Clinical evaluation of automated scan prescription of knee MR images

J Magn Reson Imaging. 2009 Jan;29(1):141-5. doi: 10.1002/jmri.21633.

Abstract

Purpose: To compare an automated scan planning method to manual scan positioning in routine knee magnetic resonance imaging (MRI) studies.

Materials and methods: The automated scan planning method uses anatomical landmarks in a 3D survey of the knee. The method is trained by example plannings, consisting of manual slice positioning by an experienced technologist in 15 MRI studies. Automated knee MR examinations obtained in three geometries in 50 consecutive patients were compared to those obtained in 50 consecutive control patients, where imaging planes were planned manually. Anatomical coverage and slice angulation were scored for each geometry on a 4-grade scale by an experienced radiologist blinded to the way of planning; groups were compared using a Mann-Whitney U-test.

Results: In 150 automated sequences the technologist adapted slice positioning in four cases (addition of slices to adapt to the size of the knee), representing the only automated sequences that received a poor rating. Thirteen sequences with manual planning received a poor rating. No difference in quality was found (P > 0.05) between automated and manual plannings for coronal coverage, sagittal coverage and angulation, and transverse angulation. Rating of automated planning was higher for transverse coverage, but lower than manual planning for coronal angulation.

Conclusion: Automated sequence prescription for knee MRI is feasible in clinical practice, with similar quality as manual positioning.

Publication types

  • Evaluation Study

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Knee Joint / anatomy & histology*
  • Magnetic Resonance Imaging / methods*
  • Observer Variation
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity