Accurate quantification of visceral adipose tissue (VAT) using water-saturation MRI and computer segmentation: Preliminary results

Comparison and precision of visceral adipose tissue measurement techniques in a multisite longitudinal study using MRI

BACKGROUND

Measurement of visceral adipose tissue (VAT) using magnetic resonance imaging (MRI) is considered accurate and safe. Single slice measurements perform similar to volumetric measurements for cross-sectional observation studies but may not perform as well for longitudinal studies. This study compared the performance of single slice to volumetric VAT measurements in a prospective longitudinal study. Consistency of results across sites and over time was also evaluated.

METHODS

A total of 935 healthy participants were recruited and scanned with MRI twice, approximately six months apart as part of a randomized, controlled, parallel arm, unblinded study conducted at four clinical centers in the United States. A 3D Dixon MRI sequence was used to image the abdomen, and visceral fat volumes were quantified for the abdomen, reduced coverage volumes (11 and 25 slices), and at single slices positioned at anatomical landmarks. A traveling phantom was scanned twice at all imaging sites.

RESULTS

The correlation of single slice VAT measurement to full abdomen volumetric measurements ranged from 0.78 to 0.93 for cross-sectional observation measurements and 0.30 to 0.55 for longitudinal change. Reduced coverage volumetric measurement outperformed single slice measurements but still showed improved precision with more slices with cross-sectional observation and longitudinal correlations of 0.94 and 0.66 for 11 slices and 0.94 and 0.70 for 25 slices, respectively. No significant differences were observed across sites or over time with the traveling phantom and the volume measurements had a standard deviation of 14.1 mL, 2.6% of the measured volume.

CONCLUSION

Single slice VAT measurements had significantly lower correlation with abdomen VAT volume for longitudinal change than for cross-sectional observation measurements and may not be suitable for longitudinal studies. Data from multiple sites, different scanners, and over time did not show significant differences.

Assessing visceral and subcutaneous adiposity using segmented T2-MRI and multi-frequency segmental bioelectrical impedance: A sex-based comparative study

BACKGROUND AND AIM

This study aims to quantify abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) using T2-weighted magnetic resonance imaging (MRI), and assess the extent of its concordance with VAT surface-area measured by a state-of-the-art segmental multi-frequency bioelectrical impedance analysis (BIA) device. A comparison between manual and semi-automated segmentation was conducted. Further, abdominal VAT and SAT sex-based comparison in healthy Arab adults was piloted.

METHODS

A cross-sectional design was followed to recruit subjects. Abdominal VAT and SAT were determined on T2-weighted MRI manually and semi-automatically. Body composition was assessed using a BIA machine. Statistical differences between the abdominal VAT areas defined by BIA, manual, and semi-automated MRI were compared. Correlation between all methods was assessed, and statistical differences between sex abdominal VAT/SAT defined areas were compared.

RESULTS

A total of 165 abdominal T2-weighted MR images taken for 55 overweight/obese adult subjects were analyzed Differences between manual and semi-automated MRI-obtained abdominal VAT and SAT were found statistically significant (P<0.001) for all subjects. Mean abdominal VAT using the BIA technique was found to correlate significantly with manually and semi-automated T2-weighted MRI defined VAT (r=0.7436; P<0.001 and r=0.8275; P<0.001, respectively). Abdominal VAT was significantly (P<0.001) different between male and female subjects accumulating at different abdominal levels.

CONCLUSION

Semi-automatic segmentation showed a stronger significant correlation with BIA compared to manual segmentation, implying a more reliable quantification of abdominal VAT/SAT. Segmental BIA technique may serve as a feasible and convenient assessment tool for the visceral adiposity in obese subjects.

Difference in the frequency of pouchitis between ulcerative colitis and familial adenomatous polyposis: is the explanation in peripouch fat?

