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.

翻译：单光子发射计算机断层扫描（SPECT）广泛用于缺血性心脏病的诊断。低剂量（LD）SPECT旨在最小化辐射暴露，但会导致图像噪声增加。有限角度（LA）SPECT可实现更快的扫描并降低硬件成本，但会降低重建精度。此外，基于计算机断层扫描（CT）的衰减图（$\mu$-图）通常用于SPECT衰减校正（AC），但这会带来额外的辐射暴露和SPECT-CT配准误差。而且，市场上大多数SPECT扫描仪并非混合SPECT/CT设备。尽管已有多种深度学习方法分别解决这些局限性，但同时应对这些挑战的方案仍未被充分探索且极具挑战性。为此，我们提出了一种跨域迭代网络（CDI-Net），用于心脏SPECT中的同时去噪、LA重建及无CT衰减校正。在CDI-Net中，配对投影域和图像域网络通过端到端连接，跨域和跨迭代融合发射与解剖信息。自适应权重校准器（AWR）调整多通道输入特征以提升预测精度。基于临床数据的实验表明，与分别处理各任务的现有方法相比，CDI-Net能生成更精确的$\mu$-图、投影及重建图像。消融研究证实了跨域与跨迭代连接以及AWR在提升重建性能中的重要性。