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

Childhood obesity: The threatening apprentice of the adiposity empire

Childhood obesity is a global health problem, with its prevalence having tripled since 1975. The increase in its prevalence has been predominantly in developing countries, but also in those with high economic status. Nowadays, there are multiple obesity definitions, however, one of the most accurate is the one which defines obesity as the accumulation of excessive body adiposity and not as an body weight excess. Nevertheless, the body mass index (BMI) is the most frequently used tool for its classification, according to the cut-off points established by the Center for Disease Control and World Health Organization tables. In children and adolescents an adiposity excess is related to the appearance of cardiovascular disease in adulthood and with many comorbidities such as metabolic syndrome, insulin resistance, type 2 diabetes, hypertension and metabolic dysfunction-associated steatotic liver disease, among others. Currently, there is still controversy about which is the ideal indicator for measuring overweight and obesity. BMI is still used as a standardized measure but may miss cases in which body composition is pathological despite a BMI within the normal-weight category. An adequate knowledge of the impact on health of dysfunctional adiposity as well as its accurate diagnosis will allow health professionals to address this condition in a more precise and comprehensive manner, and substantially improve the associated cardiometabolic risk and prognosis.

Free-breathing MRI techniques for fat and R2* quantification in the liver

OBJECTIVE

To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities.

MATERIALS AND METHODS

This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification.

RESULTS

Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity.

DISCUSSION

As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.

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.

Visceral fat and cardiometabolic future in children and adolescents: a critical update

Cardiovascular disease (CVD) is a process whose pathogenetic mechanisms start very early in life. Recently, the importance of visceral adipose tissue (VAT) has been highlighted in the development of CVD. VAT does not always depend on body mass index (BMI) and has been implicated in unfavorable metabolic activity and cardiovascular adverse events. Abnormally high deposition of VAT is associated with metabolic syndrome, obesity-associated phenotype, and cardiometabolic risk factors. Although the importance of visceral fat has not been studied broadly or extensively in long-term studies in children and adolescents, it appears that it does not have the same behavior as in adults, it is related to the appearance of cardiac risk factors. In adolescents, it plays a role in the pathogenesis of CVD that occur later in adulthood. Excess body weight and adiposity may lead to the development of early myocardial and pathological coronary changes in childhood. The purpose of this review is to summarize the risk factors, the clinical significance, and the prognostic role of visceral obesity in children and adolescents. In addition, extensive reference is made to the most commonly used techniques for the evaluation of VAT in clinical settings. IMPACT: Visceral obesity, plays an important role in cardiovascular health from very early in an individual's life. Visceral adipose tissue (VAT) distribution is not entirely related to body mass index (BMI) and provides additional prognostic information. There is a need to pay more attention to the assessment of VAT in young people, to develop methods that would go beyond the measurement of only BMI in clinical practice and to identify individuals with excess visceral adiposity and perhaps to monitor its changes.

Using Free-Breathing MRI to Quantify Pancreatic Fat and Investigate Spatial Heterogeneity in Children

BACKGROUND

MRI acquisition for pediatric pancreatic fat quantification is limited by breath-holds (BH). Full segmentation (FS) or small region of interest (ROI) analysis methods may not account for pancreatic fat spatial heterogeneity, which may limit accuracy.

PURPOSE

To improve MRI acquisition and analysis for quantifying pancreatic proton-density fat fraction (pPDFF) in children by investigating free-breathing (FB)-MRI, characterizing pPDFF spatial heterogeneity, and relating pPDFF to clinical markers.

STUDY TYPE

Prospective.

POPULATION

A total of 34 children, including healthy (N = 16, 8 female) and overweight (N = 18, 5 female) subjects.

FIELD STRENGTH AND SEQUENCES

3 T; multiecho gradient-echo three-dimensional (3D) stack-of-stars FB-MRI, multiecho gradient-echo 3D Cartesian BH-MRI.

ASSESSMENT

A radiologist measured FS- and ROI-based pPDFF on FB-MRI and BH-MRI PDFF maps, with anatomical images as references. Regional pPDFF in the pancreatic head, body, and tail were measured on FB-MRI. FS-pPDFF, ROI-pPDFF, and regional pPDFF were compared, and related to clinical markers, including hemoglobin A1c.

STATISTICAL TESTS

T-test, Bland-Altman analysis, Lin's concordance correlation coefficient (CCC), one-way analysis of variance, and Spearman's rank correlation coefficient were used. P < 0.05 was considered significant.

RESULTS

FS-pPDFF and ROI-pPDFF from FB-MRI and BH-MRI had mean difference = 0.4%; CCC was 0.95 for FS-pPDFF and 0.62 for ROI-pPDFF. FS-pPDFF was higher than ROI-pPDFF (10.4% ± 6.4% vs. 4.2% ± 2.8%). Tail-pPDFF (11.6% ± 8.1%) was higher than body-pPDFF (8.9% ± 6.3%) and head-pPDFF (8.7% ± 5.2%). Head-pPDFF and body-pPDFF positively correlated with hemoglobin A1c.

