this study is to establish an optimal calibration procedure that ensures high accuracy of WEPL measurements. To that end, several calibration settings were investigated. Approach: FP-PR calibration datasets were obtained simulating PR fields with different proton energies, directed towards water-equivalent material slabs of increasing thickness. The parameters investigated were the spacing between energy layers (ΔE) and the increment in thickness of the water-equivalent material slabs (ΔX) used for calibration. 30 calibrations were simulated, as a result of combining ΔE = 9, 7, 5, 3, 1 MeV and ΔX = 10, 8, 5, 3, 2, 1 mm. FP-PRs through a CIRS electron density phantom were simulated, and WEPL images corresponding to each calibration were obtained. Ground truth WEPL values were provided by range probing multi-layer ionization chamber simulations on each insert of the phantom. Relative WEPL errors between FP-PR simulations and ground truth were calculated for each insert. Mean relative WEPL errors and standard deviations across all inserts were computed for WEPL images obtained with each calibration. Main results: Large mean and standard deviations were found in WEPL images obtained with large ΔE values (ΔE = 9 or 7 MeV), for any ΔX. WEPL images obtained with ΔE ≤ 5 MeV and ΔX ≤ 5 mm resulted in a WEPL accuracy with mean values within ±0.5% and standard deviations around 1%. Significance: An optimal FP calibration in the framework of this study was established, characterized by 3 MeV ≤ ΔE ≤ 5 MeV and 2 mm ≤ ΔX ≤ 5 mm. Within these boundaries, highly accurate WEPL acquisitions using FP-PR are feasible and practical, holding the potential to assist future online range verification quality control procedures.

PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.METHODS: A dataset of 33 thoracic cancer patients, containing CBCTs, same-day repeat CTs (rCT), planning-CTs (pCTs) and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT-based correction method. Mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU-accuracy of sCTs in terms of range errors.RESULTS: On average, sCTs without correction resulted in a MAE of 34±6 HU and ME of 4±8 HU. The correction reduced the MAE to 31±4HU (ME to 2±4HU). Average 3%/3mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ±0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (CONCLUSION: CBCT-based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters. This article is protected by copyright. All rights reserved.

PURPOSE: Cone beam CT (CBCT) based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range probing (RP) based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.METHODS: A RP QC dataset consisting of repeat CTs (rCT), CBCTs and RP acquisitions of 7 head and neck cancer patients was retrospectively assessed. CBCT based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images.RESULTS: Mean relative range errors showed agreement between measured and simulated RP fields, ranging from -1.2% to 1.5% in rCTs, and from -0.7% to 2.7% in sCTs.CONCLUSIONS: The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer towards clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

Radiation therapy (RT) is an integral component of potentially curative management of esophageal cancer (EC). However, RT can cause significant acute and late morbidity due to excess radiation exposure to nearby critical organs, especially the heart and lungs. Sparing these organs from both low and high radiation dose has been demonstrated to achieve clinically meaningful reductions in toxicity and may improve long-term survival. Accruing dosimetry and clinical evidence support the consideration of proton beam therapy (PBT) for the management of EC. There are critical treatment planning and delivery uncertainties that should be considered when treating EC with PBT, especially as there may be substantial motion-related interplay effects. The Particle Therapy Co-operative Group Thoracic and Gastrointestinal Subcommittees jointly developed guidelines regarding patient selection, treatment planning, clinical trials, and future directions of PBT for EC.

This study evaluates the suitability of convolutional neural networks (CNNs) to automatically process proton radiography (PR)-based images. CNNs are used to classify PR images impaired by several sources of error affecting the proton range, more precisely setup and calibration curve errors. PR simulations were performed in 40 head and neck cancer patients, at three different anatomical locations (fields A, B and C, centered for head and neck, neck and base of skull coverage). Field sizes were 26 × 26cm2 for field A and 4.5 × 4.5cm2 for fields B and C. Range shift maps were obtained by comparing an unperturbed reference PR against a PR where one or more sources of error affected the proton range. CT calibration curve errors in soft, bone and fat tissues and setup errors in the anterior–posterior and inferior–superior directions were simulated individually and in combination. A CNN was trained for each type of PR field, leading to three CNNs trained with a mixture of range shift maps arising from one or more sources of range error. To test the full/partial/wrong agreement between predicted and actual sources of range error in the range shift maps, exact, partial and wrong match percentages were computed for an independent test dataset containing range shift maps arising from isolated or combined errors, retrospectively. The CNN corresponding to field A showed superior capability to detect isolated and combined errors, with exact matches of 92% and 71% respectively. Field B showed exact matches of 80% and 54%, and field C resulted in exact matches of 77% and 41%. The suitability of CNNs to classify PR-based images containing different sources of error affecting the proton range was demonstrated. This procedure enables the detection of setup and calibration curve errors when they appear individually or in combination, providing valuable information for the interpretation of PR images.

