First deep learning framework for enhanced positron annihilation interaction‐transmission imaging system precision

BACKGROUND: Positron annihilation interaction-transmission imaging (PAITI) is a novel low-dose, multiparameter anatomical imaging modality that utilizes positron annihilation photons to generate multiple 2D maps. PAITI produces secondary maps-interaction, deposited energy, attenuation, and electron density-from PAITI's basic maps via a stepwise analytical method, which currently yields an average relative error of 4.32%. PURPOSE: To improve precision for clinical applications (e.g., ion therapy treatment planning), we introduce DeepPAITI-the first deep learning approach in PAITI-to extract secondary maps with enhanced accuracy. METHODS: We developed a specialized deep learning architecture with a multibranch, multi-input, and multi-output framework. Extensive training and testing were performed on both numerical simulations and GATE Monte Carlo datasets. A sensitivity analysis was conducted, and DeepPAITI was compared against common models such as ResNet, UNet, and VGG-16. RESULTS: DeepPAITI achieved mean relative errors (MREs) of 0.63 ± 0.08% for numerical simulations and 0.91 ± 0.20% for GATE-based tests, reflecting a 77% improvement over analytical methods. Under very low-dose conditions (< 1 µGy), electron density decomposition confidence increased from 63% to 99% for key materials (water, carbon, bone, and titanium). Additionally, DeepPAITI demonstrated at least an 83% improvement in MRE compared with conventional deep learning models. CONCLUSIONS: DeepPAITI significantly surpasses traditional methods in extracting PAITI secondary maps, paving the way for more accurate clinical implementations in various field such as radiation and ion therapy treatment planning.