Meeting Banner
Abstract #0735

Neural network-based cr-EPT stabilization.

Adan Jafet Garcia Inda1, Shao Ying Huang2,3, Nevrez Imamoglu4, and Wenwei Yu5
1Medical 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, 4Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 5Center for Frontier Medical Engineering, Chiba University, Chiba, Japan

Electrical properties are a novel contrast mechanism for quantitative MRI. Conductivity can be used as a biomarker for tumorous tissues. Different analytic Magnetic-Resonance Electrical Properties Tomography (MREPT) methods have been proposed, however, accurate reconstructions require empirical assessment and setting of regularization coefficients per sample. In this work, based on a modified formulation of Convection-Reaction Equation-Based EPT (cr-EPT), the regularization coefficients are learned from the difference between reconstructed conductivity maps and their ground truth, using a convolutional neural network (CNN) model. The CNN model with the modified cr-EPT could achieve conductivity reconstructions with higher accuracy, compared to several analytical models.

This abstract and the presentation materials are available to members only; a login is required.

Join Here