A Deep Learning-Based Method for Metal Artifact-Resistant Syn-MP-RAGE Contrast Synthesis

TL;DR

This paper presents a deep learning method to synthesize high-quality brain MRI contrast images resistant to metal artifacts using only a single TSE input, improving segmentation accuracy in challenging cases.

Contribution

It introduces a novel deep learning approach that synthesizes synthetic MP-RAGE contrast from a single TSE input, bypassing the need for multi-modality data in artifact-prone scenarios.

Findings

High segmentation accuracy with DSC above 0.83

Consistent performance across artifact and non-artifact subjects

Effective synthesis from single-channel TSE input

Abstract

In certain brain volumetric studies, synthetic T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) contrast, derived from quantitative T1 MRI (T1-qMRI), proves highly valuable due to its clear white/gray matter boundaries for brain segmentation. However, generating synthetic MP-RAGE (syn-MP-RAGE) typically requires pairs of high-quality, artifact-free, multi-modality inputs, which can be challenging in retrospective studies, where missing or corrupted data is common. To overcome this limitation, our research explores the feasibility of employing a deep learning-based approach to synthesize syn-MP-RAGE contrast directly from a single channel turbo spin-echo (TSE) input, renowned for its resistance to metal artifacts. We evaluated this deep learning-based synthetic MP-RAGE (DL-Syn-MPR) on 31 non-artifact and 11 metal-artifact subjects. The segmentation results, measured by the Dice Similarity Coefficient (DSC), consistently achieved high agreement (DSC values above 0.83), indicating a strong correlation with reference segmentations, with lower input requirements. Also, no significant difference in segmentation performance was observed between the artifact and non-artifact groups.