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

Abstract

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 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 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-to-fine estimations of SPECT projections and CT-derived μ -maps. 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.

Fig. 1.

Illustration of configurations and limited-view arrangements of systems in this study. The original GE NM/CT 570c scanner has 19 detectors in total, with 5, 9, and 5 placed on the top, central, and bottom columns respectively. All the 19 detectors will collect the cardiac SPECT projection information in different spatial angles. By only including the central 9 detectors, we simulated a cost-effective single-column scanner, such as the GE MyoSPECT ES system, as denoted in the blue solid box above.

Fig. 2.

Overview of the Dual-Domain Coarse-To-Fine Progressive Network (DuDoCFNet). In each iteration, DuDoCFNet employs a Two-Stage Progressive Network (TSP-Net) in the projection domain for denoising and restoration of the LD and LV projections, and a Boundary-Aware Network (BDA-Net) in the image domain for predicting $\mu$-maps. All the TSP-Nets and BDA-Nets are cascaded to enable cross-domain and cross-modality feature fusion. The predicted projection and $\mu$-map of the last iteration are employed as the final prediction outputs of DuDoCFNet.

Fig. 3.

Two-Stage Progressive Network (TSP-Net). In Stage 1, a U-Net-like structure is utilized to achieve the LV restoration. The auxiliary anatomical features are fed into multiple downsampling layers as the multi-layer fusion (MLF) mechanism. Cross-Domain Feature Fusion (CDF) modules recalibrate the channel weights for adaptive feature fusion. A non-downsampling module is employed in Stage 2 for the LD denoising.

Fig. 4.

Boundary-Aware Network (BDA-Net). A shared encoder and two task-specific decoders are utilized to estimate a coarse $\mu$-map and its boundary image. Cross-domain features are embedded in multiple downsampling layers as the multi-level fusion. The estimated $\mu$-map and boundary image are jointly fed into a Spatial Boundary Enhancement (SBE) module to enhance the boundary accuracy of the final refined $\mu$-map.

Fig. 5.

The training and validation losses of a single-task learning group (AttenUNet) and a multi-task simultaneous learning group (DuDoCFNet). The left figure shows the losses of the predicted FDFV projections by AttenUNet. The middle and right figures show the losses of the simultaneously predicted FDFV projections and $\mu$-maps by DuDoCFNet. The single-task learning network reaches overfitting and convergence at about 50 epochs. In contrast, the multi-task learning network reaches overfitting and convergence at about 200 epochs.

Fig. 6.

Predicted FD and FV projections displayed in the central-column angle, bottom-column angle, and side view. White arrows denote the regions with over- or under-estimated projection intensities. NMSE and SSIM between the predicted and ground-truth projections are annotated.

Fig. 7.

Predicted $\mu$-maps (unit: cm⁻¹) with error maps. White arrows denote the $\mu$-map regions with inaccurate estimations. DuDoCFNet demonstrates the most accurate boundary estimations. NMSE and SSIM between the predicted and ground-truth $\mu$-maps are annotated.

Fig. 8.

Reconstructed AC SPECT images using predicted projections and $\mu$-maps presented in horizontal long axis (HLA) and short axis (SA) views. White arrows denote the image regions with inaccurate reconstructions. DuDoCFNet outputs the most accurate AC images. NMSE and SSIM between predicted and ground-truth images are annotated.

Fig. 9.

Standard 17-segment polar maps of the AC SPECT images. White arrows denote the segment regions with over- or under-estimated intensities. The polar map by DuDoCFNet is the most consistent with ground truth, compared to single-task, CDI-Net, and ablation study groups.

Fig. 10.

Correlation maps of segment values between polar maps of the ground-truth and estimated AC SPECT images. Correlation Coefficients (Corr. Coef.) and Coefficients of Determination (R²) are annotated.

Fig. 11.

Quantitative evaluations of the predicted projections, $\mu$-maps, and SPECT images by DuDoCFNet with number of iterations ranging from 1 to 8. DuDoCFNet’s performance improves as the number of iterations increases from 1 to 5. The network shows convergence over 5 iterations, and the prediction accuracy gradually decreases due to overfitting.

Fig. 12.

Quantitative evaluations of multiple groups over 8 datasets with low-dose levels varying from 1% to 80% using NMSE and SSIM.

Fig. 13.

Visualizations of the predicted projections and $\mu$-maps by DuDoCFNet using datasets with replicated padding, linear padding, or zero padding. White arrows denote the prediction inconsistency in the predicted projections and $\mu$-maps.

Fig. 14.

Visualizations of DuDoCFNet’s performance on two additional limited-view (LV) angle arrangement. The predicted projections and $\mu$-maps using the Central + Top LV dataset are shown in the top red dash box. The predicted projections and $\mu$-maps using the Central + Bottom LV dataset are shown in the bottom blue dash box. The while arrows denote the inconsistency of the predicted images.