Mutually-orthogonal-plane-based Optical-flow-regulated Diffusion Model for Stationary Head CT Reconstruction

Stationary head CT (sHCT) plays a critical role in detecting intracranial lesions. However, its fixed gantry restricts projection angles and undermines reconstruction accuracy. Recent studies have shown that score-based generative models (SGMs) can restore high-quality images from limited projections in two-dimensional (2D) limited-angle CT. Extending SGMs to three-dimensional (3D) sHCT is still challenging, as achieving high-resolution images without incurring excessive computational cost remains an open problem. To address this challenge, this study proposes a mutually orthogonal plane-based optical-flow-regulated (MOPO) diffusion model that effectively balances sampling efficiency and reconstruction accuracy. Specifically, we establish a 3D artifact-aware framework that incorporates a cross-plane attention mechanism to explicitly model and mitigate inter-slice artifact propagation. Furthermore, we design a mutually orthogonal plane-based (MOP) accelerated sampler capable of reducing the sampling process to as few as 10–20 steps. This approach achieves up to a 160× speedup while maintaining high reconstruction accuracy. To ensure global consistency, an anisotropic optical flow regularization is introduced, constructing non-rigid displacement fields between adjacent slices for structural coherence. Experimental results demonstrate that the MOPO-based framework outperforms existing methods in artifact suppression, detail preservation, and computational efficiency, providing a novel solution for high-fidelity and accelerated sHCT reconstruction.