Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging

NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

PURPOSE

This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA).

METHODS

We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared.

RESULTS

The nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945-0.979], skin: 0.997 [95CI: 0.984-0.999], tumor: 0.926 [95CI: 0.508-0.968], and ventricles: 0.910 [95CI: 0.812-0.968]. Compared to the MGA's median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000-0.926], and ventricles: 0.856 [95CI: 0.216-0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0-1616]) than the MGA (mean: 2851 s [95CI: 1482-6246]).

CONCLUSIONS

The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.

Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework

PURPOSE

Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework.

MATERIALS AND METHODS

This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader.

RESULTS

There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons.

CONCLUSION

We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.

A pilot study of visceral fat and its association with adipokines, stool calprotectin and symptoms in patients with diverticulosis

BACKGROUND

Complications of diverticular disease are increasingly common, possibly linked to increasing obesity. Visceral fat could contribute to the development of symptomatic diverticular disease through its pro-inflammatory effects.

OBJECTIVE

The study had 2 aims. A) to develop a semi-automated algorithm to measure abdominal adipose tissue from 2-echo magnetic resonance imaging (MRI) data; B) to use this to determine if visceral fat was associated with bowel symptoms and inflammatory markers in patients with symptomatic and asymptomatic diverticular disease.

DESIGN

An observational study measuring visceral fat using MRI together with serum adiponectin, leptin, stool calprotectin and patient-reported somatisation and bowel habit.

SETTING

Medical and imaging research centres of a university hospital.

PARTICIPANTS

MRI scans were performed on 55 patients after an overnight fast measuring abdominal subcutaneous and visceral adipose tissue volumes together with small bowel water content (SBWC). Blood and stool samples were collected and patients kept a 2 week stool diary and completed a somatisation questionnaire.

MAIN OUTCOME MEASURES

Difference in the volume of visceral fat between symptomatic and asymptomatic patients.

RESULTS

There were no significant differences in visceral (p = 0.98) or subcutaneous adipose (p = 0.60) tissue between symptomatic and asymptomatic patients. However measured fat volumes were associated with serum adipokines. Adiponectin showed an inverse correlation with visceral adipose tissue (VAT) (Spearman ρ = -0.5, p = 0.0003), which correlated negatively with SBWC (ρ = -0.3, p = 0.05). Leptin correlated positively with subcutaneous adipose tissue (ρ = 0.8, p < 0.0001). Overweight patients (BMI > 25 kgm-2) showed a moderate correlation between calprotectin and VAT (ρ = 0.3, p = 0.05). Somatization scores were significantly higher in symptomatic patients (p < 0.0003).

CONCLUSIONS

Increasing visceral fat is associated with lower serum adiponectin and increased faecal calprotectin suggesting a pro-inflammatory effect which may predispose to the development of complications of diverticulosis.

Association between carbohydrate intake and body composition: The Korean National Health and Nutrition Examination Survey

OBJECTIVE

Several studies have investigated the effects of dietary carbohydrate intake on body composition. However, the results are controversial and few studies have been conducted on an Asian population. The aim of this study was to investigate whether dietary carbohydrate intake is associated with body composition in Korean adults.

METHODS

The present study used data from the Korean National Health and Nutrition Examination Survey, a cross-sectional survey of Korean civilians, conducted from 2008 to 2011. The study analyzed 9594 participants. Carbohydrate intake was defined as the proportion of energy consumed from carbohydrate. Waist circumference, body mass index, and lean and fat mass using a whole-body dual-energy x-ray absorptiometry scanner were measured as body composition parameters.

RESULTS

After adjusting for age, household income, smoking, alcohol consumption, physical activity, history of diabetes, hypertension, dyslipidemia, and intake of energy and fiber per day, the proportion of carbohydrate intake was positively correlated with total limb lean mass in men (β = 0.141, P = 0.046), and in women, the proportion of carbohydrate intake was positively associated with appendicular skeletal muscle mass index (β = 0804, P = 0.003) but negatively associated with trunk fat percentage (β = -0.075, P = 0.026). Total limb lean mass and appendicular skeletal muscle mass index in women showed an increasing trend as the proportion of carbohydrate intake increased.

CONCLUSIONS

No positive association was found between the proportion of carbohydrate intake and any measure of obesity or body fat mass in either men or women. Further studies are needed to evaluate the effects of quantity and quality of carbohydrate intake on body composition.

DXA-measured visceral fat mass and lean body mass reflect abnormal metabolic phenotypes among some obese and nonobese Chinese children and adolescents

BACKGROUND AND AIM

The exact constellation of body composition characteristics among metabolically unhealthy obese (MUO) and nonobese (MUNO) children and adolescents remains unclear. The purpose of this study was to identify the major body composition determinants of metabolically unhealthy phenotypes among Chinese children and adolescents.

