Segmentation and quantification of adipose tissue by magnetic resonance imaging

Quantification of Visceral Fat at the L5 Vertebral Body Level in Patients with Crohn's Disease Using T2-Weighted MRI

The umbilical or L3 vertebral body level is often used for body fat quantification using computed tomography. To explore the feasibility of using clinically acquired pelvic magnetic resonance imaging (MRI) for visceral fat measurement, we examined the correlation of visceral fat parameters at the umbilical and L5 vertebral body levels. We retrospectively analyzed T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) MR axial images from Crohn's disease patients who underwent MRI enterography of the abdomen and pelvis over a three-year period. We determined the area/volume of subcutaneous and visceral fat from the umbilical and L5 levels and calculated the visceral fat ratio (VFR = visceral fat/subcutaneous fat) and visceral fat index (VFI = visceral fat/total fat). Statistical analyses involved correlation analysis between both levels, inter-rater analysis between two investigators, and inter-platform analysis between two image-analysis platforms. Correlational analysis of 32 patients yielded significant associations for VFI (r = 0.85; p < 0.0001) and VFR (r = 0.74; p < 0.0001). Intraclass coefficients for VFI and VFR were 0.846 and 0.875 (good agreement) between investigators and 0.831 and 0.728 (good and moderate agreement) between platforms. Our study suggests that the L5 level on clinically acquired pelvic MRIs may serve as a reference point for visceral fat quantification.

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Hepatocellular carcinoma in patients with non-alcoholic fatty liver disease is associated with heterogeneous pattern of fat infiltration in skeletal muscles

OBJECTIVES

To evaluate the association between fat infiltration in skeletal muscles (myosteatosis) and hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD).

METHODS

In a cross-sectional cohort of 72 histologically proven NAFLD patients (n = 38 with non-alcoholic steatohepatitis; NASH), among which 20 had HCC diagnosed on biopsy, we used proton density fat fraction (PDFF) at MRI to evaluate myosteatosis in skeletal muscles (mean fat fraction and first order radiomic-based pattern) at the third lumbar level, namely in erector spinae (ES), quadratus lumborum (QL), psoas, oblique, and rectus muscles.

RESULTS

PDFFES was 70% higher in patients with HCC when compared to those without HCC (9.6 ± 5.5% versus 5.7 ± 3.0%, respectively, p < 0.001). In multivariate logistic regression, PDFFES was a significant predictor of the presence of HCC (AUC = 0.72, 95% CI 0.57-0.86, p = 0.002) independently from age, sex, visceral fat area, and liver fibrosis stage (all p < 0.05). The relationship between PDFFES and HCC was exacerbated in patients with NASH (AUC = 0.79, 95% CI 0.63-0.86, p = 0.006). In patients with NASH, radiomics features of heterogeneity such as energy and entropy in any of the paraspinal muscles (i.e., ES, QL, or psoas) were independent predictors of HCC. EnergyES identified patients with HCC (n = 13) in the NASH population with AUC = 0.92 (95% CI 0.82-1.00, p < 0.001).

CONCLUSION

In patients with NAFLD, and more specifically in those with NASH, the degree and heterogeneity of myosteatosis is independently associated with HCC irrespective of liver fibrosis stage.

CLINICAL RELEVANCE STATEMENT

Our data suggest that myosteatosis could be used as a biomarker of HCC in the ever-expanding NAFLD population and pave the way for further investigation in longitudinal studies.

KEY POINTS

• HCC in patients with non-alcoholic fatty liver disease, and more specifically in those with non-alcoholic steatohepatitis, is independently associated with severe fatty infiltration (myosteatosis) of paravertebral skeletal muscles. • Association between myosteatosis and HCC is independent from liver fibrosis stage. • Histogram-based radiomics features of myosteatosis predicts the risk of HCC in patients with non-alcoholic steatohepatitis.

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs

OBJECTIVE

Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.

MATERIALS AND METHODS

920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).

RESULTS

ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis.

DISCUSSION

ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

Fat Matters: Exploring Cancer Risk through the Lens of Computed Tomography and Visceral Adiposity

Obesity is an established risk factor for cancer. However, conventional measures like body mass index lack precision in assessing specific tissue quantities, particularly of the two primary abdominal fat compartments, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Computed tomography (CT) stands as the gold standard for precisely quantifying diverse tissue types. VAT, distinguished by heightened hormonal and metabolic activity, plays a pivotal role in obesity-related tumor development. Excessive VAT is linked to aberrant secretion of adipokines, proinflammatory cytokines, and growth factors, fostering the carcinogenesis of obesity-related tumors. Accurate quantification of abdominal fat compartments is crucial for understanding VAT as an oncological risk factor. The purpose of the present research is to elucidate the role of CT, performed for staging purposes, in assessing VAT (quantity and distribution) as a critical factor in the oncogenesis of obesity-related tumors. In the field of precision medicine, this work takes on considerable importance, as quantifying VAT in oncological patients becomes fundamental in understanding the influence of VAT on cancer development-the potential "phenotypic expression" of excessive VAT accumulation. Previous studies analyzed in this research showed that VAT is a risk factor for clear cell renal cell carcinoma, non-clear cell renal cell carcinoma, prostate cancer, and hepatocarcinoma recurrence. Further studies will need to quantify VAT in other oncological diseases with specific mutations or gene expressions, in order to investigate the relationship of VAT with tumor genomics.

Chronic Insufficient Sleep in Women Impairs Insulin Sensitivity Independent of Adiposity Changes: Results of a Randomized Trial

OBJECTIVE

Insufficient sleep is associated with type 2 diabetes, yet the causal impact of chronic insufficient sleep on glucose metabolism in women is unknown. We investigated whether prolonged mild sleep restriction (SR), resembling real-world short sleep, impairs glucose metabolism in women.

