Automated liver elasticity calculation for 3D MRE

Bogdan Dzyubak, Kevin J. Glaser, Armando Manduca, Richard Lorne Ehman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Magnetic Resonance Elastography (MRE) is a phase-contrast MRI technique which calculates quantitative stiffness images, called elastograms, by imaging the propagation of acoustic waves in tissues. It is used clinically to diagnose liver fibrosis. Automated analysis of MRE is difficult as the corresponding MRI magnitude images (which contain anatomical information) are affected by intensity inhomogeneity, motion artifact, and poor tissue- and edge-contrast. Additionally, areas with low wave amplitude must be excluded. An automated algorithm has already been successfully developed and validated for clinical 2D MRE. 3D MRE acquires substantially more data and, due to accelerated acquisition, has exacerbated image artifacts. Also, the current 3D MRE processing does not yield a confidence map to indicate MRE wave quality and guide ROI selection, as is the case in 2D. In this study, extension of the 2D automated method, with a simple wave-amplitude metric, was developed and validated against an expert reader in a set of 57 patient exams with both 2D and 3D MRE. The stiffness discrepancy with the expert for 3D MRE was -0.8% ± 9.45% and was better than discrepancy with the same reader for 2D MRE (-3.2% ± 10.43%), and better than the inter-reader discrepancy observed in previous studies. There were no automated processing failures in this dataset. Thus, the automated liver elasticity calculation (ALEC) algorithm is able to calculate stiffness from 3D MRE data with minimal bias and good precision, while enabling stiffness measurements to be fully reproducible and to be easily performed on the large 3D MRE datasets.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
PublisherSPIE
Volume10134
ISBN (Electronic)9781510607132
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Other

OtherMedical Imaging 2017: Computer-Aided Diagnosis
CountryUnited States
CityOrlando
Period2/13/172/16/17

Fingerprint

Elasticity Imaging Techniques
Elasticity
Magnetic resonance
liver
Liver
magnetic resonance
elastic properties
stiffness
readers
Stiffness
Magnetic resonance imaging
artifacts
Artifacts
Tissue
fibrosis
phase contrast
Processing
Liver Cirrhosis
confidence
acquisition

Keywords

  • Automation
  • CAD
  • Elastography
  • Liver
  • MRE
  • ROI
  • Segmentation
  • Stiffness

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Dzyubak, B., Glaser, K. J., Manduca, A., & Ehman, R. L. (2017). Automated liver elasticity calculation for 3D MRE. In Medical Imaging 2017: Computer-Aided Diagnosis (Vol. 10134). [101340Y] SPIE. https://doi.org/10.1117/12.2254476

Automated liver elasticity calculation for 3D MRE. / Dzyubak, Bogdan; Glaser, Kevin J.; Manduca, Armando; Ehman, Richard Lorne.

Medical Imaging 2017: Computer-Aided Diagnosis. Vol. 10134 SPIE, 2017. 101340Y.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dzyubak, B, Glaser, KJ, Manduca, A & Ehman, RL 2017, Automated liver elasticity calculation for 3D MRE. in Medical Imaging 2017: Computer-Aided Diagnosis. vol. 10134, 101340Y, SPIE, Medical Imaging 2017: Computer-Aided Diagnosis, Orlando, United States, 2/13/17. https://doi.org/10.1117/12.2254476
Dzyubak B, Glaser KJ, Manduca A, Ehman RL. Automated liver elasticity calculation for 3D MRE. In Medical Imaging 2017: Computer-Aided Diagnosis. Vol. 10134. SPIE. 2017. 101340Y https://doi.org/10.1117/12.2254476
Dzyubak, Bogdan ; Glaser, Kevin J. ; Manduca, Armando ; Ehman, Richard Lorne. / Automated liver elasticity calculation for 3D MRE. Medical Imaging 2017: Computer-Aided Diagnosis. Vol. 10134 SPIE, 2017.
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