ArticleMagnetic resonance imaging2018
Fat spectral modeling on triglyceride composition quantification using chemical shift encoded magnetic resonance imaging.
Article in Magnetic resonance imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Magnetic Resonance Spectroscopy of Hepatic Fat from Fundamental to Clinical Applications.Diagnostics (Basel, Switzerland) · 2021Review
- Oxidized Hyaluronic Acid Hydrogels as a Carrier for Constant-Release Clenbuterol Against High-Fat Diet-Induced Obesity in Mice.Frontiers in endocrinology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeTo explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).
methodsMR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.
resultsUsing a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.
conclusionAt a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).
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