Dual-view scout scans with deep learning for ultra-low dose attenuation correction in PET

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.

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.

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.

(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.

By leveraging routinely acquired scout scans, this approach enables accurate quantitative PET reconstruction without a full CT, substantially reducing radiation dose and misalignment artifacts.