用生成模型从两期迪克森MRI合成脂肪与铁含量图，提升非侵入检测精度。
Synthesizing Proton-Density Fat Fraction and $R_2^*$ from 2-point Dixon MRI with Generative Machine Learning
- 基于邻近体素相似性，用条件GAN从两期扫描数据推断脂肪和铁含量
- 在英国生物银行肝脏数据上，合成结果与真实值相关性显著更高
- 适合需要快速、低成本成像的临床研究者使用
磁共振成像（MRI）是无创评估体内脂肪和铁含量的金标准，分别通过质子密度脂肪分数（PDFF）和$R_2^*$衡量。传统方法需至少三组扫描以逐体素估计水、脂肪和$R_2^*$，而两期迪克森（Dixon）协议仅采集两组数据，无法直接解出三个量。本研究利用邻近体素间值的相似性，提出一种生成式机器学习方法，基于英国生物银行肝脏部位的配对迪克森-IDEAL数据，采用Pix2Pix条件生成对抗网络（cGAN），首次实现大规模两期迪克森MRI对$R_2^*$的补全。所合成的PDFF与$R_2^*$图谱与真实值的相关性显著优于传统体素级基线方法。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) is the gold standard for measuring fat and iron content non-invasively in the body via measures known as Proton Density Fat Fraction (PDFF) and $R_2^*$, respectively. However, conventional PDFF and $R_2^*$ quantification methods operate on MR images voxel-wise and require at least three measurements to estimate three quantities: water, fat, and $R_2^*$. Alternatively, the two-point Dixon MRI protocol is widely used and fast because it acquires only two measurements; however, these cannot be used to estimate three quantities voxel-wise. Leveraging the fact that neighboring voxels have similar values, we propose using a generative machine learning approach to learn PDFF and $R_2^*$ from Dixon MRI. We use paired Dixon-IDEAL data from UK Biobank in the liver and a Pix2Pix conditional GAN to demonstrate the first large-scale $R_2^*$ imputation from two-point Dixon MRIs. Using our proposed approach, we synthesize PDFF and $R_2^*$ maps that show significantly greater correlation with ground-truth than conventional voxel-wise baselines.
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