A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations.

Journal: AJNR. American journal of neuroradiology
Published Date: Jan 3, 2019

Abstract

BACKGROUND AND PURPOSE: MR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series.

Authors

  • A Sreekumari
    From the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India.
  • D Shanbhag
    From the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India.
  • D Yeo
    GE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York.
  • T Foo
    GE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York.
  • J Pilitsis
    Albany Medical College (J.Pilitsis), Albany, New York.
  • J Polzin
    GE Healthcare (J.Polzin), Milwaukee, Wisconsin.
  • U Patil
    From the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India.
  • A Coblentz
    University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada.
  • A Kapadia
    University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada.
  • J Khinda
    University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada.
  • A Boutet
    University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada.
  • J Port
    Mayo Clinic (J.Port), Rochester, Minnesota.
  • I Hancu
    GE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York ihancu1@gmail.com.

Keywords

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