AIM

Patients with ulcerative colitis (UC) have an unexplained higher incidence of pouchitis and a greater amount of peripouch fat compared with patients with familial adenomatous polyposis (FAP). The aims of this study were to compare the peripouch fat areas between patients with UC and patients with FAP, and to explore relationship between peripouch fat and pouchitis or chronic antibiotic-refractory pouchitis (CARP).

METHOD

Patients with an abdominal CT image from our prospectively maintained Pouch Database were included. Abdominal fat and peripouch fat were measured on CT images at different levels or planes. Comparisons of peripouch fat and CARP were performed before and after propensity score matching.

RESULTS

A total of 277 patients with UC and 40 patients with FAP were included. Compared with patients with FAP, patients with UC were found to have a higher incidence of pouchitis (58.5% vs 15.0%, P < 0.001) and CARP (24.5% vs 2.5%, P = 0.002) and a higher total peripouch fat area (P = 0.030) and mesenteric peripouch fat area (P = 0.022) at Level-3. Univariate and multivariate analyses showed that diagnosis (UC vs FAP) and peripouch fat areas at Level-3 and Level-5 were independent risk factors for CARP. With propensity score matching, 38 pairs of patients with UC and FAP were matched successfully. After matching, patients with UC were found to have higher total peripouch fat area and higher mesenteric peripouch fat area at Level-3, and a higher incidence of pouchitis (57.9% vs 13.2%, P < 0.001) and CARP (23.7% vs 2.6%, P = 0.007).

CONCLUSION

Our study demonstrates that patients with UC have more peripouch fat than those with FAP, which may explain the difference in the frequency of pouchitis and CARP between these groups of patients.

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images

OBJECTIVES

To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.

MATERIALS AND METHODS

The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects.

RESULTS

The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively.

CONCLUSION

Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

Age and gender specific estimation of visceral adipose tissue amounts from radiological images in morbidly obese patients

Image-based quantifications of visceral adipose tissue (VAT) volumes from segmented VAT areas are increasingly considered for risk assessment in obese patients. The goal of this study was to determine the power of partial VAT areas to predict total VAT volume in morbidly obese patients (BMI > 40 kg/m²) as a function of gender, age and anatomical landmarks. 130 morbidly obese patients (mean BMI 46.5 kg/m²; 94 females) underwent IRB-approved MRI. Total VAT volumes were predicted from segmented VAT areas (of single or five adjacent slices) at common axial landmark levels and compared with the measured ones (V_VAT-T, about 40 slices between diaphragm and pelvic floor). Standard deviations σ₁ and σ₅ of the respective VAT volume differences served as measures of agreement. Mean V_VAT-T was 4.9 L for females and 8.1 L for males. Best predictions were found at intervertebral spaces L3-L4 for females (σ₅ = 688 ml, σ₁ = 832 ml) and L1-L2 for males (σ₅ = 846 ml, σ₁ = 992 ml), irrespective of age. In conclusion, VAT volumes in morbidly obese patients can be reliably predicted by multiplying the segmented VAT area at a gender-specific lumbar reference level with a fixed scaling factor and effective slice thickness.

Segmentation and quantification of adipose tissue by magnetic resonance imaging

In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water-fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations.

Fully automatic and nonparametric quantification of adipose tissue in fat-water separation MR imaging

Despite increasing demand and research efforts, currently there is no consensus on the protocol for automated and reliable quantification of adipose tissue (AT) and visceral adipose tissue (VAT) using MRI. The purpose of this study was to propose a novel computational method with enhanced objectiveness for the quantification of AT and VAT in fat-water separation MRI. 3T data from IDEAL were acquired for the fat-water separation. Fat tissues were separated from nonfat regions (background air, bone, water, and other nonfat tissues) using K-means clustering (K = 2). From the binary fat mask, arm regions were separated from body based on the relative size of connected component. AT was obtained from the binary body fat mask. With the initial contour as the outer boundary of body fat, the subcutaneous adipose tissue (SAT) and VAT were separated using deformable model driven by a specifically generated deformation field pointing to the inner boundary of SAT. The proposed method was tested on 16 patients with dyslipidemia and evaluated by comparing the correlation with semi-automatic segmentation results. Good robustness was also observed in the proposed method from the Bland-Altman plots. Compared to other established fat segmentation methods, the proposed method is highly objective for fat-water separation MRI with minimal variability induced by subjective parameter settings.