Purpose Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy. Methods Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60 Gy was generated on the average intensity image of the 4D-CT for an approximated internal target volume (ITV). Dose distributions were then recalculated on individual phases of the 4D-CT and the 4D-vCT based on the optimized plan. Dose accumulation was performed for 4D-vCT and 4D-CT using DIR of each phase to the mid position, which was chosen as reference. Dose based on the 4D-vCT was then evaluated against the dose calculated on 4D-CT both, phase-by-phase as well as accumulated, by comparing dose volume histogram (DVH) values (Dmean, D2%, D98%, D95%) for the ITV, and by a 3D-gamma index analysis (global, 3%/3 mm, 5 Gy, 20 Gy and 30 Gy dose thresholds). Results Good agreement was found between the 4D-CT and 4D-vCT-based ITV-DVH curves. The relative differences ((CT-vCT)/CT) between accumulated values of ITV Dmean, D2%, D95% and D98% for the 4D-CT and 4D-vCT-based dose distributions were −0.2%, 0.0%, −0.1% and −0.1%, respectively. Phase specific values varied between −0.5% and 0.2%, −0.2% and 0.5%, −3.5% and 1.5%, and −5.7% and 2.3%. The relative difference of accumulated Dmean over the lungs was 2.3% and Dmean for the phases varied between −5.4% and 5.8%. The gamma pass-rates with 5 Gy, 20 Gy and 30 Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%. Conclusions Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation.

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of −8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods.

PURPOSE: Patient specific quality assurance (PSQA) is required to verify the treatment delivery and the dose calculation by the treatment planning system (TPS). The objective of this work is to demonstrate the feasibility to substitute resource consuming measurement based PSQA (PSQAM) by independent dose recalculations (PSQAIDC), and that PSQAIDC results may be interpreted in a clinically relevant manner using normal tissue complication probability (NTCP) and tumor control probability (TCP) models.METHODS AND MATERIALS: A platform for the automatic execution of the two following PSQAIDC workflows was implemented: (i) using the TPS generated plan and (ii) using treatment delivery log files (log-plan). 30 head and neck cancer (HNC) patients were retrospectively investigated. PSQAM results were compared with those from the two PSQAIDC workflows. TCP / NTCP variations between PSQAIDC and the initial TPS dose distributions were investigated. Additionally, for two example patients that showed low passing PSQAM results, eight error scenarios were simulated and verified via measurements and log-plan based calculations. For all error scenarios ΔTCP / NTCP values between the nominal and the log-plan dose were assessed.RESULTS: Results of PSQAM and PSQAIDC from both implemented workflows agree within 2.7% in terms of gamma pass ratios. The verification of simulated error scenarios shows comparable trends between PSQAM and PSQAIDC. Based on the 30 investigated HNC patients, PSQAIDC observed dose deviations translate into a minor variation in NTCP values. As expected, TCP is critically related to observed dose deviations.CONCLUSIONS: We demonstrated a feasibility to substitute PSQAM with PSQAIDC. In addition, we showed that PSQAIDC results can be interpreted in clinically more relevant manner, for instance using TCP / NTCP.

PURPOSE: Pencil beam scanned proton therapy (PBS-PT) treatment quality might be compromised by interplay and motion effects. Via fraction-wise reconstruction of 4D dose distributions and dose accumulation, we assess the clinical relevance of motion related target dose degradation in thoracic cancer patients.METHODS AND MATERIALS: For the ten thoracic patients (Hodgkin lymphoma and non-small cell lung cancer) treated at our proton therapy facility, daily breathing pattern records, treatment delivery log-files and weekly repeated 4DCTs were collected. Patients exhibited point-max target motion of up to 20 mm. They received robustly optimized treatment plans, delivered with five-times rescanning in fractionated regimen. Treatment delivery records were used to reconstruct 4D dose distributions and the accumulated treatment course dose per patient. Fraction-wise target dose degradations were analyzed and the accumulated treatment course dose, representing an estimation of the delivered dose, was compared with the prescribed dose.RESULTS: No clinically relevant loss of target dose homogeneity was found in the fraction-wise reconstructed 4D dose distributions. Overall, in 97% of all reconstructed fraction doses, D98 remained within 5% from the prescription dose. The V95 of accumulated treatment course doses was higher than 99.7% for all ten patients.CONCLUSIONS: 4D dose reconstruction and accumulation enables the clinical estimation of actual exhibited interplay and motion effects. In the patients considered here, the loss of homogeneity caused by interplay and organ motion did not show systematic pattern and smeared out throughout the course of fractionated PBS-PT treatment. Dose degradation due to anatomical changes showed to be more severe and triggered treatment adaptations for five patients.

