Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior
<|reference_start|>Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior: 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.<|reference_end|>
@article{huang2024chemical,
    title={Chemical Shift Encoding based Double Bonds Quantification in
  Triglycerides using Deep Image Prior},
    author={Chaoxing Huang, Ziqiang Yu, Zijian Gao, Qiuyi Shen, Queenie Chan,
  Vincent Wai-Sun Wong, Winnie Chiu-Wing Chu, Weitian Chen},
    journal={arXiv preprint arXiv:2407.01926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01926},
    primaryClass={physics.med-ph cs.CV}
}