Quantification of abdominal fat depots in rats and mice during obesity and weight loss interventions

Background & Aims

Obesity is a leading healthcare issue contributing to metabolic diseases. There is a great interest in non-invasive approaches for quantitating abdominal fat in obese animals and humans. In this work, we propose an automated method to distinguish and quantify subcutaneous and visceral adipose tissues (SAT and VAT) in rodents during obesity and weight loss interventions. We have also investigated the influence of different magnetic resonance sequences and sources of variability in quantification of fat depots.

Materials and Methods

High-fat diet fed rodents were utilized for investigating the changes during obesity, exercise, and calorie restriction interventions (N = 7/cohort). Imaging was performed on a 7T Bruker ClinScan scanner using fast spin echo (FSE) and Dixon imaging methods to estimate the fat depots. Finally, we quantified the SAT and VAT volumes between the L1–L5 lumbar vertebrae using the proposed automatic hybrid geodesic region-based curve evolution algorithm.

Results

Significant changes in SAT and VAT volumes (p<0.01) were observed between the pre- and post-intervention measurements. The SAT and VAT were 44.22±9%, 21.06±1.35% for control, −17.33±3.07%, −15.09±1.11% for exercise, and 18.56±2.05%, −3.9±0.96% for calorie restriction cohorts, respectively. The fat quantification correlation between FSE (with and without water suppression) sequences and Dixon for SAT and VAT were 0.9709, 0.9803 and 0.9955, 0.9840 respectively. The algorithm significantly reduced the computation time from 100 sec/slice to 25 sec/slice. The pre-processing, data-derived contour placement and avoidance of strong background–image boundary improved the convergence accuracy of the proposed algorithm.

Conclusions

We developed a fully automatic segmentation algorithm to quantitate SAT and VAT from abdominal images of rodents, which can support large cohort studies. We additionally identified the influence of non-algorithmic variables including cradle disturbance, animal positioning, and MR sequence on the fat quantification. There were no large variations between FSE and Dixon-based estimation of SAT and VAT.

Specific appetite, energetic and metabolomics responses to fat overfeeding in resistant-to-bodyweight-gain constitutional thinness

Background:

Contrasting with obesity, constitutional thinness (CT) is a rare condition of natural low bodyweight. CT exhibits preserved menstruation in females, no biological marker of undernutrition, no eating disorders but a bodyweight gain desire. Anorexigenic hormonal profile with high peptide tyrosine tyrosine (PYY) was shown in circadian profile. CT could be considered as the opposite of obesity, where some patients appear to resist diet-induced bodyweight loss.

Objective:

The objective of this study was to evaluate appetite regulatory hormones in CTs in an inverse paradigm of diet-induced weight loss.

Methods:

A 4-week fat overfeeding (2640 kJ excess) was performed to compare eight CT women (body mass index (BMI)<17.5 kg m⁻²) to eight female controls (BMI 18.5–25 kg m⁻²). Appetite regulatory hormones profile after test meal, food intake, bodyweight, body composition, energy expenditure and urine metabolomics profiles were monitored before and after overfeeding.

Results:

After overfeeding, fasting total and acylated ghrelin were significantly lower in CTs than in controls (P=0.01 and 0.03, respectively). After overfeeding, peptide tyrosine tyrosine (PYY) and glucagon-like-peptide 1 both presented earlier (T15 min vs T30 min) and higher post-meal responses (incremental area under the curve) in CTs compared with controls. CTs failed to increase bodyweight (+0.22±0.18 kg, P=0.26 vs baseline), contrasting with controls (+0.72±0.26 kg, P=0.03 vs baseline, P=0.01 vs CTs). Resting energy expenditure increased in CTs only (P=0.031 vs baseline). After overfeeding, a significant negative difference between total energy expenditure and food intake was noticed in CTs only (−2754±720 kJ, P=0.01).

