Dataset Information

A chemical shift encoding (CSE) approach for spectral selection in fluorine-19 MRI.

ABSTRACT: PURPOSE:To develop a chemical shift encoding (CSE) approach for fluorine-19 MRI of perfluorocarbons in the presence of multiple known fluorinated chemical species. THEORY AND METHODS:A multi-echo CSE technique is applied for spectral separation of the perfluorocarbon perfluoro-15-crown-5-ether (PFCE) and isoflurane (ISO) based on their chemical shifts at 4.7?T. Cramér-Rao lower bound analysis is used to identify echo combinations with optimal signal-to-noise performance. Signal contributions are fit with a multispectral fluorine signal model using a non-linear least squares estimation reconstruction directly from k-space data. This CSE approach is tested in fluorine-19 phantoms and in a mouse with a 2D and 3D spoiled gradient-echo acquisition using multiple echo times determined from Cramér-Rao lower bound analysis. RESULTS:Cramér-Rao lower bound analysis for PFCE and ISO separation shows signal-to-noise performance is maximized with a 0.33 ms echo separation. A linear behavior (R2 ?=?0.987) between PFCE signal and known relative PFCE volume is observed in CSE reconstructed images using a mixed PFCE/ISO phantom. Effective spatial and spectral separation of PFCE and ISO is shown in phantoms and in vivo. CONCLUSION:Feasibility of a gradient-echo CSE acquisition and image reconstruction approach with optimized noise performance is demonstrated through fluorine-19 MRI of PFCE with effective removal of ISO signal contributions. Magn Reson Med 79:2183-2189, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

SUBMITTER: Ludwig KD

PROVIDER: S-EPMC5811371 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A chemical shift encoding (CSE) approach for spectral selection in fluorine-19 MRI.

Ludwig Kai D KD Hernando Diego D Roberts Nathan T NT van Heeswijk Ruud B RB Fain Sean B SB

Magnetic resonance in medicine 20170822 4

<h4>Purpose</h4>To develop a chemical shift encoding (CSE) approach for fluorine-19 MRI of perfluorocarbons in the presence of multiple known fluorinated chemical species.<h4>Theory and methods</h4>A multi-echo CSE technique is applied for spectral separation of the perfluorocarbon perfluoro-15-crown-5-ether (PFCE) and isoflurane (ISO) based on their chemical shifts at 4.7 T. Cramér-Rao lower bound analysis is used to identify echo combinations with optimal signal-to-noise performance. Signal co ...[more]

PMID: 28833448

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Project description:Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water-fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2-L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R2adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R2adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP.

Project description: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).

Dataset Information

A chemical shift encoding (CSE) approach for spectral selection in fluorine-19 MRI.

Publications

A chemical shift encoding (CSE) approach for spectral selection in fluorine-19 MRI.

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