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

Sodium triple quantum MR signal extraction using a single-pulse sequence with single quantum time efficiency

PURPOSE

Sodium triple quantum (TQ) signal has been shown to be a valuable biomarker for cell viability. Despite its clinical potential, application of Sodium TQ signal is hindered by complex pulse sequences with long scan times. This study proposes a method to approximate the TQ signal using a single excitation pulse without phase cycling.

METHODS

The proposed method is based on a single excitation pulse and a comparison of the free induction decay (FID) with the integral of the FID combined with a shifting reconstruction window. The TQ signal is calculated from this FID only. As a proof of concept, the method was also combined with a multi-echo UTE imaging sequence on a 9.4 T preclinical MRI scanner for the possibility of fast TQ MRI.

RESULTS

The extracted Sodium TQ signals of single-pulse and spin echo FIDs were in close agreement with theory and TQ measurement by traditional three-pulse sequence (TQ time proportional phase increment [TQTPPI)]. For 2%, 4%, and 6% agar samples, the absolute deviations of the maximum TQ signals between SE and theoretical (time proportional phase increment TQTPPI) TQ signals were less than 1.2% (2.4%), and relative deviations were less than 4.6% (6.8%). The impact of multi-compartment systems and noise on the accuracy of the TQ signal was small for simulated data. The systematic error was <3.4% for a single quantum (SQ) SNR of 5 and at maximum <2.5% for a multi-compartment system. The method also showed the potential of fast in vivo SQ and TQ imaging.

CONCLUSION

Simultaneous SQ and TQ MRI using only a single-pulse sequence and SQ time efficiency has been demonstrated. This may leverage the full potential of the Sodium TQ signal in clinical applications.

AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation

BACKGROUND AND OBJECTIVE

The metabolic syndrome induced by obesity is closely associated with cardiovascular disease, and the prevalence is increasing globally, year by year. Obesity is a risk marker for detecting this disease. However, current research on computer-aided detection of adipose distribution is hampered by the lack of open-source large abdominal adipose datasets.

METHODS

In this study, a benchmark Abdominal Adipose Tissue CT Image Dataset (AATCT-IDS) containing 300 subjects is prepared and published. AATCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks.

RESULTS

In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATCT-IDS reveals three adipose distributions in the subject population.

CONCLUSION

AATCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/AATTCT-IDS/23807256.

Abdominal fat quantification using convolutional networks

OBJECTIVES

To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance-accuracy, reliability, processing effort, and time-in comparison with an interactive reference method.

MATERIALS AND METHODS

Single-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures.

RESULTS

The FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT.

CONCLUSION

The presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification.

CLINICAL RELEVANCE STATEMENT

Deep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity.

KEY POINTS

• This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity. • Supervised deep learning-based methods using fully convolutional networks  were suited best. • Measures of accuracy were equal to or better than the operator-driven approach.

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.

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies

BACKGROUND

There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of obesity is crucial for profiling and management of obesity, as different fat-subcutaneous adipose tissue depots (SAT) and visceral adipose tissue depots (VAT) have various degrees of influence on metabolic syndrome and morbidities. Manual segmentation is time consuming and laborious. Study focuses on the development of a deep learning-based, complete data processing pipeline for MRI-based fat analysis, for large cohort studies which include (1) data augmentation and preprocessing (2) model zoo (3) visualization dashboard, and (4) correction tool, for automated quantification of fat compartments SAT and VAT.

METHODS

Our sample comprised 190 healthy community-dwelling older adults from the Geri-LABS study with mean age of 67.85 ± 7.90 years, BMI 23.75 ± 3.65 kg/m2, 132 (69.5%) female, and mainly Chinese ethnicity. 3D-modified Dixon T1-weighted gradient-echo MR images were acquired. Residual global aggregation-based 3D U-Net (RGA-U-Net) and standard 3D U-Net were trained to segment SAT, VAT, superficial and deep subcutaneous adipose tissue depots (SSAT and DSAT). Manual segmentation from 26 subjects was used as ground truth during training. Data augmentations, random bias, noise and ghosting were carried out to increase the number of training datasets to 130. Segmentation accuracy was evaluated using Dice and Hausdorff metrics.

RESULTS

The accuracy of segmentation was SSAT:0.92, DSAT:0.88 and VAT:0.9. Average Hausdorff distance was less than 5 mm. Automated segmentation significantly correlated R2 > 0.99 (p < 0.001) with ground truth for all 3-fat compartments. Predicted volumes were within ± 1.96SD from Bland-Altman analysis.

CONCLUSIONS

DL-based, comprehensive SSAT, DSAT, and VAT analysis tool showed high accuracy and reproducibility and provided a comprehensive fat compartment composition analysis and visualization in less than 10 s.