Conclusion:

CTs showed specific adaptation to fat overfeeding: overall increase in anorexigenic hormonal profile, enhanced post prandial GLP-1 and PYY and inverse to controls changes in urine metabolomics. Overfeeding revealed a paradoxical positive energy balance contemporary to a lack of bodyweight gain, suggesting yet unknown specific energy expenditure pathways in CTs.

The advantages and limitations of cross-sectional body composition analysis

PURPOSE OF REVIEW

Cross-sectional (C-S) imaging is now commonly used to measure body composition in clinical studies. This review highlights the advantages, limitations and suggested future directions for this technique.

RECENT FINDINGS

Current understanding of C-S imaging reproducibility, tissue identification and segmentation methods, comparison between imaging techniques and estimates of whole body composition using a single image are described.

SUMMARY

C-S imaging can reliably measure muscle and fat distribution and uniquely discriminate between intra-abdominal organ and muscle component of fat-free mass. It precisely tracks changes within an individual, but is less able to distinguish true differences in whole body estimates between individuals.

Impact of partial volume effects on visceral adipose tissue quantification using MRI

PURPOSE

To quantitatively estimate the impact of partial volume effects on visceral adipose tissue (VAT) quantification using typical resolution magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Nine normal or overweight subjects were scanned at central abdomen levels with a water-saturated, balanced steady-state free precession (b-SSFP) sequence. The water-saturation effectiveness was evaluated with region-of-interest analysis on fat, muscle, bowel, and noise areas. The number of full-volume (FV) and partial-volume (PV) fat pixels was estimated based on a gray-level histogram model of water-saturated images. Both FV and PV fat amounts were quantified.

RESULTS

High-quality, fat-only images were generated with the b-SSFP imaging method. Fat SNR was 77.7 ± 25.6 and water-saturation was effective, with the average fat-to-water signal intensity ratio = 20.7 ± 3.8. The average ratio of partial- to full-volume fat amounts was 104.0%. The ratio was higher with lower body mass index (BMI) and PV fat amount only increased slightly when BMI increased.

CONCLUSION

PV fat contributes a significant amount of fat to fat measurements on typical spatial resolution MRI on normal and overweight subjects. The relative PV fat contribution is markedly higher in slimmer patients. Inclusion of this portion of the adipose tissue will increase overall accuracy and decrease variability of VAT quantification using MRI.

Novel segmentation method for abdominal fat quantification by MRI

PURPOSE

To introduce and describe the feasibility of a novel method for abdominal fat segmentation on both water-saturated and non-water-saturated MR images with improved absolute fat tissue quantification.

MATERIALS AND METHODS

A general fat distribution model which fits both water-saturated (WS) and non-water-saturated (NWS) MR images based on image gray-level histogram is first proposed. Next, a novel fuzzy c-means clustering step followed by a simple thresholding is proposed to achieve automated and accurate abdominal quantification taking into consideration the partial-volume effects (PVE) in abdominal MR images. Eleven subjects were scanned at central abdomen levels with both WS and NWS MRI techniques. Synthesized "noisy" NWS (nNWS) images were also generated to study the impact of reduced SNR on fat quantification using the novel approach. The visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) amounts of the WS MR images were quantified with a traditional intensity thresholding method as a reference to evaluate the performance of the novel method on WS, NWS, and nNWS MR images.

RESULTS

The novel approach resulted in consistent SAT and VAT amounts for WS, NWS, and nNWS images. Automatic segmentation and incorporation of spatial information during segmentation improved speed and accuracy. These results were in good agreement with those from the WS images quantified with a traditional intensity thresholding method and accounted for PVE contributions.

CONCLUSION

The proposed method using a novel fuzzy c-means clustering method followed by thresholding can achieve consistent quantitative results on both WS and NWS abdominal MR images while accounting for PVE contributing inaccuracies.

Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging

As the prevalence of obesity continues to rise, rapid and accurate tools for assessing abdominal body and organ fat quantity and distribution are critically needed to assist researchers investigating therapeutic and preventive measures against obesity and its comorbidities. Magnetic resonance imaging (MRI) is the most promising modality to address such need. It is non-invasive, utilizes no ionizing radiation, provides unmatched 3-D visualization, is repeatable, and is applicable to subject cohorts of all ages. This article is aimed to provide the reader with an overview of current and state-of-the-art techniques in MRI and associated image analysis methods for fat quantification. The principles underlying traditional approaches such as T(1) -weighted imaging and magnetic resonance spectroscopy as well as more modern chemical-shift imaging techniques are discussed and compared. The benefits of contiguous 3-D acquisitions over 2-D multislice approaches are highlighted. Typical post-processing procedures for extracting adipose tissue depot volumes and percent organ fat content from abdominal MRI data sets are explained. Furthermore, the advantages and disadvantages of each MRI approach with respect to imaging parameters, spatial resolution, subject motion, scan time and appropriate fat quantitative endpoints are also provided. Practical considerations in implementing these methods are also presented.

Quantification of adiposity in small rodents using micro-CT

Non-invasive three-dimensional imaging of live rodents is a powerful research tool that has become critical for advances in many biomedical fields. For investigations into adipose development, obesity, or diabetes, accurate and precise techniques that quantify adiposity in vivo are critical. Because total body fat mass does not accurately predict health risks associated with the metabolic syndrome, imaging modalities should be able to stratify total adiposity into subcutaneous and visceral adiposity. Micro-computed tomography (micro-CT) acquires high-resolution images based on the physical density of the material and can readily discriminate between subcutaneous and visceral fat. Here, a micro-CT based method to image the adiposity of live rodents is described. An automated and validated algorithm to quantify the volume of discrete fat deposits from the computed tomography is available. Data indicate that scanning the abdomen provides sufficient information to estimate total body fat. Very high correlations between micro-CT determined adipose volumes and the weight of explanted fat pads demonstrate that micro-CT can accurately monitor site-specific changes in adiposity. Taken together, in vivo micro-CT is a non-invasive, highly quantitative imaging modality with greater resolution and selectivity, but potentially lower throughput, than many other methods to precisely determine total and regional adipose volumes and fat infiltration in live rodents.

Algebraic decomposition of fat and water in MRI

The decomposition of magnetic resonance imaging (MRI) data to generate water and fat images has several applications in medical imaging, including fat suppression and quantification of visceral fat. We introduce a novel algorithm to overcome some of the problems associated with current analytical and iterative decomposition schemes. In contrast to traditional analytical schemes, our approach is general enough to accommodate any uniform echo-shift pattern, any number of metabolites and signal samples. In contrast to region-growing method that use a smooth field-map initialization to resolve the ambiguities with the IDEAL algorithm, we propose to use an explicit smoothness constraint on the final field-map estimate. Towards this end, we estimate the number of feasible solutions at all the voxels, prior to the evaluation of the roots. This approach enables the algorithm to evaluate all the feasible roots, thus avoiding the convergence to the wrong solution. The estimation procedure is based on a modification of the harmonic retrieval (HR) framework to account for the chemical shift dependence in the frequencies. In contrast to the standard linear HR framework, we obtain the frequency shift as the common root of a set of quadratic equations. On most of the pixels with multiple feasible solutions, the correct solution can be identified by a simple sorting of the solutions. We use a region-merging algorithm to resolve the remaining ambiguity and phase-wrapping. Experimental results indicate that the proposed algebraic scheme eliminates most of the difficulties with the current schemes, without compromising the noise performance. Moreover, the proposed algorithm is also computationally more efficient.

