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.

翻译：心肌灌注成像（MPI）通过单光子发射计算机断层扫描（SPECT）广泛应用于心血管疾病的诊断。降低注射示踪剂剂量对于减少患者辐射暴露至关重要，但会导致图像噪声增加。此外，最新专用心脏SPECT扫描仪通常采用更少的探测器以更少角度采集投影数据，从而降低硬件成本，但可能造成重建精度下降。为应对这些挑战，我们提出一种双重域迭代网络，用于低剂量、少角度心脏SPECT投影的端到端联合去噪与重建。图像域网络为投影域网络提供先验估计，投影域主模块与辅助模块相互连接以实现渐进式去噪和少角度重建。自适应数据一致性（ADC）模块通过高效融合主模块与辅助模块输出，提升预测精度。基于临床MPI数据的实验表明，所提方法优于现有图像域、投影域及双重域技术，能生成更精确的投影和重建结果。消融研究证实了图像域先验估计和ADC模块对增强网络性能的关键作用。