Motion-Informed 3D Deep Learning Reconstruction in Patients with Cognitive Impairment.
Authors
Affiliations (1)
Affiliations (1)
- From the Athinoula A. Martinos Center for Biomedical Imaging (S.F., N.G., S.B., C.-H.C., J.C., S.Y.H.) and Department of Radiology (S.F., N.G., S.B., C.-H.C., J.C., S.Y.H.), Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA; Siemens Healthineers AG (D.P., D.N., D.N.S.), Forchheim, Germany; Siemens Shenzhen Magnetic Resonance Ltd. (Y.H.), Shenzhen, China; Department of Medical Imaging (C.-H.C.), Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; Department of Radiology (C.-H.C.), School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Siemens Medical Solutions (W.-C.L., B.C., S.F.C.), Boston, Massachusetts, USA; Harvard-MIT Division of Health Sciences and Technology (S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Abstract
Motion artifacts remain a key limitation in brain MRI, particularly during 3D acquisitions in cognitively impaired patients. Most deep learning (DL) reconstruction techniques improve signal-to-noise ratio but lack explicit mechanisms to correct for motion. This study aims to validate a DL reconstruction method that integrates retrospective motion correction into the reconstruction pipeline for 3D T1-weighted brain MRI. This prospective, intra-individual comparison study included a controlled-motion cohort of healthy volunteers and a clinical cohort of patients undergoing evaluation for memory loss. Each cohort was scanned at distinct imaging sites between October 2022 and August 2023 in staggered periods. All participants underwent 4-fold under-sampled 3D magnetization-prepared rapid gradient-echo imaging with integrated Scout Accelerated Motion Estimation and Reduction (SAMER) acquisition. Image volumes were reconstructed using standard-of-care methods and the proposed DL approach. Quantitative morphometric accuracy was assessed by comparing brain segmentation results of instructed-motion scans to motion-free reference scans in the healthy volunteers. Image quality was rated by two board-certified neuroradiologists using a five-point Likert scale. Statistical analysis included Wilcoxon tests and intraclass correlation coefficients. A total of 41 participants (15 women [37%]; mean age, 58 years) and 154 image volumes were evaluated. The DL-based method with integrated motion correction significantly reduced segmentation error under moderate and severe motion (12.4% to 3.5% and 44.2% to 12.5%, respectively; P < .001). Visual ratings showed improved scores across all criteria compared with standard reconstructions (overall image quality, 4.26 ± 0.72 vs. 3.59 ± 0.82; P < .001). In 47% of cases, motion artifact severity was improved following DL-based processing. Inter-reader agreement ranged from moderate to substantial. Motion-informed DL reconstruction improved both morphometric accuracy and perceived image quality in 3D T1-weighted brain MRI. This technique may enhance diagnostic utility and reduce scan failure rates in motion-prone patients with cognitive impairment. AD = Alzheimer's disease; DL = deep learning; ICC = intra-class correlation coefficient; SAMER = scout accelerated motion estimation and reduction.