RESEARCH DESIGN AND METHODS

Women (aged 20-75 years) without cardiometabolic diseases and with actigraphy-confirmed habitual total sleep time (TST) of 7-9 h/night were recruited to participate in this randomized, crossover study with two 6-week phases: maintenance of adequate sleep (AS) and 1.5 h/night SR. Outcomes included plasma glucose and insulin levels, HOMA of insulin resistance (HOMA-IR) values based on fasting blood samples, as well as total area under the curve for glucose and insulin, the Matsuda index, and the disposition index from an oral glucose tolerance test.

RESULTS

Our sample included 38 women (n = 11 postmenopausal women). Values are reported with ±SEM. Linear models adjusted for baseline outcome values demonstrated that TST was reduced by 1.34 ± 0.04 h/night with SR versus AS (P < 0.0001). Fasting insulin (β = 6.8 ± 2.8 pmol/L; P = 0.016) and HOMA-IR (β = 0.30 ± 0.12; P = 0.016) values were increased with SR versus AS, with effects on HOMA-IR more pronounced in postmenopausal women compared with premenopausal women (β = 0.45 ± 0.25 vs. β = 0.27 ± 0.13, respectively; P for interaction = 0.042). Change in adiposity did not mediate the effects of SR on glucose metabolism or change results in the full sample when included as a covariate.

CONCLUSIONS

Curtailing sleep duration to 6.2 h/night, reflecting the median sleep duration of U.S. adults with short sleep, for 6 weeks impairs insulin sensitivity, independent of adiposity. Findings highlight insufficient sleep as a modifiable risk factor for insulin resistance in women to be targeted in diabetes prevention efforts.

Body Composition Evaluation and Clinical Markers of Cardiometabolic Risk in Patients with Phenylketonuria

Cardiovascular diseases are the main cause of mortality worldwide. Patients with phenylketonuria (PKU) may be at increased cardiovascular risk. This review provides an overview of clinical and metabolic cardiovascular risk factors, explores the connections between body composition (including fat mass and ectopic fat) and cardiovascular risk, and examines various methods for evaluating body composition. It particularly focuses on nutritional ultrasound, given its emerging availability and practical utility in clinical settings. Possible causes of increased cardiometabolic risk in PKU are also explored, including an increased intake of carbohydrates, chronic exposure to amino acids, and characteristics of microbiota. It is important to evaluate cardiovascular risk factors and body composition in patients with PKU. We suggest systematic monitoring of body composition to develop nutritional management and hydration strategies to optimize performance within the limits of nutritional therapy.

A fully convolutional neural network for comprehensive compartmentalization of abdominal adipose tissue compartments in MRI

BACKGROUND

Existing literature has highlighted structural, physiological, and pathological disparities among abdominal adipose tissue (AAT) sub-depots. Accurate separation and quantification of these sub-depots are crucial for advancing our understanding of obesity and its comorbidities. However, the absence of clear boundaries between the sub-depots in medical imaging data has challenged their separation, particularly for internal adipose tissue (IAT) sub-depots. To date, the quantification of AAT sub-depots remains challenging, marked by a time-consuming, costly, and complex process.

PURPOSE

To implement and evaluate a convolutional neural network to enable granular assessment of AAT by compartmentalization of subcutaneous adipose tissue (SAT) into superficial subcutaneous (SSAT) and deep subcutaneous (DSAT) adipose tissue, and IAT into intraperitoneal (IPAT), retroperitoneal (RPAT), and paraspinal (PSAT) adipose tissue.

MATERIAL AND METHODS

MRI datasets were retrospectively collected from Singapore Preconception Study for Long-Term Maternal and Child Outcomes (S-PRESTO: 389 women aged 31.4 ± 3.9 years) and Singapore Adult Metabolism Study (SAMS: 50 men aged 28.7 ± 5.7 years). For all datasets, ground truth segmentation masks were created through manual segmentation. A Res-Net based 3D-UNet was trained and evaluated via 5-fold cross-validation on S-PRESTO data (N = 300). The model's final performance was assessed on a hold-out (N = 89) and an external test set (N = 50, SAMS).

RESULTS

The proposed method enabled reliable segmentation of individual AAT sub-depots in 3D MRI volumes with high mean Dice similarity scores of 98.3%, 97.2%, 96.5%, 96.3%, and 95.9% for SSAT, DSAT, IPAT, RPAT, and PSAT respectively.

CONCLUSION

Convolutional neural networks can accurately sub-divide abdominal SAT into SSAT and DSAT, and abdominal IAT into IPAT, RPAT, and PSAT with high accuracy. The presented method has the potential to significantly contribute to advancements in the field of obesity imaging and precision medicine.

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.

Fat quantification: Imaging methods and clinical applications in cancer

Recently, the study of the relationship between lipid metabolism and cancer has evolved. The characteristics of intratumoral and peritumoral fat are distinct and changeable during cancer development. Subcutaneous and visceral adipose tissue are also associated with cancer prognosis. In non-invasive imaging, fat quantification parameters such as controlled attenuation parameter, fat volume fraction, and proton density fat fraction from different imaging methods complement conventional images by providing concrete fat information. Therefore, measuring the changes of fat content for further understanding of cancer characteristics has been applied in both research and clinical settings. In this review, the authors summarize imaging advances in fat quantification and highlight their clinical applications in cancer precaution, auxiliary diagnosis and classification, therapy response monitoring, and prognosis.

Automated volume measurement of abdominal adipose tissue from entire abdominal cavity in Dixon MR images using deep learning

The purpose of this study was to realize an automated volume measurement of abdominal adipose tissue from the entire abdominal cavity in Dixon magnetic resonance (MR) images using deep learning. Our algorithm involves a combination of extraction of the abdominal cavity and body trunk regions using deep learning and extraction of a fat region based on automatic thresholding. To evaluate the proposed method, we calculated the Dice coefficient (DC) between the extracted regions using deep learning and labeled images. We also compared the visceral adipose tissue (VAT) and subcutaneous adipose tissue volumes calculated by employing the proposed method with those calculated from computed tomography (CT) images scanned on the same day using the automatic calculation method previously developed by our group. We implemented our method as a plug-in in a web-based medical image processing platform. The DCs of the abdominal cavity and body trunk regions were 0.952 ± 0.014 and 0.995 ± 0.002, respectively. The VAT volume measured from MR images using the proposed method was almost equivalent to that measured from CT images. The time required for our plug-in to process the test set was 118.9 ± 28.0 s. Using our proposed method, the VAT volume measured from MR images can be an alternative to that measured from CT images.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

BACKGROUND

The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents.