METHODS AND RESULTS

We used data from a cross-sectional survey in 2015 that included 1983 children and adolescents aged 6-18 years. Subjects were classified into two phenotypes based on a combination of body mass index (BMI) and metabolic syndrome components. Body composition was measured by dual-energy X-ray absorptiometry (DXA). Among all boys and among adolescent boys, those with MUNO phenotypes displayed significantly higher indices of body composition except for fat mass (FM) percentage and trunk-to-legs FM ratio compared with the metabolically healthy nonobese phenotype (all P < 0.05). MUO individuals had higher arm FM, lean body mass (LBM), and trunk lean mass compared to metabolically healthy obese individuals (all P < 0.05). Visceral fat mass (VFM) and BMI were the major independent determinants of MUNO (VFM, 6- to 9-year-old boys, OR = 1.02, 95% CI = 1.00-1.03, P = 0.021; BMI, 6- to 9-year-old girls, OR = 1.90, 95% CI = 1.31-2.84, P = 0.001; and adolescent boys, OR = 1.34, 95% CI = 1.23-1.44, P < 0.001). LBM was the major independent predictor of MUO among adolescent boys (OR = 1.90, 95% CI = 1.03-1.17, P = 0.003).

CONCLUSIONS

Among children and adolescents, the metabolically unhealthy phenotype was associated with excess of body composition, but with significant differences observed based on age and sex. VFM and LBM derived by DXA can predict the metabolically unhealthy phenotype effectively in specific sex and age groups.

Automated segmentation of abdominal subcutaneous adipose tissue and visceral adipose tissue in obese adolescent in MRI

PURPOSE

To develop a reliable and reproducible automatic technique to segment and measure SAT and VAT based on MRI.

MATERIALS AND METHODS

Chemical-shift water-fat MRI were taken on twelve obese adolescents (mean age: 16.1±0.6, BMI: 31.3±2.3) recruited under the health monitoring program. The segmentation applied a spoke template created using Midpoint Circle algorithm followed by Bresenham's Line algorithm to detect narrow connecting regions between subcutaneous and visceral adipose tissues. Upon satisfaction of given constrains, a cut was performed to separate SAT and VAT. Bone marrow was consisted in pelvis and femur. By using the intensity difference in T2*, a mask was created to extract bone marrow adipose tissue (MAT) from VAT. Validation was performed using a semi-automatic method. Pearson coefficient, Bland-Altman plot and intra-class coefficient (ICC) were applied to measure accuracy and reproducibility.

RESULTS

Pearson coefficient indicated that results from the proposed method achieved high correlation with the semi-automatic method. Bland-Altman plot and ICC showed good agreement between the two methods. Lowest ICC was obtained in VAT segmentation at lower regions of the abdomen while the rests were all above 0.80. ICC (0.98-0.99) also indicated the proposed method performed good reproducibility.

CONCLUSION

No user interaction was required during execution of the algorithm and the segmented images and volume results were given as output. This technique utilized the feature in the regions connecting subcutaneous and visceral fat and T2* intensity difference in bone marrow to achieve volumetric measurement of various types of adipose tissue in abdominal site.

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.

Preliminary study on the assessment of visceral adipose tissue using dual-energy x-ray absorptiometry in chronic obstructive pulmonary disease

Background

Visceral adipose tissue (VAT) was shown to be increased in patients with chronic obstructive pulmonary disease (COPD) compared to control subjects with comparable body mass index (BMI). Our aim was to determine the relation of VAT by dual-energy x-ray absorptiometry (DEXA) in patients with COPD by disease severity, BMI, other indices of body composition and static lung volumes.

Methods

294 COPD patients admitted for rehabilitation were studied. Lung function, static lung volumes and body composition (i.e. BMI, waist circumference, fat-free mass, fat mass and fat distribution between android and gynoid fat mass) were assessed before entering pulmonary rehabilitation. VAT was estimated within the android region by using DEXA. Patients were stratified for gender, BMI (cut-off of 25 kg/m²) and GOLD stage. To assess the impact of VAT on lung volumes, patients were also stratified for VAT less and above 50^th percentile.

Results

Both male and female patients with more severe airflow limitation had significantly lower VAT values, but these differences disappeared after stratification for BMI. VAT was significantly and strongly correlated with other body composition parameters (all p < 0.001). Patients with moderate to severe airflow limitation and lower VAT had increased static lung hyperinflation and lower diffusing capacity for carbon monoxide. Nevertheless, multivariate stepwise regression models including for BMI, age, gender and forced expiratory volume in 1 s (FEV₁) as confounders did not confirm an independent role for VAT on static lung hyperinflation and diffusion capacity.

Conclusion

After stratification for BMI, VAT is comparable in moderate to very severe COPD patients. Furthermore, BMI and demographics, but not VAT, were independent predictors of static lung hyperinflation and diffusing capacity in COPD.

Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome

Background

To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome.

Methods

A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients.

Results

The correlation coefficients between automated and radiologist’s manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients’ PFS (p < 0.01).

Conclusion

Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.

Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients

The present study aims to quantitatively measure adiposity-related image features and to test the feasibility of applying multivariate statistical data analysis-based prediction models to generate a novel clinical marker and predict the benefit of epithelial ovarian cancer (EOC) patients with and without maintenance bevacizumab-based chemotherapy. A dataset involving computed tomography (CT) images acquired from 59 patients diagnosed with advanced EOC was retrospectively collected. Among them, 32 patients received maintenance bevacizumab following primary chemotherapy, while 27 did not. A computer-aided detection scheme was developed to automatically segment visceral and subcutaneous fat areas depicted on CT images of abdominal sections, and 7 adiposity-related image features were computed. Upon combining these features with the measured body mass index, multivariate data analyses were performed using three statistical models (multiple linear, logistic and Cox proportional hazards regressions) to analyze the association between the model-generated prediction results and the treatment outcome, including progression-free survival (PFS) and overall survival (OS) of the patients. The results demonstrated that applying all three prediction models yielded a significant association between the adiposity-related image features and patients' PFS or OS in the group of the patients who received maintenance bevacizumab (P<0.010), while there was no significant difference when these prediction models were applied to predict both PFS and OS in the group of patients that did not receive maintenance bevacizumab. Therefore, the present study demonstrated that the use of a quantitative adiposity-related image feature-based statistical model may generate a novel clinical marker to predict who will benefit among EOC patients receiving maintenance bevacizumab-based chemotherapy.

Abdominal fat distribution and carotid atherosclerosis in a general population: a semi-automated method using magnetic resonance imaging

PURPOSE

Available evidence suggests functional differences in visceral and subcutaneous fat. We investigated the association between quantitative measures of central adiposity with indicators of carotid atherosclerosis including intima-media thickness (IMT) and plaque in a general population using a semi-automated method on magnetic resonance imaging (MRI) data.

METHODS

In this cross-sectional study 200 subjects (52 % female), aged 50-77 years, were randomly selected from Golestan Cohort Study. Participants underwent ultrasound examination of carotid arteries and abdominal MRI. Segmentation and calculation of visceral (VFA) and subcutaneous fat area (SFA) were performed on three levels using semi-automated software. Various conventional anthropometric indices were also measured.

RESULTS

Among 191 enrolled subjects, 77 (40 %) participants had IMT ≥0.8 mm. Carotid plaques were detected in 86 (44 %) subjects. In separate multivariate analysis models, unlike SFA and other anthropometric indices, the last tertile of VFA values was associated with at least threefold excess risk for IMT ≥0.8 mm (OR 3.8, 95 % CI 1.36-6.94, p = 0.02). There was no significant difference between mean values of categorized obesity indices in subjects with and without plaque, while participants in the highest tertile of VFA values were demonstrated to have higher risk of more than one plaque (OR 4.57, 95 % CI 1.03-20.11, p = 0.034).

CONCLUSIONS

A higher amount of visceral fat, measured by a semi-automated technique using MRI, is associated with increased IMT and having more than one carotid plaque in a general population, while subcutaneous fat measures are poor indicators for identifying carotid atherosclerosis.

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.

DXA-measured visceral adipose tissue predicts impaired glucose tolerance and metabolic syndrome in obese Caucasian and African-American women

BACKGROUND/OBJECTIVES

New methods to measure visceral adipose tissue (VAT) by dual-energy X-ray absorptiometry (DXA) may help discern sex, race and phenotype differences in the role of VAT in cardiometabolic risk. This study was designed (1) to compare relationships of DXA-VAT, anthropometric and body composition variables with cardiometabolic risk factors in obese women; (2) to determine which variables most robustly predict impaired glucose tolerance (IGT) and metabolic syndrome (MetSx); and (3) to determine thresholds for DXA-VAT by race.

SUBJECTS/METHODS

VAT mass (g) and volume (cm(3)) were measured in 229 obese (body mass index (BMI), 30-49.9) women aged 21-69 years of European-American (EA=123) and African-American (AA=106) descent using the CoreScan algorithm on a Lunar iDXA scanner. Linear regression modeling and areas under the curve (AUC of ROC (receiver operating characteristic) curves) compared relationships with cardiometabolic risk. Bootstrapping with LASSO (least absolute shrinkage and selection operator) regression modeling determined thresholds and predictors of IGT and MetSx.

RESULTS

DXA-VAT explained more of the variance in triglycerides, blood pressure, glucose and homeostatic model assessment-insulin resistance (HOMA-IR) compared with anthropometric and other body composition variables. DXA-VAT also had the highest AUC for IGT (0.767) and MetSx (0.749). Including race as a variable and the interaction between VAT and race in modeling did not significantly change the results. Thresholds at which the probability of developing IGT or MetSx was⩾50% were determined separately for AA women (IGT: 2120 cm(3); MetSx: 1320 cm(3)) and EA women (IGT: 2550 cm(3); MetSx: 1713 cm(3)). The odds for IGT or MetSx were fourfold greater with each standard deviation increase in DXA-VAT.

