DuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT. Issue 1 (28th September 2022)

• Record Type: Journal Article
• Authors: Chen, Xiongchao; Zhou, Bo; Xie, Huidong; Miao, Tianshun; Liu, Hui; Holler, Wolfgang; Lin, MingDe; Miller, Edward J.; Carson, Richard E.; Sinusas, Albert J.; Liu, Chi
• Is Part Of: Medical physics. Volume 50:Issue 1(2023)
• Page Start: 89
• Page End: 103
• Publication Date: 2022-09-28
• Subjects: cardiac SPECT -- deep learning -- myocardial perfusion imaging -- synthetic projections; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15958
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Myocardial perfusion imaging (MPI) using single‐photon emission‐computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In clinical practice, the long scanning procedures and acquisition time might induce patient anxiety and discomfort, motion artifacts, and misalignments between SPECT and computed tomography (CT). Reducing the number of projection angles provides a solution that results in a shorter scanning time. However, fewer projection angles might cause lower reconstruction accuracy, higher noise level, and reconstruction artifacts due to reduced angular sampling. We developed a deep‐learning‐based approach for high‐quality SPECT image reconstruction using sparsely sampled projections. Methods: We proposed a novel deep‐learning‐based dual‐domain sinogram synthesis (DuDoSS) method to recover full‐view projections from sparsely sampled projections of cardiac SPECT. DuDoSS utilized the SPECT images predicted in the image domain as guidance to generate synthetic full‐view projections in the sinogram domain. The synthetic projections were then reconstructed into non‐attenuation‐corrected and attenuation‐corrected (AC) SPECT images for voxel‐wise and segment‐wise quantitative evaluations in terms of normalized mean square error (NMSE) and absolute percent error (APE). Previous deep‐learning‐based approaches, including direct sinogram generation (Direct Sino2Sino) and direct image prediction (Direct Img2Img), were tested in this study for comparison. The dataset used in this study included a total of 500 anonymized clinical stress‐state MPI studies acquired on a GE NM/CT 850 scanner with 60 projection angles following the injection of 99m Tc‐tetrofosmin. Results: Our proposed DuDoSS generated more consistent synthetic projections and SPECT images with the ground truth than other approaches. The average voxel‐wise NMSE between the synthetic projections by DuDoSS and the ground‐truth full‐view projections was 2.08% ± 0.81%, as compared to 2.21% ± 0.86% ( p < 0.001) by Direct Sino2Sino. The averaged voxel‐wise NMSE between the AC SPECT images by DuDoSS and the ground‐truth AC SPECT images was 1.63% ± 0.72%, as compared to 1.84% ± 0.79% ( p < 0.001) by Direct Sino2Sino and 1.90% ± 0.66% ( p < 0.001) by Direct Img2Img. The averaged segment‐wise APE between the AC SPECT images by DuDoSS and the ground‐truth AC SPECT images was 3.87% ± 3.23%, as compared to 3.95% ± 3.21% ( p = 0.023) by Direct Img2Img and 4.46% ± 3.58% ( p < 0.001) by Direct Sino2Sino. Conclusions: Our proposed DuDoSS is feasible to generate accurate synthetic full‐view projections from sparsely sampled projections for cardiac SPECT. The synthetic projections and reconstructed SPECT images generated from DuDoSS are more consistent with the ground‐truth full‐view projections and SPECT images than other approaches. DuDoSS can potentially enable fast data acquisition of cardiac SPECT.