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Autofocusing of clinical shoulder MR images for correction of motion artifacts

  • Armando Manduca
  • Kiaran P. McGee
  • E. Brian Welch
  • Joel P. Felmlee
  • Richard L. Ehman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

A post-processing ”autofocusing” algorithm for the reduction of motion artifacts in MR images has been developed and tested on a large clinical data set of high resolution shoulder images. The algorithm uses only the raw (complex) data from the MR scanner, and requires no knowledge of the patient motion during the scan, deducing that from the raw data itself. It operates by searching over the space of possible patient motions and optimizing the image quality. Evaluation of this technique on the clinical data set (for which navigator echo based measured motions and corrected images were available) show that the algorithm can correct for the effects of global translation during the scan almost as well as the navigator echo approach and is more robust.

Keywords

Rotator Cuff Motion Correction Synthetic Aperture Radar Image Patient Motion Average Improvement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Armando Manduca
    • 1
  • Kiaran P. McGee
    • 1
  • E. Brian Welch
    • 1
  • Joel P. Felmlee
    • 1
  • Richard L. Ehman
    • 1
  1. 1.Mayo Clinic and FoundationRochester

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