Abstract

Background

Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.

Methods

Fifty anonymized patients who underwent routine 99mTc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.

Results

AttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.

Conclusions

AttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.

Details

Title
Deep learning-based multi-frequency denoising for myocardial perfusion SPECT
Author
Du, Yu 1 ; Sun, Jingzhang 2 ; Li, Chien-Ying 3 ; Yang, Bang-Hung 3 ; Wu, Tung-Hsin 4 ; Mok, Greta S. P. 1   VIAFID ORCID Logo 

1  University of Macau, Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, Taipa, China (GRID:grid.437123.0) (ISNI:0000 0004 1794 8068); University of Macau, Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, Taipa, China (GRID:grid.437123.0) (ISNI:0000 0004 1794 8068) 
2  University of Macau, Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, Taipa, China (GRID:grid.437123.0) (ISNI:0000 0004 1794 8068); Hainan University, School of Cyberspace Security, Haikou, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302) 
3  National Yang Ming Chiao Tung University, Department of Biomedical Imaging and Radiological Sciences, Taipei, Taiwan (GRID:grid.260539.b) (ISNI:0000 0001 2059 7017); Taipei Veterans General Hospital, Department of Nuclear Medicine, Taipei, Taiwan (GRID:grid.278247.c) (ISNI:0000 0004 0604 5314) 
4  National Yang Ming Chiao Tung University, Department of Biomedical Imaging and Radiological Sciences, Taipei, Taiwan (GRID:grid.260539.b) (ISNI:0000 0001 2059 7017) 
Pages
80
Publication year
2024
Publication date
Dec 2024
Publisher
Springer Nature B.V.
e-ISSN
21977364
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s40658-024-00680-w
ProQuest document ID
3112267590
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.