Photo Credit: Umnat Seebuaphan
Study results support the use of portable MRI in creating quality imaging for use in assessing patients with MS.
Joel Stein, MD, PhD, presented “Portable MRI for Multiple Sclerosis” during ACTRIMS Forum 2024, held February 29 to March 2 in West Palm Beach, FL. For a study published in medRxiv—but not necessarily part of Dr. Stein’s presentation—he and his colleagues “developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of” patients with MS scanned at portable low-field strength (64mT) and 3T, according to what they wrote in the paper.
A total of 49 patients with MS underwent scanning with T1-weighted, T2-weighted, and FLAIR acquisitions. A generative adversarial network (GAN) architecture called LowGAN was developed for low- to high-field image translation using this paired data. The synthesized images were then evaluated for image quality, brain morphometry, and white matter lesions.
Synthetic high-field images exhibited visually superior quality compared with low-field inputs and had significantly higher normalized cross-correlation (NCC) to actual high-field images for T1 (P=0.001) and FLAIR (P<0.001) contrasts. LowGAN generally outperformed the current state-of-the-art for low-field volumetrics, with outputs showing no significant differences from 3T measurements in thalamic, lateral ventricle, and total cortical volumes.
The synthetic outputs accurately preserved MS lesions and captured a known inverse relationship between total lesion volume and thalamic volume. LowGAN successfully generates synthetic high-field images with comparable visual and quantitative quality to actual high-field scans, wrote Dr. Stein and colleagues in their paper, adding that improving portable MRI image quality could enhance clinical utility and increase confidence among clinicians, potentially facilitating broader adoption of this technology.
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