Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network

Jingzhang Sun; Bang-Hung Yang; Chien-Ying Li; Yu Du; Yi-Hwa Liu; Tung-Hsin Wu; Greta S P Mok

doi:10.3389/fmed.2023.1083413

Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network

Front Med (Lausanne). 2023 Feb 3:10:1083413. doi: 10.3389/fmed.2023.1083413. eCollection 2023.

Authors

Jingzhang Sun¹, Bang-Hung Yang^{2

3}, Chien-Ying Li^{2

3}, Yu Du¹, Yi-Hwa Liu⁴, Tung-Hsin Wu², Greta S P Mok^{1

5

6}

Affiliations

¹ Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China.
² Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
³ Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei City, Taiwan.
⁴ Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States.
⁵ Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macao SAR, China.
⁶ Ministry of Education Frontiers Science Center for Precision Oncology, Faculty of Health Science, University of Macau, Taipa, Macao SAR, China.

Abstract

Purpose: Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images.

Methods: Fifty patients who underwent 1184 MBq ^99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed.

Results: All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet.

Conclusion: Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.

Keywords: attention-guided; deep learning; denoising; fast SPECT; myocardial perfusion.

Grants and funding

This work was supported by the Excellent Young Scientists Fund (Hong Kong and Macau, 81922080), Natural Science Foundation of China and Ministry of Science and Technology, Taiwan (MOST 109-2314-B-010-023-MY3).