Dual‐view scout scans with deep learning for ultra‐low dose attenuation correction in PET
Abstract Background Accurate attenuation correction (AC) is essential for quantitative positron emission tomography (PET). Conventional CT‐based AC provides reliable attenuation (μ‐) maps but adds radiation, introduces PET/CT misalignment artifacts, and is unavailable on stand‐alone PET systems. Existing deep learning (DL) methods use non‐attenuation corrected (NAC) PET data to predict CT‐like or directly AC‐PET images, but their dependence on emission characteristics limits generalizability across tracers and anatomical coverage, especially for long axial field‐of‐view PET. Purpose We propose incorporating tissue‐density information from dual‐view scout radiographs as supplementary input to NAC PET, acquired with a clinically significant radiation reduction relative to standard low‐dose CT. Methods Two DL methods were evaluated for generating transmission (Tr) images as surrogate μ‐maps for PET AC: (1) (NAC)‐to‐(Tr), using coronal and sagittal NAC PET slices, and (2) (NAC + Scout)‐to‐(Tr), combining NAC PET with anterior‐posterior and lateral scout views. 53 research scans across four tracers from the PennPET Explorer were used for training and testing, with three additional tracers included for testing. Results (NAC + Scout)‐to‐(Tr) improved quantitative accuracy, reducing NRMSE% to < 10% (versus up to 18% for (NAC)‐to‐(Tr)) and SUV biases to within ‐10%, with significant reductions in brain, liver, and muscle compared to (NAC)‐to‐(Tr). Out‐of‐distribution evaluation confirmed generalizability, maintaining SUV biases below 5%‐8% versus 10%‐13% for (NAC)‐to‐(Tr). In a longitudinal biodistribution study, the scout‐guided model showed consistent performance across repeat scans. Conclusions By leveraging routinely acquired scout scans, this approach enables accurate quantitative PET reconstruction without a full CT, substantially reducing radiation dose and misalignment artifacts.