Dosimetric accuracy of a cross-calibration coefficient for plane-parallel ionization chamber obtained in low-energy electron beams using various cylindrical dosimeters

Introduction: The accuracy of the cross-calibration procedure depends on ionization chamber type, both used as reference one and under consideration. Also, the beam energy and phantom medium could influence the precision of cross calibration coefficient, resulting in a systematic error in dose estimation and thus could influence the linac beam output checking. This will result in a systematic mismatch between dose calculated in treatment planning system and delivered to the patient. Material and methods: The usage of FC65-G, CC13 and CC01 thimble reference chambers as well as 6, 9, and 15 MeV electron beams has been analyzed. A plane-parallel PPC05 chamber was calibrated since scarce literature data are available for this dosimeter type. The influence of measurement medium and an effective point of measurement (EPOM) on obtained results are also presented. Results: Dose reconstruction precision of ~0.1% for PPC05 chamber could be obtained when cross-calibration is based on a thimble CC13 chamber. Nd,w,Qcross obtained in beam ≥ 9MeV gives 0.1 – 0.5% precision of dose reconstruction. Without beam quality correction, 15 MeV Nd,w,Qcross is 10% lower than Co-60 Nd,w,0. Various EPOM shifts resulted in up to 0.6% discrepancies in Nd,w,Qcross values. Conclusions: Ionization chamber with small active volume and tissue-equivalent materials supplies more accurate cross-calibration coefficients in the range of 6 – 15 MeV electron beams. In the case of 6 and 9 MeV beams, the exact position of an effective point of measurement is of minor importance. In-water cross-calibration coefficient can be used in a solid medium without loss of dose accuracy.

Deep learning-based 3D in vivo dose reconstruction with EPID for MR-LINACs: A proof of concept study

Objective: To develop a novel deep learning-based 3D in vivo dose reconstruction framework with electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs). Approach: The proposed method directly back-projected 2D portal dose into 3D patient coarse dose, which bypassed the complicated patient-to-EPID scatter estimation step used in conventional methods. A pre-trained convolutional neural network (CNN) was then employed to map the coarse dose to the final accurate dose. The electron return effect caused by the magnetic field was captured with the CNN model. Patient dose and portal dose datasets were synchronously generated with Monte Carlo simulation for 96 patients (78 cases for training and validation and 18 cases for testing) treated with fixed-beam intensity-modulated radiotherapy in four different tumor sites, including the brain, nasopharynx, lung, and rectum. Beam angles from the training dataset were further rotated 2–3 times, and doses were recalculated to augment the datasets. Results: The comparison between reconstructed doses and MC ground truth doses showed mean absolute errors < 0.88% for all tumor sites. The averaged 3D γ-passing rates (3%, 2 mm) were 97.42%±2.66% (brain), 98.53%±0.95% (nasopharynx), 99.41%±0.46% (lung), and 98.63%±1.01% (rectum). The dose volume histograms and indices also showed good consistency. The average dose reconstruction time, including back projection and CNN dose mapping, was less than 3 s for each individual beam. Significance: The proposed method can be potentially used for accurate and fast 3D dosimetric verification for online adaptive radiotherapy using MR-LINACs.

Investigation of Autosegmentation Techniques on T2-Weighted MRI for Off-line Dose Reconstruction in MR-Linac Adapt to Position Workflow for Head and Neck Cancers

Purpose: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. In this study, our goal is to evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. 20 autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior (IPP)) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance, Hausdorff distance, and Jaccard index. For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions (IPP_RF_4), IPP with 1 fraction (IPP_1)), and one low-performing (PAL with STAPLE and 5 atlases (PAL_ST_5)). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. Results: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 seconds per case) and PAL methods the slowest (3.7 - 13.8 minutes per case). Execution time increased with number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.