Purpose Radiation therapy for mesothelioma remains challenging, as normal tissue toxicity limits the amount of radiation that can be safely delivered to the pleural surfaces, especially radiation dose to the contralateral lung. The physical properties of proton therapy result in better sparing of normal tissues when treating the pleura, both in the postpneumonectomy setting and the lung-intact setting. Compared with photon radiation, there are dramatic reductions in dose to the contralateral lung, heart, liver, kidneys, and stomach. However, the tissue heterogeneity in the thorax, organ motion, and potential for changing anatomy during the treatment course all present challenges to optimal irradiation with protons. Methods The clinical data underlying proton therapy in mesothelioma are reviewed here, including indications, advantages, and limitations. Results The Particle Therapy Cooperative Group Thoracic Subcommittee task group provides specific guidelines for the use of proton therapy for mesothelioma. Conclusions This consensus report can be used to guide clinical practice, insurance approval, and future research.

Purpose Compared to volumetric modulated arc therapy (VMAT), clinical benefits are anticipated when treating thoracic tumours with intensity-modulated proton therapy (IMPT). However, the current concern of plan robustness as a result of motion hampers its wide clinical implementation. To define an optimal protocol to treat lung and oesophageal cancers, we present a comprehensive evaluation of IMPT planning strategies, based on patient 4DCTs and machine log files. Materials and methods For ten lung and ten oesophageal cancer patients, a planning 4DCT and weekly repeated 4DCTs were collected. For these twenty patients, the CTV volume and motion were assessed based on the 4DCTs. In addition to clinical VMAT plans, layered rescanned 3D and 4D robust optimised IMPT plans (IMPT_3D and IMPT_4D respectively) were generated, and approved clinically, for all patients. The IMPT plans were then delivered in dry runs at our proton facility to obtain log files, and subsequently evaluated through our 4D robustness evaluation method (4DREM). With this method, for each evaluated plan, fourteen 4D accumulated scenario doses were obtained, representing 14 possible fractionated treatment courses. Results From VMAT to IMPT_3D, nominal Dmean(lungs-GTV) decreased 2.75 ± 0.56 GyRBE and 3.76 ± 0.92 GyRBE over all lung and oesophageal cancer patients, respectively. A more pronounced reduction was verified for Dmean(heart): 5.38 ± 7.36 GyRBE (lung cases) and 9.51 ± 2.25 GyRBE (oesophagus cases). Target coverage robustness of IMPT_3D was sufficient for 18/20 patients. Averaged dose in critical structures over all 4DREM scenarios changed only slightly for both IMPT_3D and IMPT_4D. Relative to IMPT_3D, no gain in IMPT_4D was observed. Conclusion The dosimetric superiority of IMPT over VMAT has been established. For most thoracic tumours, our IMPT_3D planning protocol showed to be robust and clinically suitable. Nevertheless, accurate patient positioning and adapting to anatomical variations over the course of treatment remain compulsory.

Proton therapy is affected by range uncertainty, which is partly caused by an ambiguous conversion from x-ray attenuation to proton stopping power. CT calibration curves, or Hounsfield look-up tables (HLUTs), are institution-specific and may be a source of systematic errors in treatment planning. A range probing method to verify, optimize and validate HLUTs for proton treatment is proposed. An initial HLUT was determined according to the stoichiometric approach. For HLUT validation, three types of animal tissue phantoms were prepared: a pig's head, 'thorax' and femur. CT scans of the phantoms were taken and a structure, simulating a water slab, was added on the scan distal to the phantoms to mimic the detector used for integral depth-dose measurements. The CT scans were imported into the TPS to calculate individual pencil beams directed through the phantoms. The phantoms were positioned at the therapy system isocenter using x-ray imaging. Shoot-through pencil beams were delivered, and depth-dose profiles were measured using a multi-layer ionization chamber. Measured depth-dose curves were compared to the calculated curves and the range error per spot was determined. Based on the water equivalent path length (WEPL) of individual spot, a range error margin was defined. Ratios between measured error and theoretical margin were calculated per spot. The HLUT optimization was performed by identifying systematic shifts of the mean range error per phantom and minimizing the ratios between range errors and uncertainty margins. After optimization, the ratios of the actual range error and the uncertainty margin over the complete data set did not exceed 0.75 (1.5 SD), indicating that the actual errors are covered by the theoretical uncertainty recipe. The feasibility of using range probing to assess range errors was demonstrated. The theoretical uncertainty margins in the institution-specific setting potentially may be reduced by ∼25%.