CONCLUSIONS

DXA-VAT provides robust clinical information regarding cardiometabolic risk in AA and EA obese women and offers potential utility in the risk reduction interventions.

Whole-body MRI-based fat quantification: a comparison to air displacement plethysmography

PURPOSE

To demonstrate the feasibility of an algorithm for MRI whole-body quantification of internal and subcutaneous fat and quantitative comparison of total adipose tissue to air displacement plethysmography (ADP).

MATERIALS AND METHODS

For comparison with ADP, whole-body MR data of 11 volunteers were obtained using a continuously moving table Dixon sequence. Resulting fat images were corrected for B1 related intensity inhomogeneities before fat segmentation.

RESULTS

The performed MR measurements of the whole body provided a direct comparison to ADP measurements. The segmentation of subcutaneous and internal fat in the abdomen worked reliably with an accuracy of 98%. Depending on the underlying model for fat quantification, the resultant MR fat masses represent an upper and a lower limit for the true fat masses. In comparison to ADP, the results were in good agreement with ρ ≥ 0.97, P < 0.0001.

CONCLUSION

Whole-body fat quantities derived noninvasively by using a continuously moving table Dixon acquisition were directly compared with ADP. The accuracy of the method and the high reproducibility of results indicate its potential for clinical applications.

Development of automatic visceral fat volume calculation software for CT volume data

OBJECTIVE

To develop automatic visceral fat volume calculation software for computed tomography (CT) volume data and to evaluate its feasibility.

METHODS

A total of 24 sets of whole-body CT volume data and anthropometric measurements were obtained, with three sets for each of four BMI categories (under 20, 20 to 25, 25 to 30, and over 30) in both sexes. True visceral fat volumes were defined on the basis of manual segmentation of the whole-body CT volume data by an experienced radiologist. Software to automatically calculate visceral fat volumes was developed using a region segmentation technique based on morphological analysis with CT value threshold. Automatically calculated visceral fat volumes were evaluated in terms of the correlation coefficient with the true volumes and the error relative to the true volume.

RESULTS

Automatic visceral fat volume calculation results of all 24 data sets were obtained successfully and the average calculation time was 252.7 seconds/case. The correlation coefficients between the true visceral fat volume and the automatically calculated visceral fat volume were over 0.999.

CONCLUSIONS

The newly developed software is feasible for calculating visceral fat volumes in a reasonable time and was proved to have high accuracy.

Quantitative proton MR techniques for measuring fat

Accurate, precise and reliable techniques for the quantification of body and organ fat distributions are important tools in physiology research. They are critically needed in studies of obesity and diseases involving excess fat accumulation. Proton MR methods address this need by providing an array of relaxometry-based (T1, T2) and chemical shift-based approaches. These techniques can generate informative visualizations of regional and whole-body fat distributions, yield measurements of fat volumes within specific body depots and quantify fat accumulation in abdominal organs and muscles. MR methods are commonly used to investigate the role of fat in nutrition and metabolism, to measure the efficacy of short- and long-term dietary and exercise interventions, to study the implications of fat in organ steatosis and muscular dystrophies and to elucidate pathophysiological mechanisms in the context of obesity and its comorbidities. The purpose of this review is to provide a summary of mainstream MR strategies for fat quantification. The article succinctly describes the principles that differentiate water and fat proton signals, summarizes the advantages and limitations of various techniques and offers a few illustrative examples. The article also highlights recent efforts in the MR of brown adipose tissue and concludes by briefly discussing some future research directions.

Quantification of visceral adipose tissue using lunar dual-energy X-ray absorptiometry in Asian Chinese

OBJECTIVE

To evaluate the new DXA VAT method on an Asian Chinese population by comparing to a reference method, computed tomography (CT).

DESIGN AND METHODS

In total, 145 adult men and women volunteers, representing a wide range of ages (19-83 years) and BMI values (18.5-39.3 kg/m(2) ) were studied with both DXA and CT.

RESULTS

The coefficient of determination (r(2) ) for regression of CT on DXA values was 0.947 for females, 0.891 for males and 0.915 combined. The 95% confidence interval for r was 0.940-0.969 for the combined data. The Bland-Altman test showed a VAT bias (CT as standard method) of 143 cm(3) for females and 379 cm(3) for males. Combined, the bias was 262 cm(3) with 95% limits of agreement of -232 to 755 cm(3) . While the current DXA method moderately overestimates the VAT volume for the study subjects, a further analysis suggested that the overestimation could be largely contributed to VAT movement due to breath-holding status.

CONCLUSIONS

For Asian Chinese, VAT measured with DXA is highly correlated to VAT measured with CT. Validation of the DXA VAT tool using a reference method (e.g., CT) needs to carefully control the breath-holding protocol.

Body fat assessment method using CT images with separation mask algorithm

In recent years, the number of obese population in Korea has been growing up along with the economic development, environmental factors, and the change in life style. Considering the growth of obese population and the adverse effect of obesity on health, it is getting more important to prevent and diagnose the obesity with the quantitative measurement of body fat that has become an important indicator for obesity. In this study, we proposed a procedure for the automated fat assessment from computed tomography (CT) data using image processing technique. The proposed method was applied to a single-CT image as well as CT-volume data, and results were correlated to those of dual-energy X-ray absorptiometry (DEXA) that is known as the reliable method for evaluating body fat. Using single-CT images, correlation coefficients between DEXA and the automated assessment and DEXA and the manual assessment were 0.038 and 0.058, respectively (P > 0.05). Hence, there was no significant correlation between three methods using the proposed method with single-CT images. On the other hand, in case of CT-volume data, the above correlation coefficients were increased to 0.826, 0.812, and 0.805, respectively (P < 0.01). Thus, DEXA and the proposed methods with CT-volume data showed highly significant correlation with each other. The results suggest that the proposed automated assessment using CT-volume data is a reliable method for the evaluation of body fat. It is expected that the clinical application of the proposed procedure will be helpful to reduce the time for the quantitative evaluation of patient's body fat.

Whole body fat: content and distribution

Obesity and its co-morbidities, including type II diabetes, insulin resistance and cardiovascular diseases, have become one of the biggest health issues of present times. The impact of obesity goes well beyond the individual and is so far-reaching that, if it continues unabated, it will cause havoc with the economies of most countries. In order to be able to fully understand the relationship between increased adiposity (obesity) and its co-morbidity, it has been necessary to develop proper methodology to accurately and reproducibly determine both body fat content and distribution, including ectopic fat depots. Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) have recently emerged as the gold-standard for accomplishing this task. Here, we will review the use of different MRI techniques currently being used to determine body fat content and distribution. We also discuss the pros and cons of MRS to determine ectopic fat depots in liver, muscle, pancreas and heart and compare these to emerging MRI techniques currently being put forward to create ectopic fat maps. Finally, we will discuss how MRI/MRS techniques are helping in changing the perception of what is healthy and what is normal and desirable body-fat content and distribution.

Adipose tissue MRI for quantitative measurement of central obesity

PURPOSE

To validate adipose tissue magnetic resonance imaging (atMRI) for rapid, quantitative volumetry of visceral adipose tissue (VAT) and total adipose tissue (TAT).

MATERIALS AND METHODS

Data were acquired on normal adults and clinically overweight girls with Institutional Review Board (IRB) approval/parental consent using sagittal 6-echo 3D-spoiled gradient-echo (SPGR) (26-sec single-breath-hold) at 3T. Fat-fraction images were reconstructed with quantitative corrections, permitting measurement of a physiologically based fat-fraction threshold in normals to identify adipose tissue, for automated measurement of TAT, and semiautomated measurement of VAT. TAT accuracy was validated using oil phantoms and in vivo TAT/VAT measurements validated with manual segmentation. Group comparisons were performed between normals and overweight girls using TAT, VAT, VAT-TAT-ratio (VTR), body-mass-index (BMI), waist circumference, and waist-hip-ratio (WHR).

RESULTS

Oil phantom measurements were highly accurate (<3% error). The measured adipose fat-fraction threshold was 96% ± 2%. VAT and TAT correlated strongly with manual segmentation (normals r(2) ≥ 0.96, overweight girls r(2) ≥ 0.99). VAT segmentation required 30 ± 11 minutes/subject (14 ± 5 sec/slice) using atMRI, versus 216 ± 73 minutes/subject (99 ± 31 sec/slice) manually. Group discrimination was significant using WHR (P < 0.001) and VTR (P = 0.004).

CONCLUSION

The atMRI technique permits rapid, accurate measurements of TAT, VAT, and VTR.

Dual-energy X-ray absorptiometry for quantification of visceral fat

Obesity is the major risk factor for metabolic syndrome and through it diabetes as well as cardiovascular disease. Visceral fat (VF) rather than subcutaneous fat (SF) is the major predictor of adverse events. Currently, the reference standard for measuring VF is abdominal X-ray computed tomography (CT) or magnetic resonance imaging (MRI), requiring highly used clinical equipment. Dual-energy X-ray absorptiometry (DXA) can accurately measure body composition with high-precision, low X-ray exposure, and short-scanning time. The purpose of this study was to validate a new fully automated method whereby abdominal VF can be measured by DXA. Furthermore, we explored the association between DXA-derived abdominal VF and several other indices for obesity: BMI, waist circumference, waist-to-hip ratio, and DXA-derived total abdominal fat (AF), and SF. We studied 124 adult men and women, aged 18-90 years, representing a wide range of BMI values (18.5-40 kg/m(2)) measured with both DXA and CT in a fasting state within a one hour interval. The coefficient of determination (r(2)) for regression of CT on DXA values was 0.959 for females, 0.949 for males, and 0.957 combined. The 95% confidence interval for r was 0.968 to 0.985 for the combined data. The 95% confidence interval for the mean of the differences between CT and DXA VF volume was -96.0 to -16.3 cm(3). Bland-Altman bias was +67 cm(3) for females and +43 cm(3) for males. The 95% limits of agreement were -339 to +472 cm(3) for females and -379 to +465 cm(3) for males. Combined, the bias was +56 cm(3) with 95% limits of agreement of -355 to +468 cm(3). The correlations between DXA-derived VF and BMI, waist circumference, waist-to-hip ratio, and DXA-derived AF and SF ranged from poor to modest. We conclude that DXA can measure abdominal VF precisely in both men and women. This simple noninvasive method with virtually no radiation can therefore be used to measure VF in individual patients and help define diabetes and cardiovascular risk.

Evaluation of adipose tissue volume quantification with IDEAL fat-water separation

PURPOSE

To validate iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) for adipose tissue volume quantification. IDEAL allows MRI images to be produced only from adipose-containing tissues; hence, quantifying adipose tissue should be simpler and more accurate than with current methods.

MATERIALS AND METHODS

Ten healthy controls were imaged with 1.5 Tesla (T) Spin Echo (SE), 3.0T T1-weighted spoiled gradient echo (SPGR), and 3.0T IDEAL-SPGR. Images were acquired from the abdomen, pelvis, mid-thigh, and mid-calf. Mean subcutaneous and visceral adipose tissue volumes were compared between the three acquisitions for each subject.

RESULTS

There were no significant differences (P>0.05) between the three acquisitions for subcutaneous adipose tissue volumes. However, there was a significant difference (P=0.0002) for visceral adipose tissue volumes in the abdomen. Post hoc analysis showed significantly lower visceral adipose tissue volumes measured by IDEAL versus 1.5T (P<0.0001) and 3.0T SPGR (P<0.002). The lower volumes given by IDEAL are due to its ability to differentiate true visceral adipose tissue from other bright structures like blood vessels and bowel content that are mistaken for adipose tissue in non-fat suppressed images.

CONCLUSION

IDEAL measurements of adipose tissue are equivalent to established 1.5T measurement techniques for subcutaneous depots and have improved accuracy for visceral depots, which are more metabolically relevant.

Compensation of RF field and receiver coil induced inhomogeneity effects in abdominal MR images by a priori knowledge on the human adipose tissue distribution

PURPOSE

To reliably compensate bias field effects in abdominal areas to accurately quantify visceral adipose tissue using standard T1-weighted sequences on MR scanners with up to 3 Tesla (T) field strength.

MATERIALS AND METHODS

Compensation is achieved in two steps: The bias field is first estimated by picking and fitting sampling points from the subcutaneous adipose tissue, using active contours and a thin plate fitting spline. Then, additional sampling points from visceral adipose tissue compartments are detected by thresholding and the bias field estimation is refined. It was compared with an established method using a simulated abdominal image and real 3T data.

RESULTS

At low bias field amplitudes (40-50%), the simulation study showed a good reduction of the mean coefficients of variance (CV) for both approaches (>80%). At higher amplitudes, the CV reduction was significantly higher for our approach (83.6%), compared with LEMS (54.3%). In the real data study, our approach showed reliable reduction of the inhomogeneities, while the LEMS algorithm sometimes even amplified the inhomogeneities.

CONCLUSION

The proposed method enables accurate and reliable segmentation of abdominal adipose tissue using simple thresholding techniques, even in severely corrupted images slices, obtained when using high field strengths and/or phased-array coils.

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.

Follow-up whole-body assessment of adipose tissue compartments during a lifestyle intervention in a large cohort at increased risk for type 2 diabetes

PURPOSE

To assess adipose body compartments with magnetic resonance (MR) imaging and MR spectroscopy during a lifestyle intervention program that included optimized nutrition and controlled physical activity in subjects at increased risk for type 2 diabetes to determine factors that may help predict an increase in insulin sensitivity following the intervention.

MATERIALS AND METHODS

This prospective study was approved by the local review board. All participants gave written informed consent. MR imaging and MR spectroscopy were performed in 243 subjects (99 men and 144 women) before and 9 months after enrollment in a lifestyle intervention program. The results of whole-body MR imaging were used to calculate tissue profiles, differentiating between adipose tissue--especially visceral adipose tissue--and lean tissue. The concentration of hepatic lipids and intramyocellular lipids in the anterior tibial and soleus muscles was determined with MR spectroscopy, and insulin sensitivity was estimated by using an oral glucose tolerance test. The Student t test was used to assess differences between groups, and multivariate regression models were used to assess the value of adipose tissue compartments in the prediction of insulin sensitivity.

