Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency
This work addresses image quality issues in cardiac SPECT for medical diagnosis, but it is incremental as it builds on existing dual-domain approaches with specific enhancements.
The paper tackled the problem of reducing radiation exposure and hardware costs in cardiac SPECT imaging by addressing increased noise and lower reconstruction accuracy from low-dose and few-angle projections, resulting in a method that outperforms existing techniques in producing more accurate projections and reconstructions as shown in experiments with clinical data.
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.