METHODS

474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards.

RESULTS

For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS).

CONCLUSION

These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

Liposuction to improve the dorsocervical fat pad in esthetic need: Anatomical study and clinical case series

BACKGROUND

Patients are undergoing surgery to relieve the prominent dorsocervical fat pad because of their esthetic needs.

OBJECTIVE

To determine their clinical needs, it is necessary to research the clinic, radiography, and anatomy of dorsocervical fat pad.

METHODS

The occipital fat thickness, dorsal fat thickness, and the length, width, and thickness of the dorsocervical fat were measured through MRI imaging. Body projection position were recorded. The correlation between the body mass index and dorsocervical hump was analyzed. Anatomical and histological studies of cadavers with a dorsocervical hump were performed. The liposuction for dorsocervical fat pad was introduced and patients were followed up.

RESULTS

In measurement, the MRI imaging of 109 patients were evaluated. The average length, width, and thickness of the dorsocervical fat pad were 114.47, 89.24, and 23.46 mm, respectively, and it is commonly located from the 3rd cervical vertebra to the 3rd thoracic vertebra. The average dorsocervical ratio was 151%. 43.1% patients had a dorsocervical hump. Based on the protrusion degree, the dorsocervical fat pad was classified into three types. The dorsocervical hump severity had a low correlation with obesity. In anatomy, 4 cadavers were dissected. The histological staining indicated that two layers of fat pad constituted a dorsocervical fat pad. As for treatment, 34 patients underwent liposuction to improve the dorsocervical contour, all of them reported satisfactory outcome.

CONCLUSIONS

The histological manifestations and morphological measurement of dorsocervical fat pad is researched. Besides, liposuction was applied in 34 patients with dorsocervical hump, and had received satisfied outcome.

An effective automatic segmentation of abdominal adipose tissue using a convolution neural network

BACKGROUND AND AIMS

Computer-aided diagnosis and prognosis rely heavily on fully automatic segmentation of abdominal fat tissue using Emission Tomography images. The identification of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in abdomen fat faces two main challenges: (1) the great difficulties in comparison to multi-stage semantic segmentation (VAT and SAT), and (2) the subtle differences due to the high similarity of the two classes in abdomen fat and complicated VAT distribution.

METHODS

In this research, we built an automated convolutional neural network (A-CNN) for segmenting Abdominal adipose tissue (AAT) from radiology images.

RESULTS

We developed a point-to-point design for the A-CNN learning process, wherein the representing features might be learned together with a hybrid feature extraction technique. We tested the proposed model on a CT dataset and evaluated it to existing CNN models. Furthermore, our suggested approach, A-CNN, outperformed existing deep learning methods regarding segmentation outcomes, notably in the AAT segment.

CONCLUSIONS

Proposed method is extremely fast with remarkable performance on limited-scale low dose CT-scanning and demonstrates the strength in providing an efficient computer-aimed tool for segmentation of AAT in the clinic.

Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children

Purpose

To develop and evaluate an automated segmentation method for accurate quantification of abdominal adipose tissue (AAT) depots (superficial subcutaneous adipose tissue [SSAT], deep subcutaneous adipose tissue [DSAT], and visceral adipose tissue [VAT]) in neonates and young children.

Materials and Methods

This was a secondary analysis of prospectively collected data, which used abdominal MRI data from Growing Up in Singapore Towards healthy Outcomes, or GUSTO, a longitudinal mother-offspring cohort, to train and evaluate a convolutional neural network for volumetric AAT segmentation. The data comprised imaging volumes of 333 neonates obtained at early infancy (age ≤2 weeks, 180 male neonates) and 755 children aged either 4.5 years (n = 316, 150 male children) or 6 years (n = 439, 219 male children). The network was trained on images of 761 randomly selected volumes (neonates and children combined) and evaluated on 100 neonatal volumes and 227 child volumes by using 10-fold validation. Automated segmentations were compared with expert-generated manual segmentation. Segmentation performance was assessed using Dice scores.

Results

When the model was tested on the test datasets across the 10 folds, the model had strong agreement with the ground truth for all testing sets, with mean Dice similarity scores for SSAT, DSAT, and VAT, respectively, of 0.960, 0.909, and 0.872 in neonates and 0.944, 0.851, and 0.960 in children. The model generalized well to different body sizes and ages and to all abdominal levels.

Conclusion

The proposed segmentation approach provided accurate automated volumetric assessment of AAT compartments on MR images of neonates and children.Keywords Pediatrics, Deep Learning, Convolutional Neural Networks, Water-Fat MRI, Image Segmentation, Deep and Superficial Subcutaneous Adipose Tissue, Visceral Adipose TissueClinical trial registration no. NCT01174875 Supplemental material is available for this article. © RSNA, 2021.

Small extracellular vesicle-mediated targeting of hypothalamic AMPKα1 corrects obesity through BAT activation

Current pharmacological therapies for treating obesity are of limited efficacy. Genetic ablation or loss of function of AMP-activated protein kinase alpha 1 (AMPKα1) in steroidogenic factor 1 (SF1) neurons of the ventromedial nucleus of the hypothalamus (VMH) induces feeding-independent resistance to obesity due to sympathetic activation of brown adipose tissue (BAT) thermogenesis. Here, we show that body weight of obese mice can be reduced by intravenous injection of small extracellular vesicles (sEVs) delivering a plasmid encoding an AMPKα1 dominant negative mutant (AMPKα1-DN) targeted to VMH-SF1 neurons. The beneficial effect of SF1-AMPKα1-DN-loaded sEVs is feeding-independent and involves sympathetic nerve activation and increased UCP1-dependent thermogenesis in BAT. Our results underscore the potential of sEVs to specifically target AMPK in hypothalamic neurons and introduce a broader strategy to manipulate body weight and reduce obesity.

Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning

OBJECTIVE

To propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images.

MATERIALS AND METHODS

Adipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat-water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images).

RESULTS

The automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets.

CONCLUSION

The proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.

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

BACKGROUND AND AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

Evaluation of SUVlean consistency in FDG and PSMA PET/MR with Dixon-, James-, and Janma-based lean body mass correction

PURPOSE

To systematically evaluate the consistency of various standardized uptake value (SUV) lean body mass (LBM) normalization methods in a clinical positron emission tomography/magnetic resonance imaging (PET/MR) setting.

METHODS

SUV of brain, liver, prostate, parotid, blood, and muscle were measured in 90 18F-FDG and 28 18F-PSMA PET/MR scans and corrected for LBM using the James, Janma (short for Janmahasatian), and Dixon approaches. The prospective study was performed from December 2018 to August 2020 at Shanghai East Hospital. Forty dual energy X-ray absorptiometry (DXA) measurements of non-fat mass were used as the reference standard. Agreement between different LBM methods was assessed by linear regression and Bland-Altman statistics. SUV's dependency on BMI was evaluated by means of linear regression and Pearson correlation.

RESULTS

Compared to DXA, the Dixon approach presented the least bias in LBM/weight% than James and Janma models (bias 0.4±7.3%, - 8.0±9.4%, and - 3.3±8.3% respectively). SUV normalized by body weight (SUVbw) was positively correlated with body mass index (BMI) for both FDG (e.g., liver: r = 0.45, p < 0.001) and PSMA scans (r = 0.20, p = 0.31), while SUV normalized by lean body mass (SUVlean) revealed a decreased dependency on BMI (r = 0.22, 0.08, 0.14, p = 0.04, 0.46, 0.18 for Dixon, James, and Janma models, respectively). The liver SUVbw of obese/overweight patients was significantly larger (p < 0.001) than that of normal patients, whereas the bias was mostly eliminated in SUVlean. One-way ANOVA showed significant difference (p < 0.001) between SUVlean in major organs measured using Dixon method vs James and Janma models.

CONCLUSION

Significant systematic variation was found using different approaches to calculate SUVlean. A consistent correction method should be applied for serial PET/MR scans. The Dixon method provides the most accurate measure of LBM, yielding the least bias of all approaches when compared to DXA.

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies

PURPOSE

To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI.

MATERIALS AND METHODS

Quantification and localization of different adipose tissue compartments derived from whole-body MR images is of high interest in research concerning metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automated segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work, a three-dimensional (3D) densely connected convolutional neural network (DCNet) is proposed to provide robust and objective segmentation. In this retrospective study, 1000 cases (average age, 66 years ± 13 [standard deviation]; 523 women) from the Tuebingen Family Study database and the German Center for Diabetes research database and 300 cases (average age, 53 years ± 11; 152 women) from the German National Cohort (NAKO) database were collected for model training, validation, and testing, with transfer learning between the cohorts. These datasets included variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners, and imaging field strengths. The proposed DCNet was compared to a similar 3D U-Net segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap.

RESULTS

Fast (range, 5-7 seconds) and reliable adipose tissue segmentation can be performed with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%), and accuracy (98.4%) from 3D whole-body MRI datasets (field of view coverage, 450 × 450 × 2000 mm). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician.

CONCLUSION

Automated adipose tissue segmentation is feasible in 3D whole-body MRI datasets and is generalizable to different epidemiologic cohort studies with the proposed DCNet.Supplemental material is available for this article.© RSNA, 2020.

Large-scale biometry with interpretable neural network regression on UK Biobank body MRI

In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2 > 0.97$$\end{document}2>0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.

Quantitative Imaging of Body Composition

Body composition refers to the amount and distribution of lean tissue, adipose tissue, and bone in the human body. Lean tissue primarily consists of skeletal muscle; adipose tissue comprises mostly abdominal visceral adipose tissue and abdominal and nonabdominal subcutaneous adipose tissue. Hepatocellular and myocellular lipids are also fat pools with important metabolic implications. Importantly, body composition reflects generalized processes such as increased adiposity in obesity and age-related loss of muscle mass known as sarcopenia.In recent years, body composition has been extensively studied quantitatively to predict overall health. Multiple imaging methods have allowed precise estimates of tissue types and provided insights showing the relationship of body composition to varied pathologic conditions. In this review article, we discuss different imaging methods used to quantify body composition and describe important anatomical locations where target tissues can be measured.

Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT

Anthropometric parameters like muscle body mass (MBM), fat body mass (FBM), lean body mass (LBM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) are used in oncology. Our aim was to develop and evaluate the software Anthropometer3D measuring these anthropometric parameters on the CT of PET/CT. This software performs a multi-atlas segmentation of CT of PET/CT with extrapolation coefficients for the body parts beyond the usual acquisition range (from the ischia to the eyes). The multi-atlas database is composed of 30 truncated CTs manually segmented to isolate three types of voxels (muscle, fat, and visceral fat). To evaluate Anthropomer3D, a leave-one-out cross-validation was performed to measure MBM, FBM, LBM, VAT, and SAT. The reference standard was based on the manual segmentation of the corresponding whole-body CT. A manual segmentation of one CT slice at level L3 was also used. Correlations were analyzed using Dice coefficient, intra-class coefficient correlation (ICC), and Bland-Altman plot. The population was heterogeneous (sex ratio 1:1; mean age 57 years old [min 23; max 74]; mean BMI 27 kg/m² [min 18; max 40]). Dice coefficients between reference standard and Anthropometer3D were excellent (mean+/-SD): muscle 0.95 ± 0.02, fat 1.00 ± 0.01, and visceral fat 0.97 ± 0.02. The ICC was almost perfect (minimal value of 95% CI of 0.97). All Bland-Altman plot values (mean difference, 95% CI and slopes) were better for Anthropometer3D compared to L3 level segmentation. Anthropometer3D allows multiple anthropometric measurements based on an automatic multi-slice segmentation. It is more precise than estimates using L3 level segmentation.

