Cross-Domain Iterative Network for Simultaneous Denoising, Limited-Angle Reconstruction, and Attenuation Correction of Cardiac SPECT

Chen, Xiongchao; Zhou, Bo; Xie, Huidong; Guo, Xueqi; Liu, Qiong; Sinusas, Albert J.; Liu, Chi

doi:10.1007/978-3-031-45673-2_2

Cited by 1 publication

(3 citation statements)

References 19 publications

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“…The multi-energy-window image generation approach using AttenUNet with squeeze-excitation attention mechanism [62], was the previous state-of-the-art method for µ-map generation in dedicated cardiac SPECT [27]. Moreover, the multi-task learning method, CDI-Net [57], was applied to simultaneously estimate P F DF V and µ. CDI-Net is set to run for 5 iterations as in [57]. Ablation studies of DuDoCFNet were also conducted for further evaluation.…”

Section: F Implementation Details and Ablation Studiesmentioning

confidence: 99%

“…Furthermore, to assess the effect of the progressive learning strategy, Stage 2 of TSP-Net or BDA-Net in DuDoCFNet is removed to produce DuDoCFNet w/o TSP and DuDoCFNet w/o BDA. Moreover, the MLF mechanism is replaced by input-channel concatenations as in [57] to produce DuDoCFNet w/o MLF.…”

Section: F Implementation Details and Ablation Studiesmentioning

confidence: 99%

“…Chen et al [56] further developed a dual-branch squeeze-fusionexcitation (DuSFE) co-attention module for cross-modality image fusion and registration of nuclear medicine image and CT. Thus, a recent study proposed a cross-domain and cross-modality network (CDI-Net) for multi-task learning [57]. However, the simple channel concatenation in CDI-Net might not effectively fuse the cross-domain or cross-modality image features.…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

Zhou,

Guo

et al. 2024

IEEE Trans. Med. Imaging

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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 (µ-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 crossmodality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (Du-DoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free µ-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-tofine estimations of SPECT projections and CT-derived µmaps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating µ-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.

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