3181 ISMRM Abstract

Machine learning methods applied to MR-based Electrical Properties Tomography to improve noise robustness and boundary accuracy.

3181 · Potpourri of Contrast Mechanisms

Accepted

Adan Jafet Garcia Inda ¹, Shao Ying Huang^2,3, Stefano Mandija^4,5, Wenwei Yu^1,6

¹Department of Medical Engineering, Chiba University, Chiba, Japan

²Department of Surgery, National University of Singapore, Singapore, Singapore

³Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore

⁴Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands

⁵Computational Imaging Group for MR Diagnostic & Therapy, University Medical Center Utrecht, Utrecht, Netherlands

⁶Center for Frontier Medical Engineering, Chiba University, Chiba, Japan

Presenting Author: Adan Jafet Garcia Inda

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References

1. [8] Mandija, S., Sbrizzi, A., Katscher, U., Luijten, P. R. and Berg, C. A. (2018), Error analysis of helmholtz‐based MR‐electrical properties tomography. Magn. Reson. Med, 80: 90-100. [9] S. Gabriel, R. W. Lau and C. Gabriel. (1996). The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Physics in Medicine & Biology, Vol. 41, No. 11: 2271-2293. [10] Hassan, R., Mohammed, A., Janati, I., Youssef, G., Mohamed E. Multilayer Perceptron: Architecture Optimization and Training. International Journal of Interactive Multimedia and Artificial Intelligence. Vol. 4, No. 1: 26-30. [11] Y. Bengio, A. Courville, P. Vincent. (2014) Representation Learning: A and New Perspectives. Proceedings in International Conference on Learning Representations: 1-30 [12] Tipping, M. E., and Bishop, C. M. (1999). “Probabilistic principal component analysis”. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), 611-622. [13] Savitzky A, Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 1964; 36:1627–1639.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2020/3181