Abstract #0186

Electrical Property Mapping using Vision Transformers and Canny Edge Detection

Ilias Giannakopoulos¹, Xinling Yu², Giuseppe Carluccio³, Gregor Koerzdoerfer⁴, Karthik Lakshmanan^1,5, Hector Lise de Moura¹, Jose Cruz Serralles¹, Jerzy Walczyk,¹, Zheng Zhang², and Riccardo Lattanzi^1,5,6

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, ²UC Santa Barbara, Santa Barbara, CA, United States, ³Universita di Napoli Federico II, Napoli, Italy, ⁴Siemens Medical Solutions, New York, NY, United States, ⁵Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, ⁶Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties, Machine Learning

Motivation: To estimate tissue electrical properties (EP) non-invasively for specific absorption rate management and as biomarkers for pathology characterization.

Goal(s): To train neural networks for mapping transmit magnetic fields (B₁⁺) onto EP.

Approach: We developed a 3D vision transformer that takes the B₁⁺ and an edge mask based on Canny filtering of the MR image as the inputs. The targets were the EP of the object. We trained on simulated tissue mimicking objects and fine-tuned on realistic head models.

Results: Our network successfully reconstructed the EP in a phantom experiment, and detected a synthetic cyst in a realistic head model in simulation.

Impact: We propose a supervised learning approach using vision transformers and Canny edge detection to perform electrical property (EP) mapping. The network successfully reconstructs the EP using experimentally measured fields and is a promising first step towards clinically-usable in-vivo EP reconstructions.

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