Analysis of muscle, hip, and subcutaneous fat in osteoporosis patients with varying degrees of fracture risk using 3T Chemical Shift Encoded MRI

Osteoporosis (OP) is a major disease that affects 200 million people worldwide. Fatty acid metabolism plays an important role in bone health and plays an important role in bone quality and remodeling. Increased bone marrow fat quantity has been shown to be associated with a decrease in bone mineral density (BMD), which is used to predict fracture risk. Chemical-Shift Encoded magnetic resonance imaging (CSE-MRI) allows noninvasive and quantitative assessment of adipose tissues (AT). The aim of our study was to assess hip or proximal femoral bone marrow adipose tissue (BMAT), thigh muscle (MUS), and subcutaneous adipose tissue (SAT) in 128 OP subjects matched for age, BMD, weight and height with different degrees of fracture risk assessed through the FRAX score (low, moderate and high). Our results showed an increase in BMAT and in MUS in high compared to low fracture risk patients. We also assessed the relationship between fracture risk as assessed by FRAX and AT quantities. Overall, the results of this study suggest that assessment of adipose tissue via 3T CSE-MRI provides insight into the pathophysiology fracture risk by showing differences in the bone marrow and muscle fat content in subjects with similarly osteoporotic BMD as assessed by DXA, but with varying degrees of fracture risk as assessed by FRAX.

Pancreatic Fat is not significantly correlated with β-cell Dysfunction in Patients with new-onset Type 2 Diabetes Mellitus using quantitative Computed Tomography

Objective: Type 2 diabetes mellitus (T2DM) is a chronic condition resulting from insulin resistance and insufficient β-cell secretion, leading to improper glycaemic regulation. Previous studies have found that excessive fat deposits in organs such as the liver and muscle can cause insulin resistance through lipotoxicity that affects β-cell function. The relationships between fat deposits in pancreatic tissue, the function of β-cells, the method of visceral fat evaluation and T2DM have been sought by researchers. This study aims to elucidate the role of pancreatic fat deposits in the development of T2DM using quantitative computed tomography (QCT), especially their effects on islet β-cell function. Methods: We examined 106 subjects at the onset of T2DM who had undergone abdominal QCT. Estimated pancreatic fat and liver fat were quantified using QCT and calculated. We analysed the correlations with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) scores and other oral glucose tolerance test-derived parameters that reflect islet function. Furthermore, correlations of estimated pancreatic fat and liver fat with the area under the curve for insulin (AUC_INS) and HOMA-IR were assessed with partial correlation analysis and demonstrated by scatter plots. Results: Associations were found between estimated liver fat and HOMA-IR, AUC_INS, the modified β-cell function index (MBCI) and Homeostatic Model Assessment β (HOMA-β). However, no significant differences existed between estimated pancreas fat and those parameters. Similarly, after adjustment for sex, age and body mass index, only estimated liver fat was correlated with HOMA-IR and AUC_INS. Conclusions: This study suggests no significant correlation between pancreatic fat deposition and β-cell dysfunction in the early stages of T2DM using QCT as a screening tool. The deposits of fat in the pancreas and the resulting lipotoxicity may play an important role in the late stage of islet cell function dysfunction as the course of T2DM progresses.

Increased BMPR1A Expression Enhances the Adipogenic Differentiation of Mesenchymal Stem Cells in Patients with Ankylosing Spondylitis

Objective

To investigate the adipogenic differentiation capacity of mesenchymal stem cells (MSCs) from ankylosing spondylitis (AS) patients and explore the mechanism of abnormal MSC adipogenesis in AS.

Methods

MSCs from patients with AS (ASMSCs) and healthy donors (HDMSCs) were cultured in adipogenic differentiation medium for up to 21 days. Adipogenic differentiation was determined using oil red O (ORO) staining and quantification and was confirmed by assessing adipogenic marker expression (PPAR-γ, FABP4, and adiponectin). Gene expression of adipogenic markers was detected using qRT-PCR. Protein levels of adipogenic markers and signaling pathway-related molecules were assessed via Western blotting. Levels of bone morphogenetic proteins 4, 6, 7, and 9 were determined using enzyme-linked immunosorbent assays. Lentiviruses encoding short hairpin RNAs (shRNAs) were constructed to reverse abnormal bone morphogenetic protein receptor 1A (BMPR1A) expression and evaluate its role in abnormal ASMSC adipogenic differentiation. Bone marrow fat content was assessed using hematoxylin and eosin (HE) staining. BMPR1A expression in bone marrow MSCs was measured using immunofluorescence staining.

Results

ASMSCs exhibited a greater adipogenic differentiation capacity than HDMSCs. During adipogenesis, ASMSCs expressed BMPR1A at higher levels, which activated the BMP-pSmad1/5/8 signaling pathway and increased adipogenesis. BMPR1A silencing using an shRNA eliminated the difference in adipogenic differentiation between HDMSCs and ASMSCs. Moreover, HE and immunofluorescence staining showed higher bone marrow fat content and BMPR1A expression in patients with AS than in healthy donors.

Conclusion

Increased BMPR1A expression induces abnormal ASMSC adipogenic differentiation, potentially contributing to fat metaplasia and thus new bone formation in patients with AS.

Free-breathing Magnetic Resonance Imaging Assessment of Body Composition in Healthy and Overweight Children: An Observational Study

OBJECTIVE

Conventional, breath-holding magnetic resonance imaging (MRI) assesses body composition by measuring fat volumes and proton density fat fraction (PDFF). However, breath-holding MRI is not always feasible in children. This study's objective was to use free-breathing MRI to quantify visceral and subcutaneous fat volumes and PDFFs and correlate these measurements with hepatic PDFF.

