Year: 2026
Type: article
Abstract: Portable low-field MRI offers an affordable and mobile alternative to conventional high-field scanners, enabling imaging in point-of-care and resource-limited settings. However, its lower signal-to-noise ratio, reduced resolution, and acquisition artifacts raise concerns about the accuracy of standard image registration methods. Reliable registration is critical for a wide range of emerging applications, including frequent brain monitoring, assessment of neurodegenerative disease progression, and evaluation of treatment effects such as those of Alzheimer's therapeutics. In this work, we systematically evaluated state-of-the-art registration approaches on simulated low-field scans (obtained by downsampling high-field images) and on real low-field brain MRI data. We compared three representative approaches: classical optimization (NiftyReg), learning-based registration (SynthMorph), and synthesis-based registration (SynthSR+NiftyReg). Using downsampled high-field scans, all methods performed well, achieving high Dice scores and smooth deformation fields, indicating that reduced resolution alone does not hinder registration. In contrast, real low-field data exhibited lower accuracy, primarily due to geometric distortion and other acquisition-specific artifacts. Among the tested approaches, the synthesis-based pipeline achieved the most robust performance across subjects and modalities. Overall, existing algorithms can accommodate resolution limitations, however, future methods could further enhance coregistration by explicitly addressing the distortions present in low-field MRI scans.
Source: Scientific Reports
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering
Awards: G1554142629, G1650819308, G1880935189, Acquisition-independent machine learning for morphometric analysis of underrepresented aging populations with clinical and low-field brain MRI, G2100670906, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, G3216574877, G3553156159, G3752451908, G3870774570, G4119534169, G4475667458, G4501223628, G4800177743, G5209990037, G528601746, G5324093878, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, Alzheimer's Disease Neuroimaging Initiative, G5740349702, G5850682332, G6173816828, G6371410416, G6447063303, Measuring Brain Health Using Low-Field Portable MRI, G6719828067, G6792506723, G6899163935, G72415811, G764699806, G781725486, G8228018387, G8281378126, G839208290, G8895907669, G8957659042, G9000039018, G9000122335, G9000209268, Functionally guided adult whole brain cell atlas in human and NHP, Prospective Identification of Hypoxic Ischemic Brain Injury Using Portable Magnetic Resonance Imaging, Portable, Low Field Brain Magnetic Resonance Imaging (MRI) for Acute Stroke
OpenAlex ID: W7171855754