用单通道TSE图像生成抗金属伪影的合成MP-RAGE，提升脑部影像分析可靠性。
A Deep Learning-Based Method for Metal Artifact-Resistant Syn-MP-RAGE Contrast Synthesis
- 仅用抗伪影的TSE图像，通过深度学习生成合成MP-RAGE。
- 在31例无伪影和11例有伪影数据上，分割一致性DSC均超0.83。
- 适合金属植入物患者或数据缺失的回顾性脑部研究使用。
在某些脑部体积研究中，由定量T1 MRI（T1-qMRI）生成的合成T1加权磁化准备快速梯度回波（MP-RAGE）对比度因其清晰的白质/灰质边界，在脑部分割中极具价值。然而，传统合成MP-RAGE（syn-MP-RAGE）通常需要高质量、无伪影的多模态输入对，这在回顾性研究中常因数据缺失或损坏而难以实现。为克服此限制，本研究探索基于深度学习的方法，直接从单通道受金属伪影影响较小的涡旋自旋回波（TSE）图像生成syn-MP-RAGE。我们在31例无伪影与11例金属伪影受试者上评估该方法。分割结果以骰子相似系数（DSC）衡量，各项结果均高于0.83，表明与参考分割高度一致，且伪影组与无伪影组间分割性能无显著差异。
原文摘要 · Abstract (English)
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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