Relaxation effects in MRI-based quantification of fat content and fatty acid composition

Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging

This initial study aimed at testing whether fat-containing agents can be used for the fat mass estimation methods using magnetic resonance imaging (MRI). As an example for clinical application, fat-containing agents (based on soybean oil, 10% and 20%), 100% soybean oil, and saline as reference substances were placed outside the proximal femurs obtained from 14 participants and analyzed by 0.3 T MRI. Fat content was the estimated fat fraction (FF) based on signal intensity (SIeFF, %). The SIeFF values of the femoral bone marrow, including the femoral head, neck, shaft, and trochanter area, were measured. MRI data were compared in terms of bone mineral content (BMC) and bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) in the proximal femur. Twelve pig femurs were also used to confirm the correlation between FF by the DIXON method and SIeFF. According to Pearson's correlation coefficient, the SIeFF and total BMC and BMD data revealed strong and moderate negative correlations in the femoral head (r <  - 0.74) and other sites (r =  - 0.66 to - 0.45). FF and SIeFF showed a strong correlation (r = 0.96). This study was an initial investigation of a method for estimating fat mass with fat-containing agents and showed the potential for use in MRI. SIeFF and FF showed a strong correlation, and SIeFF and BMD and BMC showed correlation; however, further studies are needed to use SIeFF as a substitute for DXA.

Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes

OBJECTIVE

To accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes.

METHODS

The proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth.

RESULTS

Assessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R2 = 0.97) than U-Net (regression slope = 0.86, R2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53).

CONCLUSION

MDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times.

CLINICAL RELEVANCE STATEMENT

We have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a significant reduction in MR scan time by reducing the number of echoes required by 50%.

KEY POINTS

• Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes. • Prospective single-center validation demonstrated that echo reduction leads to a significant shortening of the scan time, compared to standard 6-echo acquisition. • Qualitative and quantitative performance of the proposed method showed no significant differences in PDFF estimation with respect to the reference technique.

Feasibility of joint mapping of triglyceride saturation and water longitudinal relaxation in a single breath hold applied to high fat-fraction adipose depots in the periclavicular anatomy

INTRODUCTION

Simultaneous mapping of triglyceride (TAG) saturation and tissue water relaxation may improve the characterization of the structure and function of anatomies with significant adipose tissue. While several groups have demonstrated in vivo TAG saturation imaging using MRI, joint mapping of relaxation and TAG saturation is understudied. Such mappings may avoid bias from physiological motion, if they can be done within a single breath-hold, and also account for static and applied magnetic field heterogeneity.

METHODS

We propose a transient-state/MR fingerprinting single breath-hold sequence at 3 T, a low-rank reconstruction, and a parameter estimation pipeline that jointly estimates the number of double bonds (NDB), number of methylene interrupted double bonds (NMIDB), and tissue water T1, while accounting for non-ideal radiofrequency transmit scaling and off-resonance effects. We test the proposed method in simulations, in phantom against MR spectroscopy (MRS), and in vivo regions in and around high fat fraction (FF) periclavicular adipose tissue. Partial volume and multi-peak transverse relaxation effects are explored.

RESULTS

The simulation results demonstrate accurate NDB, NMIDB, and water T1 estimates across a range of NDB, NMIDB, and T1 values. In phantoms, the proposed method's estimates of NDB and NMIDB correlate with those from MR spectroscopy (Pearson correlation ≥0.98), while the water T1 estimates are concordant with a standard phantom. The NDB and NMIDB are sensitive to partial volumes of water, showing increasing bias at FF < 40%. This bias is found to be due to noise and transverse relaxation effects. The in vivo periclavicular adipose tissue has high FF (>90%). The adipose tissue NDB and NMIDB, and muscle T1 estimates are comparable to those reported in the literature.

CONCLUSION

Robust estimation of NDB, NMIDB at high FF and water T1 across a broad range of FFs are feasible using the proposed methods. Further reduction of noise and model bias are needed to employ the proposed technique in low FF anatomies and pathologies.

Bilateral gradient-echo spectroscopic imaging with correction of frequency variations for measurement of fatty acid composition in mammary adipose tissue

PURPOSE

To develop a simultaneous dual-slab three-dimensional gradient-echo spectroscopic imaging (GSI) technique with frequency drift compensation for rapid (<6 min) bilateral measurement of fatty acid composition (FAC) in mammary adipose tissue.

