Deep Learning for Quantitative Cardiac MRI.

Journal: AJR. American journal of roentgenology
Published Date: Oct 31, 2019

Abstract

The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.

Authors

  • Qian Tao
    From the Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands (Q.T., E.H.M.P., D.P.S., A.d.R., H.J.L., R.J.v.d.G.); Department of Electrical Engineering, Fudan University, Shanghai, China (W.Y., Y.W.); Multidisciplinary Cardiovascular Research Centre & Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, England (P.G., S.P.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (L.H., L.X.); and Departments of Cardiology (M.S.) and Radiology (J.T.), Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
  • Boudewijn P F Lelieveldt
    Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden PO Box 9600, 2300 RC, The Netherlands; Intelligent Systems Department, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands.
  • Rob J van der Geest
    Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden PO Box 9600, 2300 RC, The Netherlands.

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