Artificial intelligence and abdominal adipose tissue analysis: a literature review

Body composition imaging relies on assessment of tissues composition and distribution. Quantitative data provided by body composition imaging analysis have been linked to pathogenesis, risk, and clinical outcomes of a wide spectrum of diseases, including cardiovascular and oncologic. Manual segmentation of imaging data allows to obtain information on abdominal adipose tissue; however, this procedure can be cumbersome and time-consuming. On the other hand, quantitative imaging analysis based on artificial intelligence (AI) has been proposed as a fast and reliable automatic technique for segmentation of abdominal adipose tissue compartments, possibly improving the current standard of care. AI holds the potential to extract quantitative data from computed tomography (CT) and magnetic resonance (MR) images, which in most of the cases are acquired for other purposes. This information is of great importance for physicians dealing with a wide spectrum of diseases, including cardiovascular and oncologic, for the assessment of risk, pathogenesis, clinical outcomes, response to treatments, and complications. In this review we summarize the available evidence on AI algorithms aimed to the segmentation of visceral and subcutaneous adipose tissue compartments on CT and MR images.

Abdominal adipose tissue components quantification in MRI as a relevant biomarker of metabolic profile

INTRODUCTION

Abnormal accumulation of adipose tissue (AT) alters the metabolic profile and underlies cardiovascular complications. Conventional measures provide global measurements for the entire body. The purpose of this study was to propose a new approach to quantify the amount and type of truncal AT automatically from MRI in metabolic patients and controls.

MATERIALS AND METHODS

DIXON acquisitions were performed at 1.5 T in 30 metabolic syndrome (MS) (59 ± 6 years), 12 obese (50 ± 11 years), 35 type 2 diabetes (T2DM) patients (56 ± 11 years) and 19 controls (52 ± 11 years). AT was segmented into: subcutaneous AT "SAT", visceral AT "VAT", deep VAT "dVAT", peri-organ VAT "pVAT" using active contours and k-means clustering algorithms. Subsequently, organ AT infiltration index "oVAT" was calculated as the normalized fat signal magnitude in organs.

RESULTS

Excellent intra- and inter-operator reproducibility was obtained for AT segmentation. MS and obese patients had the highest amount of total AT. SAT increased in MS (1144 ± 621 g) and T2DM patients (1024 ± 634 g), and twice the level of SAT in controls (505 ± 238 g), and further increased in obese patients (1429 ± 621 g). While VAT, pVAT and dVAT increased to a similar degree in the metabolic patients compared to controls, the oVAT index was able to differentiate controls from MS and T2DM patients and to discriminate the three metabolic patient groups (p < 0.01). Local AT sub-types were not related to BMI in all groups except for SAT in controls (p = 0.03).

CONCLUSION

Reproducible truncal AT sub-types quantification using 3D MRI was able to characterize patients with metabolic diseases. It may serve in the future as a non-invasive predictor of cardiovascular complications in such patients.

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

Background

Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.

Methods

Ethical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3 days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.

Results

The accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p < 0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of ± 20%.

Conclusions

The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.

Cardiovascular-related proteins and the abdominal visceral to subcutaneous adipose tissue ratio

BACKGROUND AND AIMS

An increased amount of visceral adipose tissues has been related to atherosclerosis and future cardiovascular events. The present study aims to investigate how the abdominal fat distribution links to plasma levels of cardiovascular-related proteins.

METHOD AND RESULTS

In the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (n = 326, all aged 50 years), abdominal visceral (VAT) and subcutaneous (SAT) adipose tissue volumes were quantified by MRI. Eighty-six cardiovascular-related proteins were measured by the proximity extension assay (PEA). Similar investigations were carried out in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (n = 400, all aged 75 years). In the discovery dataset (POEM), 10 proteins were related to the VAT/SAT-ratio using false discovery rate <.05. Of those, Cathepsin D (CTSD), Interleukin-1 receptor antagonist protein (IL-1RA) and Growth hormone (GH) (inversely) were related to the VAT/SAT-ratio in the validation in PIVUS following adjustment for sex, BMI, smoking, education level and exercise habits (p < 0.05). In a secondary analysis, a meta-analysis of the two samples suggested that 15 proteins could be linked to the VAT/SAT-ratio following adjustment as above and Bonferroni-correction of the p-value.

CONCLUSION

Three cardiovascular-related proteins, cathepsin D, IL-1RA and growth hormone, were being associated with the distribution of abdominal adipose tissue using a discovery/validation approach. A meta-analysis of the two samples suggested that also a number of other cardiovascular-related proteins could be associated with an unfavorable abdominal fat distribution.