METHODS

A bilateral GSI sequence was developed using a simultaneous dual-slab excitation followed by 128 monopolar echoes. A short train of navigator echoes without phase or partition encoding was included at the beginning of each pulse repetition time period to correct for frequency variation caused by respiration and heating of the cryostat. Voxel-wise spectral fitting was applied to measure the areas of the lipid spectral peaks to estimate the number of double-bond (ndb), number of methylene-interrupted double-bond (nmidb), and chain length (cl). The proposed method was tested in an oil phantom and 10 postmenopausal women to assess the influence of the frequency variation on FAC estimation.

RESULTS

The frequency drift observed over 5:27 min during the phantom scan was about 10 Hz. Phase correction based on the navigator reduced the median error of ndb, nmidb, and cl from 9.7%, 17.6%, and 3.2% to 2.1%, 9.5%, and 2.8%, respectively. The in vivo data showed a mean ± standard deviation frequency drift of 17.4 ± 2.5 Hz, with ripples at 0.3 ± 0.1 Hz. Our reconstruction algorithm successfully separated signals from the left and right breasts with negligible residual aliasing. Phase correction reduced the interquartile range within each subject's adipose tissue of ndb, nmidb, and cl by 18.4 ± 10.6%, 18.5 ± 13.9%, and 18.4 ± 10.6%, respectively.

CONCLUSION

This study shows the feasibility of obtaining bilateral spectroscopic imaging data in the breast and that incorporation of a frequency navigator improves the estimation of FAC.

Quantitative 1 H MRI and MRS of fatty acid composition

Adipose tissue as well as other depots of fat (triglycerides) are increasingly being recognized as active contributors to the human function and metabolism. In addition to the fat concentration, also the fatty acid chemical composition (FAC) of the triglyceride molecules may play an important part in diseases such as obesity, insulin resistance, hepatic steatosis, osteoporosis, and cancer. MR spectroscopy and chemical-shift-encoded imaging (CSE-MRI) are established methods for non-invasive quantification of fat concentration in tissue. More recently, similar techniques have been developed for assessment also of the FAC in terms of the number of double bonds, the fraction of saturated, monounsaturated, and polyunsaturated fatty acids, or semi-quantitative unsaturation indices. The number of papers focusing on especially CSE-MRI-based techniques has steadily increased during the past few years, introducing a range of acquisition protocols and reconstruction algorithms. However, a number of potential sources of bias have also been identified. Furthermore, the measures used to characterize the FAC using both MRI and MRS differ, making comparisons between different techniques difficult. The aim of this paper is to review MRS- and MRI-based methods for in vivo quantification of the FAC. We describe the chemical composition of triglycerides and discuss various potential FAC measures. Furthermore, we review acquisition and reconstruction methodology and finally, some existing and potential applications are summarized. We conclude that both MRI and MRS provide feasible non-invasive alternatives to the gold standard gas chromatography for in vivo measurements of the FAC. Although both are associated with gas chromatography, future studies are warranted.

Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop

More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water-fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based "big data" studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work.

Chemical-Shift-Encoded Magnetic Resonance Imaging and Spectroscopy to Reveal Immediate and Long-Term Multi-Organs Composition Changes of a 14-Days Periodic Fasting Intervention: A Technological and Case Report

Objectives: The aim of this study was to investigate the feasibility of measuring the effects of a 14-day Periodic Fasting (PF) intervention (<200 cal) on multi-organs of primary interest (liver, visceral/subcutaneous/bone marrow fat, muscle) using non-invasive advanced magnetic resonance spectroscopic (MRS) and imaging (MRI) methods.

Methods: One subject participated in a 14-day PF under daily supervision of nurses and specialized physicians, ingesting a highly reduced intake: 200 Kcal/day coupled with active walking and drinking at least 3 L of liquids/day. The fasting was preceded by a 7-day pre-fasting vegetarian period and followed by 14 days of stepwise reintroduction of food. The longitudinal study collected imaging and biological data before the fast, at peak fasting, and 7 days, 1 month, and 4 months after re-feeding. Body fat mass in the trunk, abdomen, and thigh, liver and muscle mass, were respectively computed using advanced MRI and MRS signal modeling. Fat fraction, MRI relativity index T2^* and susceptibility (Chi), as well as Fatty acid composition, were calculated at all-time points.

