Finding the optimal compression level for Strain-Encoded (SENC) breast MRI; simulations and phantom experiments

Ahmed A. Harouni, Michael A. Jacobs, Nael F. Osman

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

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 2011 - 14th International Conference, Proceedings
Pages444-451
Number of pages8
EditionPART 1
DOIs
StatePublished - Oct 11 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6891 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period9/18/119/22/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Harouni, A. A., Jacobs, M. A., & 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 2011 - 14th International Conference, Proceedings (PART 1 ed., pp. 444-451). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6891 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-23623-5_56