Quantification of body composition in renal cell carcinoma patients: Comparing computed tomography and magnetic resonance imaging measurements

PURPOSE

Body composition measures provide valuable information for prognostication and treatment election in cancer patients. We investigated the novel use of magnetic resonance imaging (MRI) for skeletal muscle and adipose tissue cross-sectional area measurements in preoperative renal cell carcinoma (RCC) patients.

MATERIALS AND METHODS

RCC patients with pre-operative CT and MRI abdominal imaging were identified. Semi-automatic segmentation measurement of skeletal muscle area (SMA), intramuscular fat area (IMFA), visceral fat area (VFA), subcutaneous fat area (SFA), linear measurements of psoas, paraspinal muscles were performed. Pearson correlation coefficients, Bland-Altman plot analyses were done. Multivariable regression analysis examined the relationship between patient characteristics and skeletal muscle.

RESULTS

Image analysis was performed on 58 RCC patients with preoperative CT and MRI imaging. For segmentation measures, r = 0.99, 0.99, 0.99, and 0.98 for SMA, IMFA, VFA, SFA, respectively, and 0.96 for linear measures of skeletal muscle. Bland-Altman analysis revealed a bias toward larger CT value for SMA (1.35 %), linear muscle measures (2.79 %), and SFA (10.34 %), and toward larger MRI values for IMFA (0.75 %) and VFA (5.81 %). ECOG ≥ 1 was associated with lower skeletal muscle than ECOG 0 for all measurements.

CONCLUSIONS

Strong correlation of CT and MRI cross sectional measurements of skeletal muscle and adipose tissues supports the use of axial MRI images for comprehensive measurement of body composition. This has widespread implications for body composition research and cancer patient care.

Adipokine Resistin Levels at Time of Pediatric Crohn Disease Diagnosis Predict Escalation to Biologic Therapy

BACKGROUND

Hypertrophy of visceral adipose tissue (VAT) is a hallmark of Crohn disease (CD). The VAT produces a wide range of adipokines, biologically active factors that contribute to metabolic disorders in addition to CD pathogenesis. The study aim was to concomitantly evaluate serum adipokine profiles and VAT volumes as predictors of disease outcomes and treatment course in newly diagnosed pediatric patients with CD.

METHODS

Pediatric patients ages 6 to 20 years were enrolled, and their clinical data and anthropometric measurements were obtained. Adipokine levels were measured at 0, 6, and 12 months after CD diagnosis and baseline in control patients (CP). The VAT volumes were measured by magnetic resonance imaging or computed tomography imaging within 3 months of diagnosis.

RESULTS

One hundred four patients undergoing colonoscopy were prospectively enrolled: 36 diagnosed with CD and 68 CP. The serum adipokine resistin and plasminogen activator inhibitor (PAI)-1 levels were significantly higher in patients with CD at diagnosis than in CP. The VAT volume was similar between CD and CP. Baseline resistin levels at the time of diagnosis in patients with CD who were escalated to biologics was significantly higher than in those not treated using biologic therapy by 12 months (29.8 ng/mL vs 13.8 ng/mL; P = 0.004). A resistin level of ≥29.8 ng/mL at the time of diagnosis predicted escalation to biologic therapy in the first year after diagnosis with a specificity of 95% (sensitivity = 53%; area under the curve = 0.82; P = 0.015 for model with log-scale). There was a significantly greater reduction in resistin (P = 0.002) and PAI-1 (P = 0.010) at the 12-month follow-up in patients on biologics compared with patients who were not treated using biologics.

CONCLUSIONS

Serum resistin levels at diagnosis of pediatric CD predict the escalation to biologic therapy at 12 months, independent of VAT volumes. Resistin and PAI-1 levels significantly improved in patients with CD after treatment using biologics compared with those not on biologics. These results suggest the utility of resistin as a predictive biomarker in pediatric CD.

FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI

PURPOSE

Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans.

METHODS

FatSegNet is composed of three stages: (a) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (b) Segmentation of adipose tissue on three views by independent CDFNets, and (c) View aggregation. FatSegNet is validated by: (1) comparison of segmentation accuracy (sixfold cross-validation), (2) test-retest reliability, (3) generalizability to randomly selected manually re-edited cases, and (4) replication of age and sex effects in the Rhineland Study-a large prospective population cohort.

RESULTS

The CDFNet demonstrates increased accuracy and robustness compared to traditional deep learning networks. FatSegNet Dice score outperforms manual raters on VAT (0.850 vs. 0.788) and produces comparable results on SAT (0.975 vs. 0.982). The pipeline has excellent agreement for both test-retest (ICC VAT 0.998 and SAT 0.996) and manual re-editing (ICC VAT 0.999 and SAT 0.999).