Purpose The treatment of moving targets with scanned proton beams is challenging. For motion mitigation, an Active Breathing Coordinator (ABC) can be used to assist breath-holding. The delivery of pencil beam scanning fields often exceeds feasible breath-hold durations, requiring high breath-hold reproducibility. We evaluated the robustness of scanned proton therapy against anatomical uncertainties when treating nonsmall-cell lung cancer (NSCLC) patients during ABC controlled breath-hold. Methods Four subsequent MRIs of five healthy volunteers (3 male, 2 female, age: 25-58, BMI: 19-29) were acquired under ABC controlled breath-hold during two simulated treatment fractions, providing both intrafractional and interfractional information about breath-hold reproducibility. Deformation vector fields between these MRIs were used to deform CTs of five NSCLC patients. Per patient, four or five cases with different tumor locations were modeled, simulating a total of 23 NSCLC patients. Robustly optimized (3 and 5 mm setup uncertainty respectively and 3% density perturbation) intensity-modulated proton plans (IMPT) were created and split into subplans of 20 s duration (assumed breath-hold duration). A fully fractionated treatment was recalculated on the deformed CTs. For each treatment fraction the deformed CTs representing multiple breath-hold geometries were alternated to simulate repeated ABC breath-holding during irradiation. Also a worst-case scenario was simulated by recalculating the complete treatment plan on the deformed CT scan showing the largest deviation with the first deformed CT scan, introducing a systematic error. Both the fractionated breath-hold scenario and worst-case scenario were dosimetrically evaluated. Results Looking at the deformation vector fields between the MRIs of the volunteers, up to 8 mm median intra- and interfraction displacements (without outliers) were found for all lung segments. The dosimetric evaluation showed a median difference in D-98% between the planned and breath-hold scenarios of -0.1 Gy (range: -4.1 Gy to 2.0 Gy). D-98% target coverage was more than 57.0 Gy for 22/23 cases. The D-1 cc of the CTV increased for 21/23 simulations, with a median difference of 0.9 Gy (range: -0.3 to 4.6 Gy). For 14/23 simulations the increment was beyond the allowed maximum dose of 63.0 Gy, though remained under 66.0 Gy (110% of the prescribed dose of 60.0 Gy). Organs at risk doses differed little compared to the planned doses (difference in mean doses

Pencil-beam scanning (PBS) proton therapy (PT), particularly intensity modulated PT, represents the latest advanced PT technology for treating cancers, including thoracic malignancies. On the basis of virtual clinical studies, PBS-PT appears to have great potential in its ability to tightly tailor the dose to the target while sparing critical structures, thereby reducing treatment-related toxicities, particularly for tumors in areas with complicated anatomy. However, implementing PBS-PT for moving targets has several additional technical challenges compared with intensity modulated photon radiation therapy or passive scattering PT. Four-dimensional computed tomographye based motion management and robust optimization and evaluation are crucial for minimizing uncertainties associated with beam range and organ motion. Rigorous quality assurance is required to validate dose delivery both before and during the course of treatment. Active motion management (eg, breath hold), beam gating, rescanning, tracking, or adaptive planning may be needed for cases involving significant motion or changes in motion or anatomy over the course of treatment. (C) 2017 The Authors. Published by Elsevier Inc.

