Portable, Low-Field Magnetic Resonance Imaging Sensitively Detects and Accurately Quantifies Multiple Sclerosis Lesions release_cgibdbeqrjhvfgrq5cwocz5rny

by Thomas Arnold, Danni Tu, SERHAT OKAR, Govind Nair, Samantha By, Karan Kawatra, Timothy E. Robert-Fitzgerald, Lisa M. Desiderio, Matthew Schindler, Russell Shinohara, Daniel S. Reich, Joel Stein

Released as a post by Cold Spring Harbor Laboratory.

2022  

Abstract

Magnetic resonance imaging is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low–field MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations. However, the sensitivity of low–field MRI for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a 64mT low–field MRI, compare automated lesion segmentations and total lesion burden between paired 3T and 64mT scans, and identify features that contribute to lesion detection accuracy. In this prospective, cross-sectional study, same–day brain MRI (FLAIR, T1, and T2) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using 3T (Siemens) and 64mT (Hyperfine) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T. Automated lesion burden estimates were highly correlated between 3T and 64mT scans (r = 0.89, p < 0.001). Bland-Altman analysis identified bias in 64mT segmentations (mean = 1.6 ml, standard error = 5.2 ml, limits of agreement = 19.0–15.9 ml), which over-estimated low lesion burden and under-estimated high burden (r = 0.74, p < 0.001). Visual inspection revealed over–segmentation was driven by flow–related hyperintensities in veins on 64mT FLAIR. Lesion size drove segmentation accuracy, with 93% of lesions >1.0 ml and all lesions >1.5 ml being detected. These results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and disease burden estimates are consistent with 3T scans.
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Date   2022-03-13
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