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arXiv · 2410.16817

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

Ziyi Zeng·Yuhao Wang·Dianlin Hu·T. Michael O'Shea·Rebecca C. Fry·Jing Cai·Lei Zhang
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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.

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Ziyi Zeng, Yuhao Wang, Dianlin Hu, T. Michael O'Shea, Rebecca C. Fry, Jing Cai, Lei Zhang. 2024-10-22. A Deep Learning-Based Method for Metal Artifact-Resistant Syn-MP-RAGE Contrast Synthesis. https://arxiv.org/abs/2410.16817

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