RESULTS

Compared with women, men had almost twice the amount of visceral adipose tissue and a smaller amount of total adipose tissue (25.9% for men and 36.9% for women) at baseline. In addition, their insulin sensitivity was significantly lower than that of women. The most pronounced changes in adipose tissue were detected for visceral adipose tissue (from 4.9 L to 4.1 L [ie, -15.1%] in men and from 2.3 L to 1.9 L [ie, -15.8%] in women) and hepatic lipids (from 8.6% to 5.4% [ie, -36.8%] in men and from 5.1% to 4.3% [ie, -16.5%] in women). The mean insulin sensitivity improved significantly (from 11.3 arbitrary units [au] to 14.6 au [ie, +29.9%] in men and from 13.6 au to 14.6 au [ie, +7.5%] in women), with 70 of the 99 men (71%) and 84 of 144 women (58%) showing an increase in insulin sensitivity. In men, low concentrations of visceral adipose tissue, hepatic lipids, and abdominal subcutaneous fat at baseline were predictive of successful intervention in terms of changes in insulin sensitivity; in women, only low hepatic lipid levels were significantly predictive of successful intervention.

CONCLUSION

Visceral adipose tissue and hepatic lipids, as assessed with MR imaging and MR spectroscopy, can be significantly reduced during lifestyle intervention. Their baseline values emerged as predictive factors for an improvement of insulin sensitivity.

Topography mapping of whole body adipose tissue using A fully automated and standardized procedure

PURPOSE

To obtain quantitative measures of human body fat compartments from whole body MR datasets for the risk estimation in subjects prone to metabolic diseases without the need of any user interaction or expert knowledge.

MATERIALS AND METHODS

Sets of axial T1-weighted spin-echo images of the whole body were acquired. The images were segmented using a modified fuzzy c-means algorithm. A separation of the body into anatomic regions along the body axis was performed to define regions with visceral adipose tissue present, and to standardize the results. In abdominal image slices, the adipose tissue compartments were divided into subcutaneous and visceral compartments using an extended snake algorithm. The slice-wise areas of different tissues were plotted along the slice position to obtain topographic fat tissue distributions.

RESULTS

Results from automatic segmentation were compared with manual segmentation. Relatively low mean deviations were obtained for the class of total tissue (4.48%) and visceral adipose tissue (3.26%). The deviation of total adipose tissue was slightly higher (8.71%).

CONCLUSION

The proposed algorithm enables the reliable and completely automatic creation of adipose tissue distribution profiles of the whole body from multislice MR datasets, reducing whole examination and analysis time to less than half an hour.

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.

Phase sensitive reconstruction for water/fat separation in MR imaging using inverse gradient

This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

OBJECTIVE

To examine five available software packages for the assessment of abdominal adipose tissue with magnetic resonance imaging, compare their features and assess the reliability of measurement results.

DESIGN

Feature evaluation and test-retest reliability of softwares (NIHImage, SliceOmatic, Analyze, HippoFat and EasyVision) used in manual, semi-automated or automated segmentation of abdominal adipose tissue.

SUBJECTS

A random sample of 15 obese adults with type 2 diabetes.

MEASUREMENTS

Axial T1-weighted spin echo images centered at vertebral bodies of L2-L3 were acquired at 1.5 T. Five software packages were evaluated (NIHImage, SliceOmatic, Analyze, HippoFat and EasyVision), comparing manual, semi-automated and automated segmentation approaches. Images were segmented into cross-sectional area (CSA), and the areas of visceral (VAT) and subcutaneous adipose tissue (SAT). Ease of learning and use and the design of the graphical user interface (GUI) were rated. Intra-observer accuracy and agreement between the software packages were calculated using intra-class correlation. Intra-class correlation coefficient was used to obtain test-retest reliability.

RESULTS

Three of the five evaluated programs offered a semi-automated technique to segment the images based on histogram values or a user-defined threshold. One software package allowed manual delineation only. One fully automated program demonstrated the drawbacks of uncritical automated processing. The semi-automated approaches reduced variability and measurement error, and improved reproducibility. There was no significant difference in the intra-observer agreement in SAT and CSA. The VAT measurements showed significantly lower test-retest reliability. There were some differences between the software packages in qualitative aspects, such as user friendliness.

CONCLUSION

Four out of five packages provided essentially the same results with respect to the inter- and intra-rater reproducibility. Our results using SliceOmatic, Analyze or NIHImage were comparable and could be used interchangeably. Newly developed fully automated approaches should be compared to one of the examined software packages.

Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

OBJECTIVES

(1) To develop a fully automated algorithm for segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), excluding intermuscular adipose tissue (IMAT) and bone marrow (BM), from axial abdominal magnetic resonance imaging (MRI) data. (2) To evaluate the algorithm accuracy and total method reproducibility using a semi-automatically segmented reference and data from repeated measurements.

BACKGROUND

MRI is a widely used in adipose tissue (AT) assessment. Manual analysis of MRI data is time consuming and biased by the operator. Automated analysis spares resources and increase reproducibility. Fully automated algorithms have been presented. However, reproducibility analysis has not been performed nor has methods for exclusion of IMAT and BM been presented.