Results: A decrease in body weight (BW: −9.5%), quadriceps muscle volume (−3.2%), Subcutaneous and Visceral Adipose Tissue (SAT −34.4%; VAT −20.8%), liver fat fraction (PDFF = 1.4 vs. 2.6 % at baseline) but increase in Spine Bone Marrow adipose tissue (BMAT) associated with a 10% increase in global adiposity fraction (PDFF: 54.4 vs. 50.9%) was observed. Femoral BMAT showed minimal changes compared to spinal level, with a slight decrease (−3.1%). Interestingly, fatty acid (FA) pattern changes differed depending on the AT locations. In muscle, all lipids increased after fasting, with a greater increase of intramyocellular lipid (IMCL: from 2.7 to 6.3 mmol/kg) after fasting compared to extramyocellular lipid (EMCL: from 6.2 to 9.5 mmol/kg) as well as Carnosine (6.9 to 8.1 mmol/kg). Heterogenous and reverse changes were also observed after re-feeding depending on the organ.

Conclusion: These results suggest that investigating the effects of a 14-day PF intervention using advanced MRI and MRS is feasible. Quantitative MR indexes are a crucial adjunct to further understanding the effective changes in multiple crucial organs especially liver, spin, and muscle, differences between adipose tissue composition and the interplay that occurs during periodic fasting.

Validation of a standardized MRI method for liver fat and T2* quantification

Purpose

Several studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of the IDEAL method licensed from the University of Wisconsin, which has been developed for routine clinical use.

Methods

LMS IDEAL is implemented using a combination of patented and/or published acquisition and some novel model fitting methods required to correct confounds which result from the imaging and estimation processes, including: water-fat ambiguity; T2* relaxation; multi-peak fat modelling; main field inhomogeneity; T1 and noise bias; bipolar readout gradients; and eddy currents. LMS IDEAL has been designed to use image acquisition protocols that can be installed on most MRI scanners and cloud-based image processing to provide fast, standardized clinical results. Publicly available phantom data were used to validate LMS IDEAL PDFF calculations against results from originally published IDEAL methodology. LMS PDFF and T2* measurements were also compared with an independent technique in human volunteer data (n = 179) acquired as part of the UK Biobank study.

Results

We demonstrate excellent agreement of LMS IDEAL across vendors, field strengths, and over a wide range of PDFF and T2* values in the phantom study. The performance of LMS IDEAL was then assessed in vivo against widely accepted PDFF and T2* estimation methods (LMS Dixon and LMS T2*, respectively), demonstrating the robustness of LMS IDEAL to potential sources of error.

Conclusion

The development and clinical validation of the LMS IDEAL algorithm as a chemical shift-encoded MRI method for PDFF and T2* estimation contributes towards robust, unbiased applications for quantification of hepatic steatosis and iron overload, which are key features of chronic liver disease.

Comparison of MRI-derived vs. traditional estimations of fatty acid composition from MR spectroscopy signals

INTRODUCTION

The composition of fatty acids in the body is gaining increasing interest, and can be followed up noninvasively by quantitative magnetic resonance spectroscopy (MRS). However, current MRS quantification methods have been shown to provide different quantitative results in terms of lipid signals, with possible varying outcomes for a given biological examination. Quantitative magnetic resonance imaging using multigradient echo sequence (MGE-MRI) has recently been added to MRS approaches. In contrast, these methods fit the undersampled magnetic resonance temporal signal with a simplified model function (expressing the triglyceride [TG] spectrum with only three TG parameters), specific implementations and prior knowledge. In this study, an adaptation of an MGE-MRI method to MRS lipid quantification is proposed.

METHODS

Several versions of the method - with time data fully or undersampled, including or excluding the spectral peak T₂ knowledge in the fitting - were compared theoretically and on Monte Carlo studies with a time-domain, peak-fitting approach. Robustness, repeatability and accuracy were also inspected on in vitro oil acquisitions and test-retest in vivo subcutaneous adipose tissue acquisitions, adding results from the reference LCModel method.

RESULTS

On simulations, the proposed method provided TG parameter estimates with the smallest variability, but with a possible bias, which was mitigated by fitting on undersampled data and considering peak T₂ values. For in vitro measurements, estimates for all approaches were correlated with theoretical values and the best concordance was found for the usual MRS method (LCModel and peak fitting). Limited in vivo test-retest variability was found (4.1% for PUFAindx, 0.6% for MUFAindx and 3.6% for SFAindx), as for LCModel (7.6% for PUFAindx, 7.8% for MUFAindx and 3.0% for SFAindx).

