Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.

Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; however, these devices produce lower-quality images than conventional high-field-strength scanners. Purpose To develop and evaluate a deep learning architecture to generate high-field-strength quality brain images from low-field-strength inputs using paired data from patients with multiple sclerosis (MS) who underwent MRI at 64 mT and 3 T. Materials and Methods Adults with MS at two institutions were scanned using portable 64-mT and standard 3-T scanners, with T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) acquisitions as part of an observational study (October 2020 to January 2022); a second validation group (January 2023 to January 2024) was also included. Using paired data, a generative adversarial network architecture for low- to high-field-strength image translation, called LowGAN, was developed. Synthetic images were evaluated with respect to image quality (eg, structural similarity index), brain morphometry, and white matter lesions. Nonparametric Wilcoxon tests were used for comparison of image quality and morphometry, and Dice scores were used for comparison of lesion segmentations. Results A total of 50 participants (median age, 47 years [IQR, 38-56 years]; 38 female) were included in the main group, and 13 participants were included in the validation group (median age, 41 years [IQR 35-53 years]; 11 female). Compared with low-field-strength input images, LowGAN synthetic high-field-strength images were visually higher in quality and showed higher structural similarity index relative to actual high-field-strength images for T1-weighted (0.87 vs 0.82;