CONCLUSIONS

FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in ∼1 minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study.

Prediction of pelvic tumour coverage by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) from referral imaging

BACKGROUND

Patient suitability for magnetic resonance-guided high intensity focused ultrasound (MRgHIFU) ablation of pelvic tumors is initially evaluated clinically for treatment feasibility using referral images, acquired using standard supine diagnostic imaging, followed by MR screening of potential patients lying on the MRgHIFU couch in a 'best-guess' treatment position. Existing evaluation methods result in ≥40% of referred patients being screened out because of tumor non-targetability. We hypothesize that this process could be improved by development of a novel algorithm for predicting tumor coverage from referral imaging.

METHODS

The algorithm was developed from volunteer images and tested with patient data. MR images were acquired for five healthy volunteers and five patients with recurrent gynaecological cancer. Subjects were MR imaged supine and in oblique-supine-decubitus MRgHIFU treatment positions. Body outline and bones were segmented for all subjects, with organs-at-risk and tumors also segmented for patients. Supine images were aligned with treatment images to simulate a treatment dataset. Target coverage (of patient tumors and volunteer intra-pelvic soft tissue), i.e. the volume reachable by the MRgHIFU focus, was quantified. Target coverage predicted from supine imaging was compared to that from treatment imaging.

RESULTS

Mean (±standard deviation) absolute difference between supine-predicted and treatment-predicted coverage for 5 volunteers was 9 ± 6% (range: 2-22%) and for 4 patients, was 12 ± 7% (range: 4-21%), excluding a patient with poor acoustic coupling (coverage difference was 53%).

CONCLUSION

Prediction of MRgHIFU target coverage from referral imaging appears feasible, facilitating further development of automated evaluation of patient suitability for MRgHIFU.

Reproducibility of myelin content-based human habenula segmentation at 3 Tesla

In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis.

Automatic Measurement of the Total Visceral Adipose Tissue From Computed Tomography Images by Using a Multi-Atlas Segmentation Method

BACKGROUND

The visceral adipose tissue (VAT) volume is a predictive and/or prognostic factor for many cancers. The objective of our study was to develop an automatic measurement of the whole VAT volume using a multi-atlas segmentation (MAS) method from a computed tomography.

METHODS

A total of 31 sets of whole-body computed tomography volume data were used. The reference VAT volume was defined on the basis of manual segmentation (VATMANUAL). We developed an algorithm, which measured automatically the VAT volumes using a MAS based on a nonrigid volume registration algorithm coupled with a selective and iterative method for performance level estimation (SIMPLE), called VATMAS_SIMPLE. The results were evaluated using intraclass correlation coefficient and dice similarity coefficients.

RESULTS

The intraclass correlation coefficient of VATMAS_SIMPLE was excellent, at 0.976 (confidence interval, 0.943-0.989) (P < 0.001). The dice similarity coefficient of VATMAS_SIMPLE was also good, at 0.905 (SD, 0.076).

CONCLUSIONS

This newly developed algorithm based on a MAS can measure accurately the whole abdominopelvic VAT.

Assessment of alveolar bone marrow fat content using 15 T MRI

OBJECTIVES

Bone marrow fat is inversely correlated with bone mineral density. The aim of this study is to present a method to quantify alveolar bone marrow fat content using a 15 T magnetic resonance imaging (MRI) scanner.

STUDY DESIGN

A 15 T MRI scanner with a 13-mm inner diameter loop-gap radiofrequency coil was used to scan seven 3-mm diameter alveolar bone biopsy specimens. A 3-D gradient-echo relaxation time (T1)-weighted pulse sequence was chosen to obtain images. All images were obtained with a voxel size (58 µm³) sufficient to resolve trabecular spaces. Automated volume of the bone marrow fat content and derived bone volume fraction (BV/TV) were calculated. Results were compared with actual BV/TV obtained from micro-computed tomography (CT) scans.

RESULTS

Mean fat tissue volume was 20.1 ± 11%. There was a significantly strong inverse correlation between fat tissue volume and BV/TV (r = -0.68; P = .045). Furthermore, there was a strong agreement between BV/TV derived from MRI and obtained with micro-CT (interclass correlation coefficient = 0.92; P = .001).

CONCLUSIONS

Bone marrow fat of small alveolar bone biopsy specimens can be quantified with sufficient spatial resolution using an ultra-high-field MRI scanner and a T1-weighted pulse sequence.

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.

Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

Muscle Tissue Labeling of Human Lower Limb in Multi-Channel mDixon MR Imaging: Concepts and Applications

With increasing resolutions and number of acquisitions, medical imaging more and more requires computer support for interpretation as currently not all imaging data is fully used. In our work, we show how multi-channel images can be used for robust air masking and reliable muscle tissue detection in the human lower limb. We exploit additional channels that are usually discarded in clinical routine. We use the common mDixon acquisition protocol for MR imaging. A series of thresholding, morphological, and connectivity operations is used for processing. We demonstrate our fully automated approach on four subjects and present a comparison with manual labeling. We discuss how this work is used for advanced and intuitive visualization, the quantification of tissue types, pose estimation, initialization of further segmentation methods, and how it could be used in clinical environments.

Automated quantification of abdominal adiposity by magnetic resonance imaging

OBJECTIVES

To develop a fully-automated algorithm to process axial magnetic resonance imaging (MRI) slices for quantifying abdominal visceral, subcutaneous and total adipose tissues, i.e., VAT, SAT, and TAT, without human intervention or prior knowledge.

MATERIALS AND METHODS

Fat regions in single MRI slice or sequence (20 slices) were identified with image processing techniques including region-growing, inhomogeneity correction, fuzzy c-means clustering, and active contours segmentation. The MR images of 85 subjects (60 males and 25 females), whose body mass index (BMI) values ranged from 19.96 to 40.35 kg/m2 , were analyzed using the fully-automated algorithm-the automatic method developed in the research and the widely used semi-automated software (sliceOmatic® Tomovision, Inc.)-the reference method.

RESULTS

The proposed automated method showed good performance against the reference method to quantify adipose tissues in both single umbilical slice and MRI sequence. The square of the Pearson correlation coefficients (R2 ) based on the results generated from the two methods for VAT/SAT/TAT were 0.977/0.998/0.997 for single slice data and 0.995/0.999/0.999 for volumetric data. The intra-class correlation of visceral adipose tissue (VAT) between the three operators was 0.939 in the reference method, which was improved to 0.999 in the automatic method. The adipose tissue measurements in the slice at Lumbar 3 vertebra have the highest correlation with the total fat volumes across the entire abdomen.

CONCLUSION

The fully-automated algorithm presented in the paper provides an accurate and reliable assessment of abdominal fat without human intervention. Am. J. Hum. Biol. 28:757-766, 2016. © 2016Wiley Periodicals, Inc.

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.

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.

MR-based assessment of body fat distribution and characteristics

The assessment of body fat distribution and characteristics using magnetic resonance (MR) methods has recently gained significant attention as it further extends our pathophysiological understanding of diseases including obesity, metabolic syndrome, or type 2 diabetes mellitus, and allows more detailed insights into treatment response and effects of lifestyle interventions. Therefore, the purpose of this study was to review the current literature on MR-based assessment of body fat distribution and characteristics. PubMed search was performed to identify relevant studies on the assessment of body fat distribution and characteristics using MR methods. T1-, T2-weighted MR Imaging (MRI), Magnetic Resonance Spectroscopy (MRS), and chemical shift-encoding based water-fat MRI have been successfully used for the assessment of body fat distribution and characteristics. The relationship of insulin resistance and serum lipids with abdominal adipose tissue (i.e. subcutaneous and visceral adipose tissue), liver, muscle, and bone marrow fat content have been extensively investigated and may help to understand the underlying pathophysiological mechanisms and the multifaceted obese phenotype. MR methods have also been used to monitor changes of body fat distribution and characteristics after interventions (e.g. diet or physical activity) and revealed distinct, adipose tissue-specific properties. Lastly, chemical shift-encoding based water-fat MRI can detect brown adipose tissue which is currently the focus of intense research as a potential treatment target for obesity. In conclusion, MR methods reliably allow the assessment of body fat distribution and characteristics. Irrespective of the promising findings based on these MR methods the clinical usefulness remains to be established.

Computerized Automated Quantification of Subcutaneous and Visceral Adipose Tissue From Computed Tomography Scans: Development and Validation Study

Background

Computed tomography (CT) is often viewed as one of the most accurate methods for measuring visceral adipose tissue (VAT). However, measuring VAT and subcutaneous adipose tissue (SAT) from CT is a time-consuming and tedious process. Thus, evaluating patients’ obesity levels during clinical trials using CT scans is both cumbersome and limiting.

Objective

To describe an image-processing-based and automated method for measuring adipose tissue in the entire abdominal region.

Methods

The method detects SAT and VAT levels using a separation mask based on muscles of the human body. The separation mask is the region that minimizes the unnecessary space between a closed path and muscle area. In addition, a correction mask, based on bones, corrects the error in VAT.

