ComptoNet: a Compton-map guided deep learning framework for multi-scatter estimation in multi-source stationary CT

Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantryless scan architecture and capability of simultaneous multi-source emission. However, the lack of anti-scatter grid deployment in MSS-CT leads to severe forward and cross scatter contamination, necessitating accurate and efficient scatter correction. In this work, we propose ComptoNet, an innovative decoupled deep learning framework that integrates Compton-scattering physics with deep learning for scatter estimation in MSS-CT. The core innovation lies in the Compton-map, a representation of large-angle Compton scatter signals outside the scan field of view. ComptoNet employs a dual-network architecture: a conditional encoder-decoder network guided by reference Compton-maps and spare detector data for cross scatter estimation, and a frequency U-Net with attention mechanisms for forward scatter correction. Experiments on Monte Carlo-simulated data demonstrate ComptoNet's superior performance, achieving a mean absolute percentage error of 0.84% on scatter estimation. After correction, CT images show nearly artifact-free quality for all test phantoms, validating ComptoNet's robustness in mitigating scatter-induced errors across diverse photon counts and phantoms comparing with other methods.