DiffCAS: Inference-time CT-free diffusion model for physics-aware multi-slice attenuation correction in cardiac SPECT

Abstract

Attenuation artifacts remain a critical challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often degrading diagnostic accuracy and clinical interpretability. While hybrid SPECT and Computed Tomography (CT) systems mitigate these artifacts using CT-derived attenuation maps, their high cost, radiation exposure, and limited accessibility restrict widespread clinical use. To address these challenges, we propose DiffCAS, an inference-time CT-free diffusion model for physics-aware multi-slice attenuation correction in cardiac SPECT. DiffCAS integrates a Brownian Bridge diffusion process with physics-guided supervision, enabling the generation of attenuation-corrected (AC) images directly from non-attenuation-corrected (NAC) inputs. Specifically, a physics-aware reconstruction module predicts voxel-wise attenuation coefficients and path lengths, then combines them via the Beer-Lambert law into an attenuation correction factor applied at each diffusion step, keeping the AC images physically consistent. The model introduces two key innovations that jointly enhance structural understanding and physics consistency. The first is multi-slice contextual learning, which captures cross-slice anatomical dependencies and improves spatial coherence in reconstructed images. The second is the 3D Computed Tomography Vision Transformer that models long-range volumetric structures and provides physics-consistent attenuation priors to guide the diffusion process. To enable CT-free attenuation correction, DiffCAS introduces the teacher-student distillation framework that transfers physics-informed knowledge from CT-conditioned training to a student network that requires no CT input at inference time, ensuring stability and interpretability. Evaluations on the CardiAC dataset, which comprises 424 patient studies with paired NAC and AC, and CT-based attenuation maps, demonstrate the strong performance of DiffCAS, evaluated using global pixel-level metrics and myocardium-specific clinical metrics. The proposed method surpasses state-of-the-art image generative methods, achieving superior reconstruction accuracy, structural consistency, and diagnostic reliability. These results highlight the proposed DiffCAS as a clinically promising, inference-time CT-free solution for attenuation correction in cardiac SPECT imaging.