Proton radiography for in vivo range verification in adaptive proton therapy

Proton therapy is a novel radiotherapy technique that exploits the characteristic dose deposition of proton beams through matter to deliver a very localized treatment via radiation. With this technology, the desired radiation dose is given to the tumor, and healthy organs surrounding the tumor can be spared more efficiently than with conventional x-ray radiation therapy. Given that the dose is deposited in a very localized manner, it is important to quantify possible deviations from the treatment plan throughout the whole treatment course (around 30 days). Deviations can occur due to CT imaging inaccuracies, weight loss or gain, changes in the tumor size, or patient position misalignments. In this thesis, proton radiography was investigated as a quality control tool to quantify the existing sources of deviation right before delivering the treatment, and adapt the treatment plan if necessary. Proton radiography is an imaging technique that uses proton radiation to obtain an image of the patient. The advantage of this technique is that the same radiation type is used for treatment and for imaging, providing a direct measurement of the residual proton range. Clinically oriented applications of proton radiography were proposed through simulation and experimental studies, to investigate how proton radiography can detect treatment deviations and trigger plan adaptations. Furthermore, the suitability of two different radiation detectors was investigated for the clinical implementation of proton radiography. The thesis concluded with an analysis of the first proton radiographies acquired for head and neck cancer patients treated with proton therapy

Technical Note:First report on an in vivo range probing quality control procedure for scanned proton beam therapy in head and neck cancer patients

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

Classification of various sources of error in range assessment using proton radiography and neural networks in head and neck cancer patients

This study evaluates the suitability of convolutional neural networks (CNN) 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 26x26cm2 for field A and 4.5x4.5cm2 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 3 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

Range probing as a quality control tool for CBCT-based synthetic CTs:In vivo application for head and neck cancer patients

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 seven 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 toward 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

Optimizing calibration settings for accurate water equivalent path length assessment using flat panel proton radiography

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. 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. Large mean and standard deviations were found in WEPL images obtained with large ΔE values (ΔE= 9 or 7MeV), for any ΔX. WEPL images obtained with ΔE≤ 5MeV and ΔX≤ 5mm resulted in a WEPL accuracy with mean values within ±0.5% and standard deviations around 1%. An optimal FP calibration in the framework of this study was established, characterized by 3MeV≤ ΔE ≤ 5MeV and 2mm ≤ ΔX ≤ 5mm. 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

Technical note:Flat panel proton radiography with a patient specific imaging field for accurate WEPL assessment

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.</p

Assessment of range uncertainty in lung-like tissue using a porcine lung phantom and proton radiography

Thoracic tumours are increasingly considered indications for pencil beam scanned proton therapy (PBS-PT) treatments. Conservative robustness settings have been suggested due to potential range straggling effects caused by the lung micro-structure. Using proton radiography (PR) and a 4D porcine lung phantom, we experimentally assess range errors to be considered in robust treatment planning for thoracic indications. A human-chest-size 4D phantom hosting inflatable porcine lungs and corresponding 4D computed tomography (4DCT) were used. Five PR frames were planned to intersect the phantom at various positions. Integral depth-dose curves (IDDs) per proton spot were measured using a multi-layer ionisation chamber (MLIC). Each PR frame consisted of 81 spots with an assigned energy of 210 MeV (full width at half maximum (FWHM) 8.2 mm). Each frame was delivered five times while simultaneously acquiring the breathing signal of the 4D phantom, using an ANZAI load cell. The synchronised ANZAI and delivery log file information was used to retrospectively sort spots into their corresponding breathing phase. Based on this information, IDDs were simulated by the treatment planning system (TPS) Monte Carlo dose engine on a dose grid of 1 mm. In addition to the time-resolved TPS calculations on the 4DCT phases, IDDs were calculated on the average CT. Measured IDDs were compared with simulated ones, calculating the range error for each individual spot. In total, 2025 proton spots were individually measured and analysed. The range error of a specific spot is reported relative to its water equivalent path length (WEPL). The mean relative range error was 1.2% (1.5 SD 2.3 %) for the comparison with the time-resolved TPS calculations, and 1.0% (1.5 SD 2.2 %) when comparing to TPS calculations on the average CT. The determined mean relative range errors justify the use of 3% range uncertainty for robust treatment planning in a clinical setting for thoracic indications

Assessment of range uncertainty in lung-like tissue using a porcine lung phantom and proton radiography

Thoracic tumours are increasingly considered indications for pencil beam scanned proton therapy (PBS-PT) treatments. Conservative robustness settings have been suggested due to potential range straggling effects caused by the lung micro-structure. Using proton radiography (PR) and a 4D porcine lung phantom, we experimentally assess range errors to be considered in robust treatment planning for thoracic indications. A human-chest-size 4D phantom hosting inflatable porcine lungs and corresponding 4D computed tomography (4DCT) were used. Five PR frames were planned to intersect the phantom at various positions. Integral depth-dose curves (IDDs) per proton spot were measured using a multi-layer ionisation chamber (MLIC). Each PR frame consisted of 81 spots with an assigned energy of 210 MeV (full width at half maximum (FWHM) 8.2 mm). Each frame was delivered five times while simultaneously acquiring the breathing signal of the 4D phantom, using an ANZAI load cell. The synchronised ANZAI and delivery log file information was used to retrospectively sort spots into their corresponding breathing phase. Based on this information, IDDs were simulated by the treatment planning system (TPS) Monte Carlo dose engine on a dose grid of 1 mm. In addition to the time-resolved TPS calculations on the 4DCT phases, IDDs were calculated on the average CT. Measured IDDs were compared with simulated ones, calculating the range error for each individual spot. In total, 2025 proton spots were individually measured and analysed. The range error of a specific spot is reported relative to its water equivalent path length (WEPL). The mean relative range error was 1.2% (1.5 SD 2.3 %) for the comparison with the time-resolved TPS calculations, and 1.0% (1.5 SD 2.2 %) when comparing to TPS calculations on the average CT. The determined mean relative range errors justify the use of 3% range uncertainty for robust treatment planning in a clinical setting for thoracic indications