Abstract #3181

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

Adan Jafet Garcia Inda¹, Shao Ying Huang^2,3, Stefano Mandija^4,5, and 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

MREPT is a technique used to non-invasively estimate the electrical properties (EPs) of tissues based on Maxwell equations from MRI measurements. However, most reconstruction techniques are susceptible to noise and have severe boundary artifacts. In this work, we designed problem-oriented machine learning methods to improve the MREPT reconstructions. Through numerical experiments with 2-D cylindrical phantoms and comparison with cr-EPT, we demonstrate the feasibility of ML approaches to provide more noise robust EPT reconstructions with lower boundary artifacts.

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