Improving dual-panel in-beam PET imaging for proton therapy monitoring using 3D U-Net.

Abstract

Background: In-beam positron emission tomography (PET) integrates dedicated detectors into proton therapy systems, enabling real-time acquisition of proton-induced positron-emitting activity. By pre-delivering a subset of single-energy proton spots as probes, in-beam PET has the potential to support online proton range verification and may inform subsequent spot delivery. However, the limited acquisition time and low yields of positron emitters result in noisy reconstructed images, and the open geometry of dual-panel PET further introduces stretching artifacts.

Purpose: To address these issues, this study proposed a 3D U-Net-based post-processing method to enhance in-beam PET image quality and evaluated its feasibility through Monte Carlo simulations and preliminary experimental validation.

Methods: Thirteen head computed tomography (CT) phantoms were irradiated with horizontally and vertically incident single-energy probes, representing two scenarios in which beams passed through relatively homogeneous and heterogeneous tissues. A phantom-level split was used, with seven CT phantoms for training, two for validation, and four held-out CT phantoms for testing. Proton probes in 3×3, 5×5, 7×7, and 9×9 spot patterns were simulated, with each spot delivering 2×10⁷ protons. In-beam PET acquisition was performed for 30 s. Unfiltered reconstructed PET images were used as network inputs, and ground-truth activity distributions served as labels. Gaussian-filtered PET images served as the baseline for performance evaluation. Image quality was quantified by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), and proton range verification accuracy was evaluated by absolute range error (ARE). Additional simulations with CT density perturbations were conducted to evaluate sensitivity to proton range shifts. Preliminary experimental validation was performed using an all-digital PET prototype and polymethyl methacrylate (PMMA) phantoms.

Results: For horizontal beams, median PSNR values increased from 21.4-23.9 to 35.2-37.4, median SSIM values increased from 0.450-0.621 to 0.906-0.961, and median ARE values decreased from 1.05-1.18 mm to 0.52-0.73 mm. For vertical beams, median PSNR values improved from 21.9-23.2 to 29.2-34.1, median SSIM values improved from 0.480-0.560 to 0.745-0.895, and median ARE values decreased from 1.80-2.40 mm to 1.50-1.73 mm. The proposed method also demonstrated improved sensitivity to simulated range shifts and consistent improvements on experimental PET data.

Conclusion: These findings demonstrate the preliminary feasibility of using 3D U-Net-based post-processing to enhance dual-panel in-beam PET image quality and improve PET-based proton range verification.