Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images.

Autor: Islam KT; Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia. KhTohidul.Islam@monash.edu., Zhong S; Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.; Australian National Imaging Facility, Brisbane, QLD, Australia., Zakavi P; Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia., Chen Z; Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.; Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia., Kavnoudias H; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.; Department of Radiology, Alfred Hospital, Melbourne, VIC, Australia., Farquharson S; Australian National Imaging Facility, Brisbane, QLD, Australia., Durbridge G; Herston Imaging Research Facility, University of Queensland, Brisbane, QLD, Australia., Barth M; School of Information Technology and Electrical Engineering and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia., McMahon KL; School of Clinical Science, Herston Imaging Research Facility, Queensland University of Technology, Brisbane, QLD, Australia., Parizel PM; David Hartley Chair of Radiology, Department of Radiology, Royal Perth Hospital, Perth, WA, Australia.; Medical School, University of Western Australia, Perth, WA, Australia., Dwyer A; South Australian Health and Medical Research Institute, Adelaide, SA, Australia., Egan GF; Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia., Law M; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.; Department of Radiology, Alfred Hospital, Melbourne, VIC, Australia., Chen Z; Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.; Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Dec 01; Vol. 13 (1), pp. 21183. Date of Electronic Publication: 2023 Dec 01.
DOI: 10.1038/s41598-023-48438-1
Abstrakt: Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.
(© 2023. The Author(s).)
Databáze: MEDLINE
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