The relative biological effectiveness (RBE) of protons is highly variable and difficult to quantify. However, RBE is related to the local ionization density, which can be related to the physical measurable dose weighted linear energy transfer (LETD). The aim of this study was to validate the LETD calculations for proton therapy beams implemented in a commercially available treatment planning system (TPS) using microdosimetry measurements and independent LETD calculations (Open-MCsquare (MCS)). The TPS (RayStation v6R) was used to generate treatment plans on the CIRS-731-HN anthropomorphic phantom for three anatomical sites (brain, nasopharynx, neck) for a spherical target ( = 5 cm) with uniform target dose to calculate the LETD distribution. Measurements were performed at the University Medical Center Groningen proton therapy center (Proteus Plus, IBA) using a µ +-probe utilizing silicon on insulator microdosimeters capable of detecting lineal energies as low as 0.15 keV µm-1 in tissue. Dose averaged mean lineal energy depth-profiles were measured for 70 and 130 MeV spots in water and for the three treatment plans in water and an anthropomorphic phantom. The measurements were compared to the LETD calculated in the TPS and MCS independent dose calculation engine. D • was compared to D • LETD in terms of a gamma-index with a distance-to-agreement criteria of 2 mm and increasing dose difference criteria to determine the criteria for which a 90% pass rate was accomplished. Measurements of D • were in good agreement with the D • LETD calculated in the TPS and MCS. The 90% passing rate threshold was reached at different D • LETD difference criteria for single spots (TPS: 1% MCS: 1%), treatment plans in water (TPS: 3% MCS: 6%) and treatment plans in an anthropomorphic phantom (TPS: 6% MCS: 1%). We conclude that D • LETD calculations accuracy in the RayStation TPS and open MCSquare are within 6%, and sufficient for clinical D • LETD evaluation and optimization. These findings remove an important obstacle in the road towards clinical implementation of D • LETD evaluation and optimization of proton therapy treatment plans. Novelty and significance The dose weighed linear energy transfer (LETD) distribution can be calculated for proton therapy treatment plans by Monte Carlo dose engines. The relative biological effectiveness (RBE) of protons is known to vary with the LETD distribution. Therefore, there exists a need for accurate calculation of clinical LETD distributions. Previous LETD validations have focused on general purpose Monte Carlo dose engines which are typically not used clinically. We present the first validation of mean lineal energy measurements of the LETD against calculations by the Monte Carlo dose engines of the Raystation treatment planning system and open MCSquare.

Purpose: The introduction of advanced treatment techniques in proton therapy, such as intensity-modulated proton therapy, leads to an increased need for patient-specific quality assurance, especially an accurate treatment plan verification becomes inevitable. In this study, signal theoretical analysis of dose distributions in scanned proton therapy is performed to investigate the feasibility and limits of two-dimensional (2D) detector arrays for treatment plan verification. Methods: 2D detector arrays are characterized by two main aspects: the distance between the single detectors on the array or the sampling frequency; and the lateral response functions of a single detector. The analysis is based on single spots, reference fields and on measured and calculated dose distributions of typical intensity-modulated proton therapy treatment plans with and without range shifter. Measurements were performed with Gafchromic EBT3 films (Ashland Speciality Ingredients G.P., Bridgewater, NJ, USA), the MatriXX PT detector array (IBA Dosimetry, Schwarzenbruck, Germany) and the OCTAVIUS detector array 1500XDR (PTW-Freiburg, Germany) at an IBA Proteus PLUS proton therapy system (Ion Beam Applications, Louvain-la-Neuve, Belgium). Dose calculations were performed with the treatment planning system RayStation 6 or 8 (RaySearch Laboratories, Sweden). Results: The Fourier analysis of the data of the treatment planning system and film measurements show maximum frequencies of 0.06/mm for the plan with range shifter and 0.083/mm for the plan without range shifter. According to the Nyquist theorem, this corresponds to minimum required sampling distances of 8.3 and 6 mm, respectively. By comparison, the sampling distances of the arrays of 7.6 mm (MatriXX PT) and 7.1 mm (OD1500XDR) are sufficient to reconstruct the dose distributions adequately from measurements if range shifters are used, whereas some fields of the plans without range shifter violated the Nyquist requirement. The lateral dose response functions of the single detectors within the arrays have clearly higher frequencies than the treatment plans and thus the volume effect only slightly influences the measurements. Consequently, the array measurements show high gamma passing rates with at least 96 % and a good agreement between the investigated line profiles. Conclusion: The results indicate that the detector dimensions and sampling distances of the arrays are in most studied cases adequate not to substantially influence the measurement process when they are used for analyzing typical intensity-modulated proton therapy treatment plans. Nevertheless, clinical conditions have been identified, for instance treatment plans without range shifter, under which the Nyquist theorem is violated such that a full representation of the dose distributions with the measurements is not feasible. In these cases, analysis of measurements is limited to pointwise comparisons.

