Finding the optimal compression level for strain-encoded (SENC) breast MRI; simulations and phantom experiments.

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Breast cancer is the most common cancer among women and the second highest cause of cancer-related death. Diagnostic magnetic resonance imaging (MRI) is recommended to screen high-risk patients. Strain-Encoded (SENC) can improve MRI's specificity by detecting and differentiating masses according to their stiffness. Previous phantom and ex-vivo studies have utilized SENC to detect cancerous masses. However, SENC required a 30% compression of the tissue, which may not be feasible for in-vivo imaging. In this work, we use finite element method simulations and phantom experiments to determine the minimum compression required to detect and classify masses. Results show that SENC is capable of detecting stiff masses at compression level of 7%, though higher compression is needed in order to differentiate between normal tissue and benign or malignant masses. With on-line SENC calculations implemented on the scanner console, we propose to start with small compressions for maximum patient comfort, then progress to larger compressions if any masses are detected.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages444-451
Number of pages8
Volume14
EditionPt 1
StatePublished - 2011

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ASJC Scopus subject areas

  • Medicine(all)

Cite this

Harouni, A. A., Jacobs, M., & Osman, N. F. (2011). Finding the optimal compression level for strain-encoded (SENC) breast MRI; simulations and phantom experiments. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 14, pp. 444-451)