This study evaluated a deep learning-based method using Deep Image Prior (DIP) to quantify triglyceride double bonds from chemical-shift encoded multi-echo gradient echo images without network training. We employed a cost function based on signal constraints to iteratively update the neural network on a single dataset. The method was validated using phantom experiments and in vivo scans. Results showed close alignment between measured and reference double bond values, with phantom experiments yielding a Pearson correlation coefficient of 0.96 (p = .0005). In vivo results demonstrated good agreement in subcutaneous fat. We conclude that Deep Image Prior shows feasibility for quantifying double bonds and fatty acid content from chemical-shift encoded multi-echo MRI.

本研究使用深度学习方法Deep Image Prior（DIP）对化学位移编码多回波梯度回波图像进行三酰甘油双键的定量分析，无需网络训练。我们采用基于信号约束的成本函数，在单个数据集上迭代更新神经网络。通过幻像实验和活体扫描对该方法进行验证。结果显示测量值与参考双键值之间存在密切的对应关系，幻像实验的皮尔逊相关系数为0.96（p = .0005）。活体结果在皮下脂肪中表现出良好的一致性。我们得出结论，Deep Image Prior显示出了从化学位移编码多回波MRI中定量化双键和脂肪酸含量的可行性。

基于化学位移编码的深度图像先验下甘油三酯双键定量