CONCLUSION

This study shows that fitting the three TG parameters directly on MRS data is one valuable solution to circumvent the poor conditioning of the MRS quantification problem.

Chemical shift-encoded MRI for assessment of bone marrow adipose tissue fat composition: Pilot study in premenopausal versus postmenopausal women

OBJECT

To quantify and compare subregional proximal femur bone marrow fat composition in premenopausal and postmenopausal women using chemical shift-encoded-MRI (CSE-MRI).

MATERIALS AND METHODS

A multi gradient-echo sequence at 3 T was used to scan both hips of premenopausal (n = 9) and postmenopausal (n = 18) women. Subregional fat composition (saturation, poly-unsaturation, mono-unsaturation) was quantitatively assessed in the femoral head, femoral neck, Ward's triangle, greater trochanter, and proximal shaft in bone marrow adipose tissue and separately within red and yellow marrow adipose tissue.

RESULTS

Significant differences in fat composition in postmenopausal compared to premenopausal women, which varied depending on the subregion analyzed, were found. Within both whole and yellow marrow adipose tissue, postmenopausal women demonstrated higher saturation (+14.7% to +43.3%), lower mono- (-11.4% to -33%) and polyunsaturation (-52 to -83%) (p < 0.05). Within red marrow adipose tissue, postmenopausal women demonstrated lower fat quantity (-16% to -24%) and decreased polyunsaturation (-80 to -120%) in the femoral neck, greater trochanter, and Ward's triangle (p < 0.05).

CONCLUSION

CSE-MRI can be used to detect subregional differences in proximal femur marrow adipose tissue composition between pre- and post-menopausal women in clinically feasible scan times.

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

PURPOSE

To 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).

METHODS

MR 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.

RESULTS

Using 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.

CONCLUSION

At 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).

A data-oriented self-calibration and robust chemical-shift encoding by using clusterization (OSCAR): Theory, optimization and clinical validation in neuromuscular disorders

Multi-echo Chemical Shift-Encoded (CSE) methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water/fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant (such as for neuromuscular disorders) because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. Results established that the presented algorithm is able to identify at least 4 different partitions from MRI dataset under which to perform independent self-calibration routines and was found robust in NMD imaging studies (as evaluated on a cohort of 24 subjects) against latest CSE techniques with either calibrated or non-calibrated approaches. Particularly, the PDFF of the thigh was more reproducible for the quantitative estimation of pathological muscular fat infiltrations, which may be promising to evaluate disease progression in clinical practice.

In vivo triglyceride composition of abdominal adipose tissue measured by 1 H MRS at 3T

PURPOSE

To investigate the regional variability of adipose tissue triglyceride composition in vivo using ¹ H MRS, examining potential confounders and corrections for artifacts, to allow for adipose tissue spectrum estimation.

MATERIALS AND METHODS

¹ H magnetic resonance (MR) stimulated echo acquisition mode (STEAM) spectra were acquired in vivo at 3T from 340 adult patients (mean age 48.9 years, range 21-79 years; 172 males, 168 females; mean body mass index [BMI] 34.0, range 22-49 kg/m² ) with known or suspected nonalcoholic fatty liver disease (NAFLD) in deep (dSCAT), surface (sSCAT) subcutaneous adipose tissue, and visceral adipose tissue (VAT). Triglyceride composition was characterized by the number of double bonds (ndb) and number of methylene-interrupted double bonds (nmidb). A subset of patients (dSCAT n = 80, sSCAT n = 55, VAT n = 194) had the acquisition repeated three times to examine the repeatability of ndb and nmidb estimation.

RESULTS

Mean ndb and nmidb showed significant (P < 0.0001) differences between depots except for dSCAT and sSCAT nmidb (dSCAT ndb 2.797, nmidb 0.745; sSCAT ndb 2.826, nmidb 0.737; VAT ndb 2.723, nmidb 0.687). All ndb and nmidb estimates were highly repeatable (VAT ndb ICC = 0.888, nmidb ICC = 0.853; sSCAT: ndb ICC = 0.974, nmidb ICC = 0.964; dSCAT: ndb ICC = 0.959, nmidb ICC = 0.948).

