Fat spectral modeling on triglyceride composition quantification using chemical shift encoded magnetic resonance...

Area of Science:

• Magnetic Resonance Imaging
• Biophysics
• Biochemistry

Background:

• Chemical-shift encoded MRI (CSE-MRI) is used for fat quantification.
• Accurate fat spectral models are crucial for reliable triglyceride composition and PDFF estimation.
• High field strengths (7T) can introduce challenges in fat quantification accuracy.

Purpose of the Study:

• To evaluate the performance of different fat spectral models for triglyceride composition and PDFF quantification at 7T CSE-MRI.
• To compare the accuracy of generic versus material-specific fat spectral models.
• To assess the impact of spectral model choice on high-field MRI fat quantification.

Main Methods:

• CSE-MRI experiments were performed on fatty materials and in vivo mouse tissues at 7T.
• Triglyceride composition and PDFF were estimated using 6- and 9-peak fat spectral models.
• NMR spectroscopy was used to derive material-specific fat spectral models for comparison.

Main Results:

• A 6-peak fat spectral model led to significant biases in triglyceride quantification at 7T.
• Triglyceride composition estimates varied with different 9-peak models, while PDFF estimates were consistent.
• Material-specific models showed better correlation with NMR spectroscopy results than generic models.

Conclusions:

• A material-specific 9-peak fat spectral model is essential for accurate triglyceride composition quantification with 7T CSE-MRI.
• PDFF quantification is robust across different a priori spectral models at 7T, consistent with lower field strengths.
• CSE-MRI enables spatial mapping of triglyceride composition for in vivo applications.