Results

To validate the method, the volume of total adipose tissue (TAT), SAT, and VAT were measured for a total of 100 CTs using the automated method, and the results compared with those from manual measurements obtained by 2 experts. Dice’s similarity coefficients (DSCs) between the first manual measurement and the automated result for TAT, SAT, and VAT are 0.99, 0.98, and 0.97, respectively. The DSCs between the second manual measurement and the automated result for TAT, SAT, and VAT are 0.98, 0.98, and 0.97, respectively. Moreover, intraclass correlation coefficients (ICCs) between the automated method and the results of the manual measurements indicate high reliability as the ICCs for the items are all .99 (P<.001).

Conclusions

The results described in this paper confirm the accuracy and reliability of the proposed method. The method is expected to be both convenient and useful in the clinical evaluation and study of obesity in patients who require SAT and VAT measurements.

Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle

The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.

Strong and persistent effect on liver fat with a Paleolithic diet during a two-year intervention

BACKGROUND/OBJECTIVES

Our objective was to investigate changes in liver fat and insulin sensitivity during a 2-year diet intervention. An ad libitum Paleolithic diet (PD) was compared with a conventional low-fat diet (LFD).

SUBJECTS/METHODS

Seventy healthy, obese, postmenopausal women were randomized to either a PD or a conventional LFD. Diet intakes were ad libitum. Liver fat was measured with proton magnetic resonance spectroscopy. Insulin sensitivity was evaluated with oral glucose tolerance tests and calculated as homeostasis model assessment-insulin resistance (HOMA-IR)/liver insulin resistance (Liver IR) index for hepatic insulin sensitivity and oral glucose insulin sensitivity (OGIS)/Matsuda for peripheral insulin sensitivity. All measurements were performed at 0, 6 and 24 months. Forty-one women completed the examinations for liver fat and were included.

RESULTS

Liver fat decreased after 6 months by 64% (95% confidence interval: 54-74%) in the PD group and by 43% (27-59%) in the LFD group (P<0.01 for difference between groups). After 24 months, liver fat decreased 50% (25-75%) in the PD group and 49% (27-71%) in the LFD group. Weight reduction between baseline and 6 months was correlated to liver fat improvement in the LFD group (rs=0.66, P<0.01) but not in the PD group (rs=0.07, P=0.75). Hepatic insulin sensitivity improved during the first 6 months in the PD group (P<0.001 for Liver IR index and HOMA-IR), but deteriorated between 6 and 24 months without association with liver fat changes.

CONCLUSIONS

A PD with ad libitum intake had a significant and persistent effect on liver fat and differed significantly from a conventional LFD at 6 months. This difference may be due to food quality, for example, a higher content of mono- and polyunsaturated fatty acids in the PD. Changes in liver fat did not associate with alterations in insulin sensitivity.

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.

Serum Triglyceride Levels Independently Contribute to the Estimation of Visceral Fat Amount Among Nondiabetic Obese Adults

Determining the visceral fat amount is important in the risk stratification for the prevention of type 2 diabetes and obesity-related disorders. The area-based measurement of visceral fat area (VFA) via magnetic resonance imaging (MRI) is an accurate but expensive and time-consuming method for estimating visceral fat amount. The aim of our study was to identify a practical predictive parameter for visceral obesity in clinical settings. In this cross-sectional study, we recruited 51 nondiabetic obese (body mass index [BMI] ≥ 27 kg/m²) adults in Taiwan (21 men and 30 women, mean age 35.6 ± 9.2 years, mean BMI 33.3 ± 3.9 kg/m²). VFA was quantified by a single-slice MRI image. Anthropometric indices and biochemical parameters including fasting plasma glucose, serum level of alanine aminotransferase, and lipid profiles were measured. The associations between different variables and VFA were analyzed by linear regression analysis. Increases in BMI, waist circumference, serum levels of alanine aminotransferase and triglycerides (TGs), and decreased serum levels of high-density lipoprotein cholesterol were correlated with larger VFA. After adjustment for age, sex, and anthropometric indices, only serum TG level remained as an independent correlate of VFA. Besides demographic and anthropometric indices, adding TG level may explain a greater variance of VFA. In stepwise multivariate regression analysis, male sex, age, waist circumference, and serum TG level remained significant predictors of VFA. In a subgroup analysis among subjects with BMI ≥30 kg/m², similar results were demonstrated and serum TG level remained as significant independent correlates of VFA in all of the predictive models. Among nondiabetic obese adults, serum TG level was positively associated with VFA. The combination of sex, age, anthropometric indices, and serum TG level may be used to estimate VFA in clinical settings.

Accuracy and reproducibility of adipose tissue measurements in young infants by whole body magnetic resonance imaging

PURPOSE

MR might be well suited to obtain reproducible and accurate measures of fat tissues in infants. This study evaluates MR-measurements of adipose tissue in young infants in vitro and in vivo.

