Tang, Xueyan

Abstract Objective . Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery. Approach . Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use. Main Results . The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels. Significance . DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.

Abstract Background Proton therapy offers precise tumor targeting while minimizing damage to surrounding healthy tissue, making it especially valuable for treating tumors near critical organs and in pediatric patients. However, its success depends on accurate beam delivery, requiring rigorous quality assurance (QA) to maintain treatment precision and effectiveness. Purpose This study aims to standardize the measurement of key aspects of proton therapy delivery, such as gantry and couch isocentricity, spot position accuracy, and spot size consistency. The goal is to enhance the delivery accuracy of proton therapy across various clinical settings, improving patient outcomes. Methods The QA framework uses the Octopoint phantom, Gafchromic film, and spot position monitor (SPM) log data to evaluate proton beam isocentricity, spot position, and spot size. The Octopoint phantom, made from acrylic, was used with Gafchromic films at various gantry and couch angles to measure isocentricity. MATLAB tools were used to analyze spot positions, and SPM logs provided verification. Sensitivity tests were conducted to assess the system's response to intentional shifts and errors, ensuring alignment with clinical QA standards. Results The Octopoint phantom's stepwise two‐dimensional (2D) fitting process, validated against film‐measured dose profiles, accurately identified beam and ball bearing (BB) centroid locations. Isocentricity tests conducted over 8 months across four gantries demonstrated consistent beam‐to‐BB radial offsets. The phantom showed excellent repeatability, with a maximum standard deviation of 0.1 mm across various couch‐gantry combinations. Sensitivity testing across all axes revealed a strong alignment between intended shifts and measured values. Over the course of a year, film measurements tracked spot position and size consistency, with deviations remaining within acceptable clinical limits. Comparisons with SPM data further confirmed the system's reliability in maintaining beam accuracy across different gantry angles and energy levels. Conclusions This study presents a reliable QA framework for ensuring precision in proton therapy delivery. By combining the Octopoint phantom, Gafchromic film analysis, and SPM log file evaluation, we effectively measured isocentricity, spot position accuracy, and spot size stability. The framework demonstrated adaptability across various clinical QA tasks, enhancing the accuracy and safety of proton therapy treatments.

Abstract Background Conventional proton dose calculation methods are either time‐ and resource‐intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade‐off between computational efficiency and accuracy highlights the need for improved dose calculation approaches in clinical settings. Purpose This study aims to develop a deep‐learning‐based model that calculates dose‐to‐water ( D W ) and dose‐to‐medium ( D M ) using patient anatomy and proton spot map (PSM), achieving approaching MC‐level accuracy with significantly reduced computation time. Additionally, the study seeks to generalize the model to different treatment sites using transfer learning. Methods A SwinUNetr model was developed using 259 four‐field prostate proton stereotactic body radiation therapy (SBRT) plans to calculate patient‐specific D W and D M distributions from CT and projected PSM (PPSM). The PPSM was created by projecting PSM into the CT scans using spot coordinates, stopping power ratio, beam divergence, and water‐equivalent thickness. Fine‐tuning was then performed for the central nervous system (CNS) site using 84 CNS plans. The model's accuracy was evaluated against MC simulation benchmarks using mean absolute error (MAE), gamma analysis (2% local dose difference, 2‐mm distance‐to‐agreement, 10% low dose threshold), and relevant clinical indices on the test dataset. Results The trained model achieved a single‐field dose calculation time of 0.07 s on a Nvidia‐A100 GPU, over 100 times faster than MC simulators. For the prostate site, the best‐performing model showed an average MAE of 0.26 ± 0.17 Gy and a gamma index of 92.2% ± 3.1% in dose regions above 10% of the maximum dose for D W calculations, and an MAE of 0.30 ± 0.19 Gy with a gamma index of 89.7% ± 3.9% for D M calculations. After transfer learning for CNS plans, the model achieved an MAE of 0.49 ± 0.24 Gy and a gamma index of 90.1% ± 2.7% for D W computations, and an MAE of 0.47 ± 0.25 Gy with a gamma index of 85.4% ± 7.1% for D M computations. Conclusions The SwinUNetr model provides an efficient and accurate method for computing dose distributions in proton therapy. It also opens the possibility of reverse‐engineering PSM from D W , potentially speeding up treatment planning while maintaining accuracy.