CONCLUSION

Adipose tissue composition can be estimated repeatably using ¹ H MRS and different fat depots have different triglyceride compositions.

LEVEL OF EVIDENCE

2 J. MAGN. RESON. IMAGING 2017;45:1455-1463.

Fat-water MRI of a diet-induced obesity mouse model at 15.2T

Quantitative fat-water MRI (FWMRI) methods provide valuable information about the distribution, volume, and composition of adipose tissue (AT). Ultra high field FWMRI of animal models may have the potential to provide insights into the progression of obesity and its comorbidities. Here, we present quantitative FWMRI with all known confounder corrections on a 15.2T preclinical scanner for noninvasive in vivo monitoring of an established diet-induced obesity mouse model. Male C57BL/6J mice were placed on a low-fat (LFD) or a high-fat diet (HFD). Three-dimensional (3-D) multiple gradient echo MRI at 15.2T was performed at baseline, 4, 8, 12, and 16 weeks after diet onset. A 3-D fat-water separation algorithm and additional processing were used to generate proton-density fat fraction (PDFF), local magnetic field offset, and [Formula: see text] maps. We examined these parameters in perirenal AT ROIs from LFD and HFD mice. The data suggest that PDFF, local field offset, and [Formula: see text] have different time course behaviors between LFD and HFD mice over 16 weeks. This work suggests FWMRI at 15.2T may be a useful tool for longitudinal studies of adiposity due to the advantages of ultra high field although further investigation is needed to understand the observed time course behavior.

Hepatic fat fraction and visceral adipose tissue fatty acid composition in mice: Quantification with 7.0T MRI

PURPOSE

To develop an MRI method for quantifying hepatic fat content and visceral adipose tissue fatty acid composition in mice on a 7.0T preclinical system.

METHODS

MR acquisitions were performed with a multiple echo spoiled gradient echo with bipolar readout gradients. After phase correction, the number of double bounds (ndb) and the number of methylene interrupted double bounds (nmidb) were quantified with a model including eight fat components, and parametric maps of saturated, monounsaturated, and polyunsaturated fatty acids were derived. The model included a complex error map to correct for the phase errors and the amplitude modulation caused by the bipolar acquisition. Validations were performed in fat-water emulsions and vegetable oils. In vivo, the feasibility was evaluated in mice receiving a high-fat diet containing primarily saturated fatty acids and a low-fat diet containing primarily unsaturated fatty acids.

RESULTS

Linear regressions showed strong agreements between ndb and nmidb quantified with MRI and the theoretical values calculated using oil compositions, as well as between the proton density and the fat fractions in the emulsions. At MRI, the mouse liver fat fraction was smaller in mice fed the low-fat diet compared with mice fed the high-fat diet. In visceral adipose tissue, saturated fatty acids were significantly higher, whereas monounsaturated and polyunsaturated fatty acids were significantly lower in mice fed the low-fat diet compared with mice fed the high-fat diet.

CONCLUSION

It is feasible to simultaneously quantify hepatic fat content and visceral adipose tissue fatty acid composition with 7.0T MRI in mice. Magn Reson Med 76:510-518, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Quantification of mass fat fraction in fish using water-fat separation MRI

Selection of fish with appropriate fat content and anatomic distribution is searched in fish industry. This necessitates fast and accurate measurements of mass fat fraction maps on a large number of fish. The objective of this work is to assess the relevance of MRI water-fat separation for this purpose. For the separation of the water and fat images we rely on a single T2(⁎) and a multiple peak fat spectrum model, the parameters of which are estimated using the "Varpro" method. The difference of proton density between fat and water and the lack of the signal from the macromolecules are taken into account to convert the obtained proton density fat fraction into mass fat fraction. We used 0.23T NMR to validate the method on 30 salmon steaks. The fat fraction values were in the range of 5% to 25%. Very good accordance was found between 1.5T MRI and NMR although MRI slightly overestimated the mass fat fraction. The R(2) of the linear regression was equal to 0.96 (P<10(-5)), the slope to 1.12 (CI.95=0.03). These results demonstrate that a good accuracy can be achieved. We also show that high throughput can be achieved since the measurements do not depend on the position and we conclude that, for example, it is feasible to quantify the mass fat fraction in fish steaks within about one minute per sample.