Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle
  Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT
Xiongchao Chen Bo Zhou Huidong Xie Xueqi Guo Qiong Liu Albert J. Sinusas Chi Liu
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 it will cause extra radiation exposure and SPECT-CT
misalignments. In addition, the majority of SPECT scanners in the market are
not hybrid SPECT/CT scanners. 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 emission and anatomical information across
domains and iterations. Adaptive Weight Recalibrators (AWR) adjust the
multi-channel input features to enhance prediction accuracy. Our experiments
using clinical data showed that CDI-Net produced more accurate $\mu$-maps,
projections, and 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.