Purpose: To introduce a fast ray-tracing algorithm in pencil proton radiography (PR) with a multilayer ionization chamber (MLIC) for in vivo range error mapping. Methods: Pencil beam PR was obtained by delivering spots uniformly positioned in a square (45x45 mm(2) field-of-view) of 9x9 spots capable of crossing the phantoms (210 MeV). The exit beam was collected by a MLIC to sample the integral depth dose (IDDMLIC). PRs of an electron-density and of a head phantom were acquired by moving the couch to obtain multiple 45x45 mm2 frames. To map the corresponding range errors, the two-dimensional set of IDDMLIC was compared with (i) the integral depth dose computed by the treatment planning system (TPS) by both analytic (IDDTPS) and Monte Carlo (IDDMC) algorithms in a volume of water simulating the MLIC at the CT, and (ii) the integral depth dose directly computed by a simple ray-tracing algorithm (IDDdirect) through the same CT data. The exact spatial position of the spot pattern was numerically adjusted testing different in-plane positions and selecting the one that minimized the range differences between IDDdirect and IDDMLIC. Results: Range error mapping was feasible by both the TPS and the ray-tracing methods, but very sensitive to even small misalignments. In homogeneous regions, the range errors computed by the direct ray-tracing algorithm matched the results obtained by both the analytic and the Monte Carlo algorithms. In both phantoms, lateral heterogeneities were better modeled by the ray-tracing and the Monte Carlo algorithms than by the analytic TPS computation. Accordingly, when the pencil beam crossed lateral heterogeneities, the range errors mapped by the direct algorithm matched better the Monte Carlo maps than those obtained by the analytic algorithm. Finally, the simplicity of the ray-tracing algorithm allowed to implement a prototype procedure for automated spatial alignment. Conclusions: The ray-tracing algorithm can reliably replace the TPS method in MLIC PR for in vivo range verification and it can be a key component to develop software tools for spatial alignment and correction of CT calibration. (C) 2016 American Association of Physicists in Medicine.

Due to anticipated clinical benefits, moving targets are potential future indications for pencil beam scanned proton therapy (PBS-PT). However, currently they are not widely treated at PBS-PT facilities due to dosimetric uncertainties caused by motion. We developed a method, the 4D robustness evaluation method (4DREM), to realistically and efficiently assess all possible events impacting PBS-PT treatments in the thorax. Using the 4DREM in large cohorts of lung and oesophageal cancer patients, it will become possible to illustrate, in clinical practice, how to trigger robustness settings for plan optimisation and to select and apply motion mitigation techniques. (C) 2019 Elsevier B.V. All rights reserved. Radiotherapy and Oncology

Background and purpose Motion-induced uncertainties hamper the clinical implementation of pencil beam scanning proton therapy (PBS-PT). Prospective pretreatment evaluations only provide multiscenario predictions without giving a clear conclusion for the actual treatment. Therefore, in this proof-of-concept study we present a methodology for a fraction-wise retrospective four-dimensional (4D) dose reconstruction and accumulation aiming at the evaluation of treatment quality during and after treatment. Material and methods We implemented an easy-to-use, script-based 4D dose assessment of PBS-PT for patients with moving tumors in a commercially available treatment planning system. This 4D dose accumulation uses treatment delivery log files and breathing pattern records of each fraction as well as weekly repeated 4D-CT scans acquired during the treatment course. The approach was validated experimentally and was executed for an exemplary dataset of a lung cancer patient. Results The script-based 4D dose reconstruction and accumulation was implemented successfully, requiring minimal user input and a reasonable processing time (around 10 min for a fraction dose assessment). An experimental validation using a dynamic CIRS thorax phantom confirmed the precision of the 4D dose reconstruction methodology. In a proof-of-concept study, the accumulation of 33 reconstructed fraction doses showed a linear increase of D98 values. Projected treatment course D98 values revealed a CTV underdosage after fraction 25. This loss of target coverage was confirmed in a dose volume histogram comparison of the nominal, the projected (after 16 fractions) and the accumulated (after 33 fractions) dose distribution. Conclusions The presented method allows for the assessment of the conformity between planned and delivered dose as the treatment course progresses. The implemented approach considers the influence of changing patient anatomy and variations in the breathing pattern. This facilitates treatment quality evaluation and supports decisions regarding plan adaptation. In a next step, this approach will be applied to a larger patient cohort to investigate its capability as 4D quality control and decision support tool for treatment adaptation. (c) 2019 American Association of Physicists in Medicine