Deep Learning-Based Acceleration in MRI: Current Landscape and Clinical Applications in Neuroradiology.

Journal: AJNR. American journal of neuroradiology
Published Date: Jul 28, 2025

Abstract

Magnetic resonance imaging (MRI) is a cornerstone of neuroimaging, providing unparalleled soft-tissue contrast. However, its clinical utility is often limited by long acquisition times, which contribute to motion artifacts, patient discomfort, and increased costs. Although traditional acceleration techniques, such as parallel imaging and compressed sensing help reduce scan times, they may reduce signal-to-noise ratio (SNR) and introduce artifacts. The advent of deep learning-based image reconstruction (DLBIR) may help in several ways to reduce scan times while preserving or improving image quality. Various DLBIR techniques are currently available through different vendors, with claimed reductions in gradient times up to 85% while maintaining or enhancing lesion conspicuity, improved noise suppression and diagnostic accuracy. The evolution of DLBIR from 2D to 3D acquisitions, coupled with advancements in self-supervised learning, further expands its capabilities and clinical applicability. Despite these advancements, challenges persist in generalizability across scanners and imaging conditions, susceptibility to artifacts and potential alterations in pathology representation. Additionally, limited data on training, underlying algorithms and clinical validation of these vendor-specific closed-source algorithms pose barriers to end-user trust and widespread adoption. This review explores the current applications of DLBIR in neuroimaging, vendor-driven implementations, and emerging trends that may impact accelerated MRI acquisitions.ABBREVIATIONS: PI＝ parallel imaging; CS＝ compressed sensing; DLBIR = deep learning-based image reconstruction; AI= artificial intelligence; DR =. Deep resolve; ACS = Artificial-intelligence-assisted compressed sensing.

Authors

  • Pranjal Rai
    From the Department of Radiology, Tata Memorial Hospital, Mumbai, Maharashtra, IN 400012 (P.R.), Division of Neuroradiology, Department of Radiology, Mayo Clinic, Rochester, MN (I.T.M., F.D., S.A.M., J.C.B., A.M., G.B.), Division of Neuroradiology, Department of Radiology, Mayo Clinic, Jacksonville, FL (N.S.), and Division of Neuroradiology, Department of Radiology, Geisinger Health, Danville, PA 17822 (A.A.).
  • Ian T Mark
    Department of Radiology, Mayo Clinic, Rochester, Minnesota.
  • Neetu Soni
    University of Rochester Medical Center Rochester, New York, USA.
  • Felix Diehn
    From the Department of Radiology, Tata Memorial Hospital, Mumbai, Maharashtra, IN 400012 (P.R.), Division of Neuroradiology, Department of Radiology, Mayo Clinic, Rochester, MN (I.T.M., F.D., S.A.M., J.C.B., A.M., G.B.), Division of Neuroradiology, Department of Radiology, Mayo Clinic, Jacksonville, FL (N.S.), and Division of Neuroradiology, Department of Radiology, Geisinger Health, Danville, PA 17822 (A.A.).
  • Steven A Messina
    From the Department of Radiology (G.B., S.A.M., D.F.B., J.C.B., B.C.R., I.T.M., F.E.D.), Mayo Clinic, Rochester, Minnesota.
  • John C Benson
    Department of Radiology, Mayo Clinic, Rochester, Minnesota.
  • Ajay Madhavan
    From the Department of Radiology, Tata Memorial Hospital, Mumbai, Maharashtra, IN 400012 (P.R.), Division of Neuroradiology, Department of Radiology, Mayo Clinic, Rochester, MN (I.T.M., F.D., S.A.M., J.C.B., A.M., G.B.), Division of Neuroradiology, Department of Radiology, Mayo Clinic, Jacksonville, FL (N.S.), and Division of Neuroradiology, Department of Radiology, Geisinger Health, Danville, PA 17822 (A.A.).
  • Amit Agarwal
  • Girish Bathla
    Mayo Clinic Rochester, Minnesota, USA.

Keywords

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