Application of Deep Learning Accelerated Image Reconstruction in T2-Weighted Turbo Spin-Echo Imaging of the Brain at 7T.

Journal: AJNR. American journal of neuroradiology
Published Date:

Abstract

Prolonged imaging times and motion sensitivity at 7T necessitate advancements in image acceleration techniques. This study evaluates a 7T deep learning (DL)-based image reconstruction by using a deep neural network trained on 7T data, applied to T2-weighted turbo spin-echo imaging. Raw -space data from 30 consecutive clinical 7T brain MRI patients was reconstructed by using both DL and standard methods. Qualitative assessments included overall image quality, artifacts, sharpness, structural conspicuity, and noise level, while quantitative metrics evaluated contrast-to-noise ratio (CNR) and image noise. DL-based reconstruction consistently outperformed standard methods across all qualitative metrics ( < .001), with a mean CNR increase of 50.8% (95% CI: 43.0%-58.6%) and a mean noise reduction of 35.1% (95% CI: 32.7%-37.6%). These findings demonstrate that DL-based reconstruction at 7T significantly enhances image quality without introducing adverse effects, offering a promising tool for addressing the challenges of ultra-high-field MRI.

Authors

  • Zeyu Liu
    Department of Aerospace and Mechanical Engineering , University of Notre Dame , Notre Dame , Indiana 46556 , United States.
  • Xiangzhi Zhou
    From the Department of Radiology, Mayo Clinic, Jacksonville, FL, USA (Z.L., X.Z., S.T., E.M.W., V.G., E.H.M.); Department of Radiology, Peking Union Medical College Hospital, Beijing, China (Z.L.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland (T.Y.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (T.Y.); LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland (T.Y.); Siemens Medical Solutions USA, Inc., Jacksonville, FL, USA (J.M.); MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany (P.L.); Siemens Medical Solutions USA, Inc., Scottsdale, AZ, USA (H.M.); Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA (H.M.).
  • Shengzhen Tao
    Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Patrick Liebig
    Siemens Healthcare, GmbH, Erlangen, Germany.
  • Mahmoud Mostapha
    Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America. Electronic address: mahmoudm@cs.unc.edu.
  • Vishal Patel
  • Erin M Westerhold
    From the Department of Radiology, Mayo Clinic, Jacksonville, FL, USA (Z.L., X.Z., S.T., E.M.W., V.G., E.H.M.); Department of Radiology, Peking Union Medical College Hospital, Beijing, China (Z.L.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland (T.Y.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (T.Y.); LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland (T.Y.); Siemens Medical Solutions USA, Inc., Jacksonville, FL, USA (J.M.); MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany (P.L.); Siemens Medical Solutions USA, Inc., Scottsdale, AZ, USA (H.M.); Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA (H.M.).
  • Hamed Mojahed
    From the Department of Radiology, Mayo Clinic, Jacksonville, FL, USA (Z.L., X.Z., S.T., E.M.W., V.G., E.H.M.); Department of Radiology, Peking Union Medical College Hospital, Beijing, China (Z.L.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland (T.Y.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (T.Y.); LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland (T.Y.); Siemens Medical Solutions USA, Inc., Jacksonville, FL, USA (J.M.); MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany (P.L.); Siemens Medical Solutions USA, Inc., Scottsdale, AZ, USA (H.M.); Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA (H.M.).
  • Vivek Gupta
    From the Department of Radiology, Mayo Clinic, Jacksonville, FL, USA (Z.L., X.Z., S.T., E.M.W., V.G., E.H.M.); Department of Radiology, Peking Union Medical College Hospital, Beijing, China (Z.L.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland (T.Y.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (T.Y.); LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland (T.Y.); Siemens Medical Solutions USA, Inc., Jacksonville, FL, USA (J.M.); MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany (P.L.); Siemens Medical Solutions USA, Inc., Scottsdale, AZ, USA (H.M.); Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA (H.M.).
  • Erik H Middlebrooks
    From the Department of Radiology, Mayo Clinic, Jacksonville, FL, USA (Z.L., X.Z., S.T., E.M.W., V.G., E.H.M.); Department of Radiology, Peking Union Medical College Hospital, Beijing, China (Z.L.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland (T.Y.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (T.Y.); LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland (T.Y.); Siemens Medical Solutions USA, Inc., Jacksonville, FL, USA (J.M.); MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany (P.L.); Siemens Medical Solutions USA, Inc., Scottsdale, AZ, USA (H.M.); Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA (H.M.).

Keywords

No keywords available for this article.