Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free $\mu$-map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived $\mu$-maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating $\mu$-maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

我们提出了一种用于同时进行低剂量去噪、有限视图重建和无CT的心脏SPECT的μ图生成的多任务学习方法DuDoCFNet，通过联级式融合机制实现跨域和交叉模态特征融合，应用两阶段渐进学习策略来获得SPECT投影和CT导出的μ图的粗到细估计。我们的实验结果表明，在各种迭代和低剂量水平下，DuDoCFNet在估计投影、生成μ图和AC重建方面的准确性优于现有的单一或多任务学习方法。

Dual-Domain Coarse-to-Fine Progressive Estimation Network for
  Simultaneous Denoising, Limited-View Reconstruction, and Attenuation
  Correction of Cardiac SPECT

用于心脏SPECT图像的双域粗到细的逐步估计网络，用于同时去噪，有限视角重建和衰减校正