MATERIAL AND METHODS

MR images of ten phantoms simulating subcutaneous fat of an infant's torso were obtained using a 1.5T MR scanner with and without simulated breathing. Scans consisted of a cartesian water-suppression turbo spin echo (wsTSE) sequence, and a PROPELLER wsTSE sequence. Fat volume was quantified directly and by MR imaging using k-means clustering and threshold-based segmentation procedures to calculate accuracy in vitro. Whole body MR was obtained in sleeping young infants (average age 67±30 days). This study was approved by the local review board. All parents gave written informed consent. To obtain reproducibility in vivo, cartesian and PROPELLER wsTSE sequences were repeated in seven and four young infants, respectively. Overall, 21 repetitions were performed for the cartesian sequence and 13 repetitions for the PROPELLER sequence.

RESULTS

In vitro accuracy errors depended on the chosen segmentation procedure, ranging from 5.4% to 76%, while the sequence showed no significant influence. Artificial breathing increased the minimal accuracy error to 9.1%. In vivo reproducibility errors for total fat volume of the sleeping infants ranged from 2.6% to 3.4%. Neither segmentation nor sequence significantly influenced reproducibility.

CONCLUSION

With both cartesian and PROPELLER sequences an accurate and reproducible measure of body fat was achieved. Adequate segmentation was mandatory for high accuracy.

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.

Relationship between dual-energy X-ray absorptiometry volumetric assessment and X-ray computed tomography-derived single-slice measurement of visceral fat

To reduce radiation exposure and cost, visceral adipose tissue (VAT) measurement on X-ray computed tomography (CT) has been limited to a single slice. Recently, the US Food and Drug Administration has approved a dual-energy X-ray absorptiometry (DXA) application validated against CT to measure VAT volume. The purpose of this study was to develop an algorithm to compute single-slice area values on DXA at 2 common landmarks, L2/3 and L4/5, from an automated volumetrically derived measurement of VAT. Volumetric CT and total body DXA were measured in 55 males (age: 21-77 yr; body mass index [BMI]: 21.1-37.9) and 60 females (age: 21-85 yr; BMI: 20.0-39.7). Equations were developed by applying the relationship of CT single-slice area and volume measurements of VAT to the DXA VAT volume measure as well as validating these against the CT single-slice measurements. Correlation coefficients between DXA estimate of single-slice area and CT were 0.94 for L2/3 and 0.96 for L4/5. The mean difference between DXA estimate of single-slice area and CT was 5 cm(2) at L2/3 and 3.8 cm(2) at L4/5. Bland-Altman analysis showed a fairly constant difference across the single-slice range in this study, and the 95% limits of agreement for the 2 methods were -44.6 to +54.6 cm(2) for L2/3 and -47.3 to +54.9 cm(2) for L4/5. In conclusion, a volumetric measurement of VAT by DXA can be used to estimate single-slice measurements at the L2/3 and the L4/5 landmarks.

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.

Software for automated MRI-based quantification of abdominal fat and preliminary evaluation in morbidly obese patients

PURPOSE

To present software for supervised automatic quantification of visceral and subcutaneous adipose tissue (VAT, SAT) and evaluates its performance in terms of reliability, interobserver variation, and processing time, since fully automatic segmentation of fat-fraction magnetic resonance imaging (MRI) is fast but susceptible to anatomical variations and artifacts, particularly for advanced stages of obesity.

MATERIALS AND METHODS

Twenty morbidly obese patients (average BMI 44 kg/m(2) ) underwent 1.5-T MRI using a double-echo gradient-echo sequence. Fully automatic analysis (FAA) required no user interaction, while supervised automatic analysis (SAA) involved review and manual correction of the FAA results by two observers. Standard of reference was provided by manual segmentation analysis (MSA).

RESULTS

Average processing times per patient were 6, 6+4, and 21 minutes for FAA, SAA, and MSA (P < 0.001), respectively. For VAT/SAT assessment, Pearson correlation coefficients, mean (bias), and standard deviations of the differences were R = 0.950, +0.003, and 0.043 between FAA and MSA and R = 0.981, +0.009, and 0.027 between SAA and MSA. Interobserver variation and intraclass correlation were 3.1% and 0.996 for SAA, and 6.6% and 0.986 for MSA, respectively.

CONCLUSION

The presented supervised automatic approach provides a reliable option for MRI-based fat quantification in morbidly obese patients and was much faster than manual analysis.

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.

