4-D Cone-Beam CT Reconstruction via Diffusion Model and Motion Compensation

Abstract

4-Dcone-beam computed tomography (4-D CBCT) has recently been recognized as a proficient technique in mitigating motion artifacts attributed to respiratory organ movement. The primary challenges in 4-D CBCT reconstruction encompass the precision in projection grouping, the efficacy in reconstructing from sparsely sampled data, and the accuracy in deformation field estimation. To surmount these challenges, we propose an innovative approach that integrates meticulous respiratory curve extraction for projection grouping and utilizes a diffusion model network with motion compensation (MoCo) techniques targeted at significantly enhancing image quality. An object detection network is employed to ascertain the exact position of the diaphragm, which is then normalized to formulate the respiratory curve. Further, we employ a U-Net architecture-based diffusion model, which integrates attention mechanisms to enhance sparse-view reconstruction and reduce artifacts through Guided-Diffusion. Deviating from conventional optical flow methods, our approach introduces an unsupervised registration network for deformation vector field (DVF) in phase-enhanced images. This DVF is then utilized in a motion-compensated, ordered-subset, simultaneous algebraic reconstruction technique, culminating in the generation of 4-D CBCT images. The efficacy of this method has been substantiated through validation on both simulated and clinical datasets, with the results from comparative experiments indicating promising outcomes.