Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model

  • Authors:

  • Bing Nan Li;Chee Kong Chui;Sim Heng Ong;Toshikatsu Washio;Tomokazu Numano;Stephen Chang;Sudhakar Venkatesh;Etsuko Kobayashi

  • Affiliations:

  • NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore and Department of Precision Engineering, University of Tokyo, Tokyo, Japan;Department of Mechanical Engineering, National University of Singapore, Singapore;Department of Electrical and Computer Engineering and Division of Bioengineering, National University of Singapore, Singapore;Surgical Assist Technology Group, AIST, Tsukuba East, Japan;Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo, Japan;Department of Surgery, National University Hospital, Singapore;Department of Diagnostic Radiology, National University of Singapore, Singapore;Department of Precision Engineering, University of Tokyo, Tokyo, Japan

  • Venue:
  • MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
  • Year:
  • 2010

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Abstract

Magnetic resonance elastography (MRE) noninvasively images the propagation of mechanical waves within soft tissues. The elastic properties of soft tissues can then be quantified from MRE wave snapshots. Various algorithms have been proposed to obtain their inversion for soft tissue elasticity. Anomalies are assumed to be discernible in the elasticity map. We propose a new elastic level set model to directly detect and track abnormal soft tissues in MRE wave images. It is derived from the Mumford-Shah functional, and employs partial differential equations for function modeling and smoothing. This level set model can interpret MRE wave images without elasticity reconstruction. The experimental results on synthetic and real MRE wave images confirm its effectiveness for soft tissue discrimination.