Magnetic resonance or computerized tomography imaging to predict difficulty of robotic surgery for endometrial cancer

To determine if the difficulty of a robotic hysterectomy for endometrial cancer can be predicted by MRI, CT or other methods. All robotic cases from 1 August 2006 through 30 July 2009 were identified. Data collected prospectively included co-morbidities, body mass index, surgical times, estimated blood loss (EBL), uterine weight, and pre- and postoperative complications. Those patients who received an MRI or CT scan prior to robotic hysterectomy had additional data gathered from imaging, including uterine volume, pelvic measurements and abdominal wall thickness. Cases were labeled difficult for the following reasons: outliers greater than 2 SD from the mean EBL, hysterectomy time and total console time. Additional factors identifying difficult cases included the need to undock to remove the specimen or conversion to laparotomy. Data were analyzed for their possible role in causing difficulty in a robotic hysterectomy. Comparative statistics utilized included chi-square and t-test, ANOVA and logistic regression analysis.From 2 August 2006 through 30 July 2009, 119 patients underwent robotic surgery for endometrial cancer and are included in this study. Of these patients, 25/119 (20.0%) were identified as difficult cases. Difficulty was found in those patients with greater than 2 SD from the mean for hysterectomy time, >90.9 min (n = 3, 2.5%), total console time of >178.1 min (n = 6, 5.0%), EBL >232 cc (n = 7, 5.9%) and undocking to remove the uterine specimen in 8 (6.7%) cases; 1/119 (0.8%) was converted to laparotomy. Lymphadenectomy (P = 0.005) was associated with case difficulty. In a logistic regression analysis CT/MRI measurements of uterine volume greater than 793 cm³ and CT/MRI pelvimetry, as well as abdominal wall thickness were independent predictors of a difficult case (P = 0.0116). MRI and CT scans can detect the probability that a robotic surgery will be difficult by determining uterine volume and pelvimetry; however, these were not the strongest predictors in our study. A narrow pelvic outlet as measured on CT/MRI and uterine volume of greater than 793 cc should raise a flag of caution when planning robotic hysterectomy for endometrial cancer.

Impaired insulin sensitivity as indexed by the HOMA score is associated with deficits in verbal fluency and temporal lobe gray matter volume in the elderly

OBJECTIVE

Impaired insulin sensitivity is linked to cognitive deficits and reduced brain size. However, it is not yet known whether insulin sensitivity involves regional changes in gray matter volume. Against this background, we examined the association between insulin sensitivity, cognitive performance, and regional gray matter volume in 285 cognitively healthy elderly men and women aged 75 years from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study.

RESEARCH DESIGN AND METHODS

Insulin sensitivity was calculated from fasting serum insulin and plasma glucose determinations using the homeostasis model assessment of insulin resistance (HOMA-IR) method. Cognitive performance was examined by a categorical verbal fluency. Participants also underwent a magnetic resonance imaging (MRI) brain scan. Multivariate analysis using linear regression was conducted, controlling for potential confounders (sex, education, serum LDL cholesterol, mean arterial blood pressure, and abdominal visceral fat volume).

RESULTS

The HOMA-IR was negatively correlated with verbal fluency performance, brain size, and temporal lobe gray matter volume in regions known to be involved in speech production (Brodmann areas 21 and 22, respectively). No such effects were observed when examining diabetic (n = 55) and cognitively impaired (n = 27) elderly subjects as separate analyses.

CONCLUSIONS

These cross-sectional findings suggest that both pharmacologic and lifestyle interventions improving insulin signaling may promote brain health in late life but must be confirmed in patient studies.

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.

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 human body fat tissue percentage by MRI

The MRI-based evaluation of the quantity and regional distribution of adipose tissue is one objective measure in the investigation of obesity. The aim of this article was to report a comprehensive and automatic analytical method for the determination of the volumes of subcutaneous fat tissue (SFT) and visceral fat tissue (VFT) in either the whole human body or selected slices or regions of interest. Using an MRI protocol in an examination position that was convenient for volunteers and patients with severe diseases, 22 healthy subjects were examined. The software platform was able to merge MRI scans of several body regions acquired in separate acquisitions. Through a cascade of image processing steps, SFT and VFT volumes were calculated. Whole-body SFT and VFT distributions, as well as fat distributions of defined body slices, were analysed in detail. Complete three-dimensional datasets were analysed in a reproducible manner with as few operator-dependent interventions as possible. In order to determine the SFT volume, the ARTIS (Adapted Rendering for Tissue Intensity Segmentation) algorithm was introduced. The advantage of the ARTIS algorithm was the delineation of SFT volumes in regions in which standard region grow techniques fail. Using the ARTIS algorithm, an automatic SFT volume detection was feasible. MRI data analysis was able to determine SFT and VFT volume percentages using new analytical strategies. With the techniques described, it was possible to detect changes in SFT and VFT percentages of the whole body and selected regions. The techniques presented in this study are likely to be of use in obesity-related investigations, as well as in the examination of longitudinal changes in weight during various medical conditions.

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.