Motion-Informed 3D Deep Learning Reconstruction in Patients with Cognitive Impairment.

Journal: AJNR. American journal of neuroradiology
Published Date: Feb 3, 2026

Abstract

BACKGROUND AND PURPOSE: Motion artifacts remain a key limitation in brain MRI, particularly during 3D acquisitions in cognitively impaired patients. Most deep learning (DL) reconstruction techniques improve the SNR 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. MATERIALS AND METHODS: This prospective, intraindividual 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 undersampled 3D MPRAGE with an integrated scout accelerated motion estimation and reduction (SAMER) acquisition. Image volumes were reconstructed by using standard of care methods and the proposed DL approach. Quantitative morphometric accuracy was assessed by comparing brain segmentation results of instructed-motion scans with motion-free reference scans in the healthy volunteers. Image quality was rated by 2 board-certified neuroradiologists by using a 5-point Likert scale. Statistical analysis included Wilcoxon tests and intraclass correlation coefficients. RESULTS: 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 [SD, 0.72] versus 3.59 [SD, 0.82]; P < .001). In 47% of cases, motion artifact severity was improved following DL-based processing. Interreader agreement ranged from moderate to substantial. CONCLUSIONS: Motion-informed DL reconstruction improved both morphometric accuracy and perceived image quality on 3D T1-weighted brain MRI. This technique may enhance diagnostic utility and reduce scan failure rates in motion-prone patients with cognitive impairment.

Authors

  • Shohei Fujita
    Department of Radiology, Juntendo University School of Medicine.
  • Daniel Polak
    Siemens Healthcare GmbH, Erlangen, Germany.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Daniel N Splitthoff
    Siemens Healthcare, Erlangen, Germany.
  • Daniel Nicolas Splitthoff
    Siemens Healthcare GmbH, Erlangen, Germany.
  • Yantu Huang
    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.
  • Nelson Gil
    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.
  • Sittaya Buathong
    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.
  • Chen-Hua Chiang
    Department of Radiology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
  • Wei-Ching Lo
    Siemens Medical Solutions USA, Boston, Massachusetts, USA.
  • Bryan Clifford
    Siemens Medical Solutions USA, Boston, Massachusetts, USA.
  • Stephen F Cauley
    A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • John Conklin
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Department of Radiology, Massachusetts General Hospital, Boston, USA.
  • Susie Y Huang
    Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.

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