Magnetic Resonance

European Radiology

, Volume 17, Issue 1, pp 259-264

First online:

Application and assessment of a robust elastic motion correction algorithm to dynamic MRI

  • K.-H. HerrmannAffiliated withMedical Physics Group, Institute for Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena Email author 
  • , S. WurdingerAffiliated withInstitute for Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena
  • , D. R. FischerAffiliated withInstitute for Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena
  • , I. KrumbeinAffiliated withInstitute for Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena
  • , M. SchmittAffiliated withSiemens Medical Solutions
  • , G. HermosilloAffiliated withSiemens Computer Aided Diagnosis and Therapy
  • , K. ChaudhuriAffiliated withSiemens Computer Aided Diagnosis and Therapy
  • , A. KrishnanAffiliated withSiemens Computer Aided Diagnosis and Therapy
  • , M. SalganicoffAffiliated withSiemens Computer Aided Diagnosis and Therapy
    • , W. A. KaiserAffiliated withInstitute for Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena
    • , J. R. ReichenbachAffiliated withMedical Physics Group, Institute for Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena

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Abstract

The purpose of this study was to assess the performance of a new motion correction algorithm. Twenty-five dynamic MR mammography (MRM) data sets and 25 contrast-enhanced three-dimensional peripheral MR angiographic (MRA) data sets which were affected by patient motion of varying severeness were selected retrospectively from routine examinations. Anonymized data were registered by a new experimental elastic motion correction algorithm. The algorithm works by computing a similarity measure for the two volumes that takes into account expected signal changes due to the presence of a contrast agent while penalizing other signal changes caused by patient motion. A conjugate gradient method is used to find the best possible set of motion parameters that maximizes the similarity measures across the entire volume. Images before and after correction were visually evaluated and scored by experienced radiologists with respect to reduction of motion, improvement of image quality, disappearance of existing lesions or creation of artifactual lesions. It was found that the correction improves image quality (76% for MRM and 96% for MRA) and diagnosability (60% for MRM and 96% for MRA).

Keywords

Motion correction MR mammography MR angiography Motion artifacts Registration