METHODS

This was an observational, hypothesis-forming study that enrolled 2 groups of children (ages 6-17 years), healthy children and overweight children with presumed nonalcoholic fatty liver disease. Free-breathing MRI was used to measure visceral and subcutaneous fat volumes and PDFFs, and hepatic PDFF. Imaging biomarkers were compared between groups, and correlations coefficients (r) and coefficients of determination (R) were calculated.

RESULTS

When compared with the control group (n = 10), the overweight group (n = 9) had greater mean visceral (1843 vs 329 cm, P < 0.001) and subcutaneous fat volumes (7663 vs 893 cm, P < 0.001), as well as greater visceral (80% vs 45%, p < 0.001) and subcutaneous fat PDFFs (89% vs 75%, P = 0.003). Visceral fat volume (r = 0.79, P < 0.001) and PDFF (r = 0.92, P < 0.001) correlated with hepatic PDFF. In overweight subjects, for each unit increase in visceral fat PDFF, hepatic PDFF increased by 2.64%; visceral fat PDFF explained 54% of hepatic PDFF variation (R = 0.54, P = 0.02).

CONCLUSIONS

In this study, we used free-breathing MRI to measure body composition in children. Future studies are needed to investigate the possible value of subcutaneous and visceral fat PDFFs, and validate free-breathing MRI body composition biomarkers.

Male and female cats have different regional body compositions and energy requirements for weight loss and weight maintenance

Body composition and energy requirements are different between males and females in several species, and both interfere with weight loss. The aim of this study was to compare the total and regional body composition and energy requirements in obese male (n = 8) and female (n = 8) cats, during weight loss and weight maintenance over 17 subsequent weeks after regimen. The total and regional (thoracic and pelvic limbs, and trunk) body composition was assessed by dual-energy X-ray absorptiometry (DXA). Females exhibited a higher fat mass (FM) than males (p < 0.05), and the trunk was the site with greater fat accumulation regardless of gender. A 23.0 ± 2.8% reduction in body weight was followed by a 50.3 ± 9.4% and a 37.0 ± 8.9% reduction in fat in the trunk region in males and females respectively. Lean mass (LM) mobilization was also increased in the trunk (p < 0.05), and the loss of LM was associated with a reduction in bone mass. The energy intake to achieve the same rate of weight loss was 12.9 ± 3.4% higher in males (p < 0.05). The cats exhibited a gradual increase in energy requirements to maintain their body weight after weight loss (p < 0.05). It was concluded that obese cats mainly accumulate fat in the trunk. The reduction in lean mass after the regimen also occurred in the trunk, with no modifications in the skeletal muscle mass of the limbs. Neutered male cats have higher energy requirements than neutered females, and gender should be considered during obesity management in cats.

Quantification of visceral adipose tissue by computed tomography and magnetic resonance imaging: reproducibility and accuracy

Objective

To evaluate the feasibility of quantifying visceral adipose tissue (VAT) on computed tomography (CT) and magnetic resonance imaging (MRI) scans, using freeware, as well as calculating intraobserver and interobserver reproducibility.

Materials and Methods

We quantified VAT in patients who underwent abdominal CT and MRI at our institution between 2010 and 2015, with a maximum of three months between the two examinations. A slice acquired at the level of the umbilicus was selected. Segmentation was performed with the region growing algorithm of the freeware employed. Intraobserver and interobserver reproducibility were evaluated, as was the accuracy of MRI in relation to that of CT.

Results

Thirty-one patients (14 males and 17 females; mean age of 57 ± 15 years) underwent CT and MRI (mean interval between the examinations, 28 ± 12 days). The interobserver reproducibility was 82% for CT (bias = 1.52 cm²; p = 0.488), 86% for T1-weighted MRI (bias = −4.36 cm²; p = 0.006), and 88% for T2-weighted MRI (bias = −0.52 cm²; p = 0.735). The intraobserver reproducibility was 90% for CT (bias = 0.14 cm²; p = 0.912), 92% for T1-weighted MRI (bias = −3,4 cm²; p = 0.035), and 90% for T2-weighted MRI (bias = −0.30 cm²; p = 0.887). The reproducibility between T1-weighted MRI and T2-weighted MRI was 87% (bias = −0.11 cm²; p = 0.957). In comparison with the accuracy of CT, that of T1-weighted and T2-weighted MRI was 89% and 91%, respectively.

Conclusion

The program employed can be used in order to quantify VAT on CT, T1-weighted MRI, and T2-weighted MRI scans. Overall, the accuracy of MRI (in comparison with that of CT) appears to be high, as do intraobserver and interobserver reproducibility. However, the quantification of VAT seems to be less reproducible in T1-weighted sequences.

Greater Reduction in Abdominal Than in Upper Arms Subcutaneous Fat in 10- to 12-Year-Old Tennis Players: A Volumetric MRI Study

Background: Little is known about the impact of long term participation in sports and subcutaneous fat volume in children. This study aimed at determining whether tennis participation is associated with lower subcutaneous adipose tissue volume (SATv) in the abdominal and upper extremities in children. Methods: Magnetic resonance imaging (MRI) was used to determine the SATv stored in the abdominal region and upper arms in seven tennis players and seven inactive children matched by height and age (147 cm and 10.9 years). All participants were in Tanner stage 1 or 2. Results: Playing tennis was associated with 48% (P = 0.001) lower abdominal SATv and 17-18% (P > 0.05) lower upper arms SATv compared to controls. The ratio between abdominal/upper arms SATv was larger in the controls vs. tennis players (69% P = 0.001). The SATv was similar in the dominant and non-dominant arm within each group. Conclusion: Playing tennis during childhood is associated with reduced SATv in the abdominal region and a more favorable regional distribution of fat. Despite the large amount of contractile activity of the playing (dominant) arm, there was no indication of between-arms differences in SATv.

