Abstract #3545

Multi-Contrast-Specific Objective Functions for MR Image Deep Learning - Losses for Pixelwise Error, Misregistration, and Local Variance

Hanbyol Jang¹, Sewon Kim¹, Jinseong Jang¹, Young Han Lee², and Dosik Hwang*¹

¹School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea, ²Yonsei University College of Medicine, Seoul, Republic of Korea

The goal of this study is to make new contrast image from multiple contrast Magnetic Resonance Image (MRI) using deep learning with loss function specialized for multiple image processing. Our contrast-conversion deep neural network (CC-DNN) is an end-to-end architecture that trains the model to create one image (STIR image) from three images (T1-weighted, T2-weighted, and GRE images). And we propose a new loss function to take into account intensity differences, misregistration, and local intensity variations.

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