METHODS

In total, 49 data sets from 31 subjects were acquired using a clinical 1.5 T MRI scanner. Thirteen data sets were used in the derivation of the automated algorithm and 36 were used in the validation. Common image analysis tools such as thresholding, morphological operations and geometrical models were used to segment VAT and SAT. Accuracy was assessed using a semi-automatically created reference. Reproducibility was assessed from repeated measurements.

RESULTS

Resulting AT volumes from the automated analysis and the reference were not found to differ significantly (2.0+/-14% and 0.84+/-2.7%, given as mean+/-s.d., for VAT and SAT, respectively). The automated analysis of the repeated measurements data significantly increased the reproducibility of the VAT measurements. One athletic subject with very small amounts of AT was considered to be an outlier.

CONCLUSIONS

An automated method for segmentation of VAT and SAT and exclusion of IMAT and BM from abdominal MRI data has been reported. The accuracy and reproducibility of the method has also been demonstrated using a semi-automatically segmented reference and analysis of repeated acquisitions. The accuracy of the method is limited in lean subjects.

Visceral and subcutaneous adiposity measurements in adults: influence of measurement site

OBJECTIVE

Excess abdominal adiposity is a known risk factor for cardiovascular diseases. Computed tomography can be used to examine the visceral (VAT) and subcutaneous (SAT) components of abdominal adiposity, but it is unresolved whether single-slice or multi-slice protocols are needed.

RESEARCH METHOD AND PROCEDURES

Nine computed tomography scans were obtained in the lumbar spine region of 24 adults. The nine slices were obtained at three intervertebral positions (L2-L3, L3-L4, and L4-L5) and at 7 mm above and below these locations. Intra-site and inter-site differences in SAT, VAT, total adipose tissue, and the VAT/SAT ratio were examined using ANOVA and confidence intervals for pairwise differences between means.

RESULTS

Intervertebral SAT values increased from 103.1 +/- 50.9 (standard deviation) cm(2) at L2-L3 to 153.3 +/- 68.8 cm(2) at L4-L5, whereas the corresponding VAT values decreased from 164.3 +/- 125.4 to 126.0 +/- 82.7 cm(2). The VAT/SAT ratio was not constant, decreasing from 1.8 +/- 1.4 to 0.9 +/- 0.7. Repeated-measures ANOVA indicated significant inter- and intra-site differences (p </= 0.02) for SAT, VAT, and the VAT/SAT ratio at L3-L4 and L4-L5 (p < 0.001).

DISCUSSION

These differences show the limitation of using a single-slice assessment of abdominal fat distribution, both for a subject and between subjects. Furthermore, the sizeable differences in the intra-site scans indicate that precise repositioning is needed for longitudinal studies. In summary, our findings suggest that a multi-site imaging protocol may provide a more complete assessment of abdominal fat stores and distribution than use of a single site.

Semi-automated segmentation and quantification of adipose tissue in calf and thigh by MRI: a preliminary study in patients with monogenic metabolic syndrome

BACKGROUND

With the growing prevalence of obesity and metabolic syndrome, reliable quantitative imaging methods for adipose tissue are required. Monogenic forms of the metabolic syndrome include Dunnigan-variety familial partial lipodystrophy subtypes 2 and 3 (FPLD2 and FPLD3), which are characterized by the loss of subcutaneous fat in the extremities. Through magnetic resonance imaging (MRI) of FPLD patients, we have developed a method of quantifying the core FPLD anthropometric phenotype, namely adipose tissue in the mid-calf and mid-thigh regions.

METHODS

Four female subjects, including an FPLD2 subject (LMNA R482Q), an FPLD3 subject (PPARG F388L), and two control subjects were selected for MRI and analysis. MRI scans of subjects were performed on a 1.5T GE MR Medical system, with 17 transaxial slices comprising a 51 mm section obtained in both the mid-calf and mid-thigh regions. Using ImageJ 1.34 n software, analysis of raw MR images involved the creation of a connectedness map of the subcutaneous adipose tissue contours within the lower limb segment from a user-defined seed point. Quantification of the adipose tissue was then obtained after thresholding the connected map and counting the voxels (volumetric pixels) present within the specified region.

RESULTS

MR images revealed significant differences in the amounts of subcutaneous adipose tissue in lower limb segments of FPLD3 and FPLD2 subjects: respectively, mid-calf, 15.5% and 0%, and mid-thigh, 25.0% and 13.3%. In comparison, old and young healthy controls had values, respectively, of mid-calf, 32.5% and 26.2%, and mid-thigh, 52.2% and 36.1%. The FPLD2 patient had significantly reduced subcutaneous adipose tissue compared to FPLD3 patient.

CONCLUSION

Thus, semi-automated quantification of adipose tissue of the lower extremity can detect differences between individuals of various lipodystrophy genotypes and represents a potentially useful tool for extended quantitative phenotypic analysis of other genetic metabolic disorders.