Techniques and Applications of Magnetic Resonance Imaging for Studying Brown Adipose Tissue Morphometry and Function

The present review reports on the current knowledge and recent findings in magnetic resonance imaging (MRI) and spectroscopy (MRS) of brown adipose tissue (BAT). The work summarizes the features and mechanisms that allow MRI to differentiate BAT from white adipose tissue (WAT) by making use of their distinct morphological appearance and the functional characteristics of BAT. MR is a versatile imaging modality with multiple contrast mechanisms as potential candidates in the study of BAT, targeting properties of ¹H, ¹³C, or ¹²⁹Xe nuclei. Techniques for assessing BAT morphometry based on fat fraction and markers of BAT microstructure, including intermolecular quantum coherence and diffusion imaging, are first described. Techniques for assessing BAT function based on the measurement of BAT metabolic activity, perfusion, oxygenation, and temperature are then presented. The application of the above methods in studies of BAT in animals and humans is described, and future directions in MR study of BAT are finally discussed.

Body composition techniques

Body composition is known to be associated with several diseases, such as cardiovascular disease, diabetes, cancers, osteoporosis and osteoarthritis. Body composition measurements are useful in assessing the effectiveness of nutritional interventions and monitoring the changes associated with growth and disease conditions. Changes in body composition occur when there is a mismatch between nutrient intake and requirement. Altered body composition is observed in conditions such as wasting and stunting when the nutritional intake may be inadequate. Overnutrition on the other hand leads to obesity. Many techniques are available for body composition assessment, which range from simple indirect measures to more sophisticated direct volumetric measurements. Some of the methods that are used today include anthropometry, tracer dilution, densitometry, dual-energy X-ray absorptiometry, air displacement plethysmography and bioelectrical impedance analysis. The methods vary in their precision and accuracy. Imaging techniques such as nuclear magnetic resonance imaging and computed tomography have become powerful tools due to their ability of visualizing and quantifying tissues, organs, or constituents such as muscle and adipose tissue. However, these methods are still considered to be research tools due to their cost and complexity of use. This review was aimed to describe the commonly used methods for body composition analysis and provide a brief introduction on the latest techniques available.

Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI

PURPOSE

An approach for the automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water-fat MRI scans of the abdomen was investigated, using 2 different neural network architectures.

METHODS

The 2 fully convolutional network architectures U-Net and V-Net were trained, evaluated, and compared using the water-fat MRI data. Data of the study Tellus with 90 scans from a single center was used for a 10-fold cross-validation in which the most successful configuration for both networks was determined. These configurations were then tested on 20 scans of the multicenter study beta-cell function in JUvenile Diabetes and Obesity (BetaJudo), which involved a different study population and scanning device.

RESULTS

The U-Net outperformed the used implementation of the V-Net in both cross-validation and testing. In cross-validation, the U-Net reached average dice scores of 0.988 (VAT) and 0.992 (SAT). The average of the absolute quantification errors amount to 0.67% (VAT) and 0.39% (SAT). On the multicenter test data, the U-Net performs only slightly worse, with average dice scores of 0.970 (VAT) and 0.987 (SAT) and quantification errors of 2.80% (VAT) and 1.65% (SAT).

CONCLUSION

The segmentations generated by the U-Net allow for reliable quantification and could therefore be viable for high-quality automated measurements of VAT and SAT in large-scale studies with minimal need for human intervention. The high performance on the multicenter test data furthermore shows the robustness of this approach for data of different patient demographics and imaging centers, as long as a consistent imaging protocol is used.

MRI adipose tissue and muscle composition analysis-a review of automation techniques

MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.

How to best assess abdominal obesity

PURPOSE OF REVIEW

Abdominal obesity, especially the increase of visceral adipose tissue (VAT), is closely associated with increased mortality related to cardiovascular disease, diabetes, and fatty liver disease. This review provides an overview of the recent advances for abdominal obesity measurement.

RECENT FINDINGS

Compared to simple waist circumference, emerging three-dimensional (3D) body-scanning techniques also measure abdominal volume and shape. Abdominal dimension measures have been implemented in bioelectrical impedance analysis to improve accuracy when estimating VAT. Geometrical models have been applied in ultrasound to convert depth measurement into VAT area. Only computed tomography (CT) and MRI can provide direct measures of VAT. Recent advances in imaging allow for evaluating functional aspects of abdominal fat such as brown adipose tissue and fatty acid composition.

SUMMARY

Waist circumference is a simple, inexpensive method to measure abdominal obesity. CT and MRI are reference methods for measuring VAT. Further studies are needed to establish the accuracy for dual-energy X-ray absorptiometry in estimating longitudinal changes of VAT. Further studies are needed to establish whether bioelectrical impedance analysis, ultrasound, or 3D body scanning is consistently superior to waist circumference in estimating VAT in different populations.

Advanced body composition assessment: from body mass index to body composition profiling

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment.

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.

Adipose tissue and liver

Adipose tissue and liver are central tissues in whole body energy metabolism. Their composition, structure, and function can be noninvasively imaged using a variety of measurement techniques that provide a safe alternative to an invasive biopsy. Imaging of adipose tissue is focused on quantitating the distribution of adipose tissue in subcutaneous and intra-abdominal (visceral) adipose tissue depots. Also, detailed subdivisions of adipose tissue can be distinguished with modern imaging techniques. Adipose tissue (or adipocyte) accumulation or infiltration of other organs can also be imaged, with intramuscular adipose tissue a common example. Although liver fat content is now accurately imaged using standard magnetic resonance imaging (MRI) techniques, inflammation and fibrosis are more difficult to determine noninvasively. Liver imaging efforts are therefore concerted on developing accurate imaging markers of liver fibrosis and inflammatory status. Magnetic resonance elastography (MRE) is presently the most reliable imaging technique for measuring liver fibrosis but requires an external device for introduction of shear waves to the liver. Methods using multiparametric diffusion, perfusion, relaxometry, and hepatocyte-specific MRI contrast agents may prove to be more easily implemented by clinicians, provided they reach similar accuracy as MRE. Adipose tissue imaging is experiencing a revolution with renewed interest in characterizing and identifying distinct adipose depots, among them brown adipose tissue. Magnetic resonance spectroscopy provides an interesting yet underutilized way of imaging adipose tissue metabolism through its fatty acid composition. Further studies may shed light on the role of fatty acid composition in different depots and why saturated fat in subcutaneous adipose tissue is a marker of high insulin sensitivity.

