Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT
  Using a Dual-domain Iterative Network with Adaptive Data Consistency
Xiongchao Chen Bo Zhou Huidong Xie Xueqi Guo Qiong Liu Albert J. Sinusas Chi Liu
Myocardial perfusion imaging (MPI) by single-photon emission computed
tomography (SPECT) is widely applied for the diagnosis of cardiovascular
diseases. Reducing the dose of the injected tracer is essential for lowering
the patient's radiation exposure, but it will lead to increased image noise.
Additionally, the latest dedicated cardiac SPECT scanners typically acquire
projections in fewer angles using fewer detectors to reduce hardware expenses,
potentially resulting in lower reconstruction accuracy. To overcome these
challenges, we propose a dual-domain iterative network for end-to-end joint
denoising and reconstruction from low-dose and few-angle projections of cardiac
SPECT. The image-domain network provides a prior estimate for the
projection-domain networks. The projection-domain primary and auxiliary modules
are interconnected for progressive denoising and few-angle reconstruction.
Adaptive Data Consistency (ADC) modules improve prediction accuracy by
efficiently fusing the outputs of the primary and auxiliary modules.
Experiments using clinical MPI data show that our proposed method outperforms
existing image-, projection-, and dual-domain techniques, producing more
accurate projections and reconstructions. Ablation studies confirm the
significance of the image-domain prior estimate and ADC modules in enhancing
network performance.