DATA CONCLUSION

FB-MRI pPDFF is comparable to BH-MRI. Spatial heterogeneity affects pPDFF quantification. Regional measurements of pPDFF in the head and body were correlated with hemoglobin A1c, a marker of insulin sensitivity.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Pregnancies complicated by gestational diabetes and fetal growth restriction: an analysis of maternal and fetal body composition using magnetic resonance imaging

INTRODUCTION

Maternal body composition may influence fetal body composition.

OBJECTIVE

The objective of this pilot study was to investigate the relationship between maternal and fetal body composition.

METHODS

Three pregnant women cohorts were studied: healthy, gestational diabetes (GDM), and fetal growth restriction (FGR). Maternal body composition (visceral adipose tissue volume (VAT), subcutaneous adipose tissue volume (SAT), pancreatic and hepatic proton-density fat fraction (PDFF) and fetal body composition (abdominal SAT and hepatic PDFF) were measured using MRI between 30 to 36 weeks gestation.

RESULTS

Compared to healthy and FGR fetuses, GDM fetuses had greater hepatic PDFF (5.2 [4.2, 5.5]% vs. 3.2 [3, 3.3]% vs. 1.9 [1.4, 3.7]%, p = 0.004). Fetal hepatic PDFF was associated with maternal SAT (r = 0.47, p = 0.02), VAT (r = 0.62, p = 0.002), and pancreatic PDFF (r = 0.54, p = 0.008). When controlling for maternal SAT, GDM increased fetal hepatic PDFF by 0.9 ([0.51, 1.3], p = 0.001).

CONCLUSION

In this study, maternal SAT, VAT, and GDM status were positively associated with fetal hepatic PDFF.

Visceral Obesity: Terminology, Measurement, and Its Correlation with Inflammation

The prevalence of childhood obesity in the world is significant and it is topical issue due to the high risk of chronic non-communicable diseases development. This article presents the analysis of pathogenetic role of visceral obesity, describes modern methods for measuring visceral adipose tissue, discusses major terminology on obesity. The current data on inflammation induced by excess of visceral adipose tissue and inflammasome’s role in this process are summed up. All the findings are crucial for the development of tools for prevention any obesity associated adverse effects in children.

3D Neural Networks for Visceral and Subcutaneous Adipose Tissue Segmentation using Volumetric Multi-Contrast MRI

Individuals with obesity have larger amounts of visceral (VAT) and subcutaneous adipose tissue (SAT) in their body, increasing the risk for cardiometabolic diseases. The reference standard to quantify SAT and VAT uses manual annotations of magnetic resonance images (MRI), which requires expert knowledge and is time-consuming. Although there have been studies investigating deep learning-based methods for automated SAT and VAT segmentation, the performance for VAT remains suboptimal (Dice scores of 0.43 to 0.89). Previous work had key limitations of not fully considering the multi-contrast information from MRI and the 3D anatomical context, which are critical for addressing the complex spatially varying structure of VAT. An additional challenge is the imbalance between the number and distribution of pixels representing SAT/VAT. This work proposes a network based on 3D U-Net that utilizes the full field-of-view volumetric T₁-weighted, water, and fat images from dual-echo Dixon MRI as the multi-channel input to automatically segment SAT and VAT in adults with overweight/obesity. In addition, this work extends the 3D U-Net to a new Attention-based Competitive Dense 3D U-Net (ACD 3D U-Net) trained with a class frequency-balancing Dice loss (FBDL). In an initial testing dataset, the proposed 3D U-Net and ACD 3D U-Net with FBDL achieved 3D Dice scores of (mean ± standard deviation) 0.99 ±0.01 and 0.99±0.01 for SAT, and 0.95±0.04 and 0.96 ±0.04 for VAT, respectively, compared to manual annotations. The proposed 3D networks had rapid inference time (<60 ms/slice) and can enable automated segmentation of SAT and VAT.Clinical relevance- This work developed 3D neural networks to automatically, accurately, and rapidly segment visceral and subcutaneous adipose tissue on MRI, which can help to characterize the risk for cardiometabolic diseases such as diabetes, elevated glucose levels, and hypertension.

Imaging of body composition in children

Overweight and obesity in children and adolescents have become a worldwide public health concern with an ever-increasing prevalence. An excessive accumulation of intraabdominal fat tissue increases the risk of developing insulin resistance, diabetes, and cardiovascular diseases in adulthood. Body composition has a role in metabolism regulation in children and adolescents with differences between genders and age groups. Until recently, Air Displacement Plethysmography and Dual-energy X-ray Absorptiometry (DXA) have been the most common techniques used to assess body composition in children. Ultrasound (US) is an accurate, readily available, and radiation-free technique to quantify intra-abdominal fat in adults, but its use in children has not yet been validated. Computed tomography (CT) is a reliable tool to assess body composition, but its use in children should be avoided due to the significant radiation burden. Quantitative Magnetic Resonance Imaging (qMRI) provides an accurate measurement of body composition, through the quantification of the visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and brown adipose tissue (BAT), as well as lean mass. Furthermore, qMRI provides other significant estimates such as the Proton Density Fat-Fraction of the fat tissue. This review article aims to briefly describe the state of art of the advanced imaging techniques to provide a quantitative assessment of body composition in children and adolescents.