Increased brain cortical thickness associated with visceral fat in adolescents

BACKGROUND

There has been a growing amount of evidence indicating that excess visceral fat is associated with alterations in brain structure and function, including brain cortical thinning in adults.

OBJECTIVES

This study aims to investigate the relationship between brain cortical thickness with obesity assessments, in adolescents.

METHODS

In this study, we measured three different obesity assessments within an adolescent population (aged 15 - 18 years): body mass index (BMI), visceral fat ratio measured with an MRI and hepatorenal gradient measured with an ultrasound. Volunteers also underwent an MRI scan to measure brain structure.

RESULTS

Results indicated that there was no relationship of BMI or hepatorenal gradient with brain cortical dimensions. However, there was a significant association between visceral fat ratio and an increase of cortical thickness throughout the brain.

CONCLUSIONS

These results suggest that visceral fat, but not BMI, is correlated with cortical thickening in adolescence.

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.

Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images

Human brown adipose tissue (BAT), with a major site in the cervical-supraclavicular depot, is a promising anti-obesity target. This work presents an automated method for segmenting cervical-supraclavicular adipose tissue for enabling time-efficient and objective measurements in large cohort research studies of BAT. Fat fraction (FF) and R₂^* maps were reconstructed from water-fat magnetic resonance imaging (MRI) of 25 subjects. A multi-atlas approach, based on atlases from nine subjects, was chosen as automated segmentation strategy. A semi-automated reference method was used to validate the automated method in the remaining subjects. Automated segmentations were obtained from a pipeline of preprocessing, affine registration, elastic registration and postprocessing. The automated method was validated with respect to segmentation overlap (Dice similarity coefficient, Dice) and estimations of FF, R₂^* and segmented volume. Bias in measurement results was also evaluated. Segmentation overlaps of Dice = 0.93 ± 0.03 (mean ± standard deviation) and correlation coefficients of r > 0.99 (P < 0.0001) in FF, R₂^* and volume estimates, between the methods, were observed. Dice and BMI were positively correlated (r = 0.54, P = 0.03) but no other significant bias was obtained (P ≥ 0.07). The automated method compared well with the reference method and can therefore be suitable for time-efficient and objective measurements in large cohort research studies of BAT.

Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging-based, Semiautomated Analysis Method

Purpose To determine the repeatability and accuracy of a commercially available magnetic resonance (MR) imaging-based, semiautomated method to quantify abdominal adipose tissue and thigh muscle volume and hepatic proton density fat fraction (PDFF). Materials and Methods This prospective study was institutional review board- approved and HIPAA compliant. All subjects provided written informed consent. Inclusion criteria were age of 18 years or older and willingness to participate. The exclusion criterion was contraindication to MR imaging. Three-dimensional T1-weighted dual-echo body-coil images were acquired three times. Source images were reconstructed to generate water and calibrated fat images. Abdominal adipose tissue and thigh muscle were segmented, and their volumes were estimated by using a semiautomated method and, as a reference standard, a manual method. Hepatic PDFF was estimated by using a confounder-corrected chemical shift-encoded MR imaging method with hybrid complex-magnitude reconstruction and, as a reference standard, MR spectroscopy. Tissue volume and hepatic PDFF intra- and interexamination repeatability were assessed by using intraclass correlation and coefficient of variation analysis. Tissue volume and hepatic PDFF accuracy were assessed by means of linear regression with the respective reference standards. Results Adipose and thigh muscle tissue volumes of 20 subjects (18 women; age range, 25-76 years; body mass index range, 19.3-43.9 kg/m²) were estimated by using the semiautomated method. Intra- and interexamination intraclass correlation coefficients were 0.996-0.998 and coefficients of variation were 1.5%-3.6%. For hepatic MR imaging PDFF, intra- and interexamination intraclass correlation coefficients were greater than or equal to 0.994 and coefficients of variation were less than or equal to 7.3%. In the regression analyses of manual versus semiautomated volume and spectroscopy versus MR imaging, PDFF slopes and intercepts were close to the identity line, and correlations of determination at multivariate analysis (R²) ranged from 0.744 to 0.994. Conclusion This MR imaging-based, semiautomated method provides high repeatability and accuracy for estimating abdominal adipose tissue and thigh muscle volumes and hepatic PDFF. ^© RSNA, 2017.

Low muscle mass at initiation of anti-TNF therapy for inflammatory bowel disease is associated with early treatment failure: a retrospective analysis

BACKGROUND/OBJECTIVES

Delayed treatment failure occurs in a significant proportion of inflammatory bowel disease (IBD) patients treated with tumor necrosis factor-alpha (TNF) antagonists. Identification of predictors of loss of response (LOR) may help to optimize therapy. We sought to determine whether body composition parameters at the commencement of anti-TNF therapy were associated with earlier treatment failure.

SUBJECTS/METHODS

A retrospective cohort study was performed on 68 patients who had undergone cross-sectional abdominal imaging coincident with the commencement of anti-TNF drugs. Analysis of the images at the third lumbar vertebra was performed using standard techniques to determine cross-sectional areas of skeletal muscle (SM), visceral adipose tissue, subcutaneous adipose tissue and intermuscular adipose tissue. Treatment failure was defined as: post-induction hospital admission or surgery for IBD, escalation of TNF dose or immunosuppressants for clinical LOR, emergence of a new fistula or Crohn's Disease Activity Index (CDAI) >150.

RESULTS

Two-thirds of patients had myopenia. Patients with less than gender-specific median SM area had a median time to failure of 520 (s.d. 135) days compared to 1100 (s.d. 151) days for those with more than median SM area (P=0.036). No difference was found in disease duration, inflammatory markers or CDAI between quartiles of SM area. No relation between outcomes and measures of adipose tissue, weight or body mass index was observed.

CONCLUSIONS

Identifying low muscle mass at anti-TNF induction as a risk factor for treatment failure may contribute to a more tailored approach to IBD therapy.