Finding the Optimal Compression Level for Strain-Encoded (SENC) Breast MRI; Simulations and Phantom Experiments

  • Ahmed A. Harouni
  • Michael A. Jacobs
  • Nael F. Osman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)

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.

Keywords

Dynamic Mechanical Analyzer Breast Magnetic Resonance Imaging Finite Element Method Simulation Phantom Experiment Malignant Mass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    American Cancer society, Cancer Facts and Figures 2009 (2009), http://www.cancer.org
  2. 2.
    Bluemke, D.A., Gatsonis, C., Chen, M.H., DeAngelis, G.A., DeBruhl, N., Harms, S., Heywang-Kobrunner, S.H., Hylton, N., Kuhl, C.K., Lehman, C., et al.: Magnetic resonance imaging of the breast prior to biopsy. Jama 292(22), 2735 (2004)CrossRefGoogle Scholar
  3. 3.
    Samani, A., Zubovits, J., Plewes, D.: Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples. Physics in Medicine and Biology 52(6), 1565–1576 (2007)CrossRefGoogle Scholar
  4. 4.
    Osman, N., Sampath, S., Atalar, E., Prince, J.: Imaging longitudinal cardiac strain on short-axis images using strain-encoded MRI. Magnetic Resonance in Medicine 46(2), 324–334 (2001)CrossRefGoogle Scholar
  5. 5.
    Osman, N.: Detecting stiff masses using strain-encoded (SENC) imaging. Magnetic Resonance in Medicine 49(3), 606–608 (2003)CrossRefGoogle Scholar
  6. 6.
    Fahmy, A., Krieger, A., Osman, N.: An integrated system for real-time detection of stiff masses with a single compression. IEEE Transactions on Biomedical Engineering 53(7), 1286–1293 (2006)CrossRefGoogle Scholar
  7. 7.
    Harouni, A.A., Hossain, J., Jacobs, M.A., Osman, N.F.: Improved Hardware for Higher Spatial Resolution Strain-Encoded (SENC) MRI. Academic Radiology 18, 705–715 (2011)CrossRefGoogle Scholar
  8. 8.
    Harouni, A.A., El Khouli, R.H., Hossain, J., Bluemke, D.A., Osman, N.F., Jacobs, M.A.: Enhancing mass detection and classification in breast tissue using Strain-ENCoded (SENC) breast MRI with histological validation. Submitted to Journal of Magnetic Resonance Imaging (March 2011)Google Scholar
  9. 9.
    Yousef, T.A., Osman, N.F.: Effect of Noise and Slice Profile on Strain Quantifications of Strain Encoding (SENC) MRI. In: Sachse, F.B., Seemann, G. (eds.) FIHM 2007. LNCS, vol. 4466, pp. 50–59. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ahmed A. Harouni
    • 1
  • Michael A. Jacobs
    • 2
  • Nael F. Osman
    • 1
    • 2
  1. 1.Electrical and Computer EngineeringJohns Hopkins UniversityUSA
  2. 2.Russell H. Morgan Department of Radiology and OncologyJohns Hopkins University school of medicineBaltimoreUSA

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