Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks

TL;DR

This paper introduces a conditional GAN-based method for multi-contrast MRI synthesis that preserves high-frequency details and improves synthesis quality, enabling better diagnostic images without longer scan times.

Contribution

It presents a novel GAN framework for multi-contrast MRI synthesis that outperforms existing methods by preserving details and utilizing cycle-consistency and neighboring slice information.

Findings

Superior synthesis quality compared to state-of-the-art methods

Effective preservation of high-frequency details

Improved diagnostic utility of synthesized images

Abstract

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may prohibit acquisition of certain contrasts, and images for some contrast may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts from remaining contrasts can improve diagnostic utility. For multi-contrast synthesis, current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can in turn suffer from loss of high-spatial-frequency information in synthesized images. Here we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves high-frequency details via an adversarial loss; and it offers enhanced synthesis performance via a pixel-wise loss for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improved synthesis quality. Demonstrations on T1- and T2-weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to previous state-of-the-art methods. Our synthesis approach can help improve quality and versatility of multi-contrast MRI exams without the need for prolonged examinations.