Automated separation of visceral and subcutaneous adiposity in in vivo microcomputed tomographies of mice

Reflecting its high resolution and contrast capabilities, microcomputed tomography (microCT) can provide an in vivo assessment of adiposity with excellent spatial specificity in the mouse. Herein, an automated algorithm that separates the total abdominal adiposity into visceral and subcutaneous compartments is detailed. This algorithm relies on Canny edge detection and mathematical morphological operations to automate the manual contouring process that is otherwise required to spatially delineate the different adipose deposits. The algorithm was tested and verified with microCT scans from 74 C57BL/6J mice that had a broad range of body weights and adiposity. Despite the heterogeneity within this sample of mice, the algorithm demonstrated a high degree of stability and robustness that did not necessitate changing of any of the initially set input variables. Comparisons of data between the automated and manual methods were in complete agreement (R (2) = 0.99). Compared to manual contouring, the increase in precision and accuracy, while decreasing processing time by at least an order of magnitude, suggests that this algorithm can be used effectively to separately assess the development of total, visceral, and subcutaneous adiposity. As an application of this method, preliminary data from adult mice suggest that a relative increase in either subcutaneous, visceral, or total fat negatively influences skeletal quantity and that fat infiltration in the liver is greatly increased by a high-fat diet.

Quantitative and qualitative differences in subcutaneous adipose tissue stores across lipodystrophy types shown by magnetic resonance imaging

Background

Lipodystrophies are characterized by redistributed subcutaneous fat stores. We previously quantified subcutaneous fat by magnetic resonance imaging (MRI) in the legs of two patients with familial partial lipodystrophy subtypes 2 and 3 (FPLD2 and FPLD3, respectively). We now extend the MRI analysis across the whole body of patients with different forms of lipodystrophy.

Methods

We studied five subcutaneous fat stores (supraclavicular, abdominal, gluteal, thigh and calf) and the abdominal visceral fat stores in 10, 2, 1, 1 and 2 female subjects with, respectively, FPLD2, FPLD3, HIV-related partial lipodystrophy (HIVPL), acquired partial lipodystrophy (APL), congenital generalized lipodystrophy (CGL) and in six normal control subjects.

Results

Compared with normal controls, FPLD2 subjects had significantly increased supraclavicular fat, with decreased abdominal, gluteal, thigh and calf subcutaneous fat. FPLD3 subjects had increased supraclavicular and abdominal subcutaneous fat, with less severe reductions in gluteal, thigh and calf fat compared to FPLD2 subjects. The repartitioning of fat in the HIVPL subject closely resembled that of FPLD3 subjects. APL and CGL subjects had reduced upper body, gluteal and thigh subcutaneous fat; the APL subject had increased, while CGL subjects had decreased subcutaneous calf fat. Visceral fat was markedly increased in FPLD2 and APL subjects.

Conclusion

Semi-automated MRI-based adipose tissue quantification indicates differences between various lipodystrophy types in these studied clinical cases and is a potentially useful tool for extended quantitative phenomic analysis of genetic metabolic disorders. Further studies with a larger sample size are essential for confirming these preliminary findings.

Automated method for accurate abdominal fat quantification on water-saturated magnetic resonance images

PURPOSE

To introduce and evaluate the performance of an automated fat quantification method for water-saturated magnetic resonance images.

MATERIALS AND METHODS

A fat distribution model is proposed for fat quantification on water saturated magnetic resonance images. Fat from both full- and partial-volume voxels are accounted for in this model based on image intensity histogram analysis. An automated threshold method is therefore proposed to accurately quantify total fat. This method is compared to a traditional full-volume-fat-only method in phantom and human studies. In the phantom study, fat quantification was performed on MR images obtained from a human abdomen oil phantom and was compared with the true oil volumes. In the human study, results of the two fat quantification methods of six subjects were compared on abdominal images with different spatial resolutions.

RESULTS

In the phantom study, the proposed method provided significantly more accurate estimations of true oil volumes compared to the reference method (P < 0.0001). In human studies, fat quantification using the proposed method gave much more consistent results on images with different spatial resolutions, and on regions with different degrees of partial volume averaging.

CONCLUSION

The proposed automated method is simple, rapid, and accurate for fat quantification on water-saturated MR images.