Dual-Domain Coarse-to-Fine Progressive Estimation Network for
  Simultaneous Denoising, Limited-View Reconstruction, and Attenuation
  Correction of Cardiac SPECT
Xiongchao Chen Bo Zhou Xueqi Guo Huidong Xie Qiong Liu James S. Duncan Albert J. Sinusas Chi Liu
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.