Cross-Domain Iterative Network for Simultaneous Denoising, Limited-Angle Reconstruction, and Attenuation Correction of Cardiac SPECT

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of ischemic heart diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-angle (LA) SPECT enables faster scanning and reduced hardware costs but results in lower reconstruction accuracy. Additionally, computed tomography (CT)-derived attenuation maps (mu-maps) are commonly used for SPECT attenuation correction (AC), but this will cause extra radiation exposure and SPECT-CT mis-alignments. Although various deep learning methods have been introduced to separately address these limitations, the solution for simultaneously addressing these challenges still remains highly under-explored and challenging. To this end, we propose a Cross-domain Iterative Network (CDI-Net) for simultaneous denoising, LA reconstruction, and CT-free AC in cardiac SPECT. In CDI-Net, paired projection- and image-domain networks are end-to-end connected to fuse the cross-domain emission and anatomical information in multiple iterations. Adaptive Weight Recalibrators (AWR) adjust the multi-channel input features to further enhance prediction accuracy. Our experiments using clinical data showed that CDI-Net produced more accurate mu-maps, projections, and AC reconstructions compared to existing approaches that addressed each task separately. Ablation studies demonstrated the significance of cross-domain and cross-iteration connections, as well as AWR, in improving the reconstruction performance. The source code of this work is released at https://github.com/XiongchaoChen/CDI-Net.