Chemical shift encoding based double bonds quantification in triglycerides using deep image prior.

ABSTRACT: Fatty acid can potentially serve as biomarker for evaluating metabolic disorder and inflammation condition, and quantifying the double bonds is the key for revealing fatty acid information. This study presents an assessment of a deep learning approach utilizing deep image prior (DIP) for the quantification of double bonds and methylene-interrupted double bonds of triglyceride derived from chemical-shift encoded multi-echo gradient echo images, all achieved without the necessity for network training. The methodology implemented a cost function grounded in signal constraints to continually refine the neural network's parameters on a single slice of images through iterative processes. Validation procedures encompassed both phantom experiments and in-vivo scans. The outcomes evidenced a

SUBMITTER: Huang C 

PROVIDER: S-EPMC11744162 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature