Magnetic resonance imaging-signal fat fraction (MRI-SFF) quantifies tissue fat and serves as an established biomarker for metabolic and musculoskeletal disorders. The acquisition requires, however, specialized MRI sequences, which are not available routinely. We investigate whether SFF can be estimated from widely available T2-weighted (T2w) MRI via image-to-image translation (I2I). We further compare a lightweight 4-level U-Net to a state-of-the-art Denoising Diffusion Probabilistic Model (DDPM) using a dataset of 230 048 paired 2D images (183 517 train, 23 621 val, 22 910 test) from the German National Cohort (NAKO). Both models clearly outperform the identity baseline (Pearson correlation r = 0.769, mean absolute error MAE = 0.070 +/- 0.054), which confirms that the models learn a non-trivial cross-modal mapping. Interestingly, the lightweight U-Net outperforms the DDPM in both correlation (r = 0.975 vs. 0.962) and error (MAE = 0.014 +/- 0.015 vs. 0.019 +/- 0.019), while reducing inference time by a factor of 208 (25.2 ms vs. 5 227.2 ms per image using 50 Denoising Diffusion Implicit Model (DDIM) steps). The strong clinical performance at substantially reduced computational cost enables real-time clinical use.

翻译：磁共振成像-信号脂肪分数（MRI-SFF）可量化组织脂肪含量，并作为代谢性与肌肉骨骼疾病的既定生物标志物。然而，该指标的获取需要专用的MRI序列，这在常规临床中难以实现。本研究探究能否通过图像到图像翻译（I2I）技术，从广泛可用的T2加权（T2w）MRI中估算SFF。我们进一步比较了轻量级四层U-Net与最先进的去噪扩散概率模型（DDPM），使用德国国家队列（NAKO）的230,048对二维图像（训练集183,517对，验证集23,621对，测试集22,910对）进行测试。两种模型均显著优于恒等基线（皮尔逊相关系数r=0.769，平均绝对误差MAE=0.070±0.054），证实模型学习了非平凡的跨模态映射。值得注意的是，轻量级U-Net在相关性（r=0.975 vs. 0.962）和误差（MAE=0.014±0.015 vs. 0.019±0.019）两方面均优于DDPM，同时推理时间减少208倍（每张图像25.2毫秒 vs. 使用50步去噪扩散隐式模型（DDIM）的5,227.2毫秒）。该模型在显著降低计算成本的同时展现出强大的临床性能，为实时临床应用奠定了基础。