MRI in the assessment of adipose tissues and muscle composition: how to use it

Huber, Florian A.; Grande, Filippo Del; Rizzo, Stefania; Guglielmi, Giuseppe; Guggenberger, Roman

doi:10.21037/qims.2020.02.06

Cited by 35 publications

(27 citation statements)

References 103 publications

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“…In contrast, MRI is a valuable tool for quantifying the characteristics of BAT independent of the tissue's activation state. The lower fat content in BATs and the use of non-ionizing radiation make MRI a particularly attractive method (11,12), especially when assessing BAT alterations in response to pharmacologic stimulation that requires repeated scans. Chemical shift imaging has also been used to differentiate WAT and BAT; yet, BAT shows poor tissue-signal contrast due to the relatively limited imaging spatial resolution in this method (13,14).…”

Section: Introductionmentioning

confidence: 99%

Quantification of brown adipose tissue in vivo using synthetic magnetic resonance imaging: an experimental study with mice model

Huo¹,

Ye²,

Dong³

et al. 2022

Quant Imaging Med Surg

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Background: The white adipose tissue (WAT) and brown adipose tissue (BAT) are associated with the development of several obesity-associated disorders. The use of imaging techniques to differentiate BAT from WAT and quantify BAT volume remains challenging, due to limitations such as spatial resolution and magnetic field inhomogeneity. This study aimed to investigate the feasibility for differentiating BAT from WAT, and quantify the BAT volume in vivo using synthetic magnetic resonance imaging (MRI).Methods: A total of 16 C57BL/6 mice were scanned using synthetic MRI. Quantitative longitudinal relaxation time (T1) and transverse relaxation time (T2) maps were obtained from the original synthetic MRI data using the synthetic MRI software offline. The T1 and T2 values of interscapular BAT (IBAT) and dorsal subcutaneous WAT were measured. The IBAT volume was calculated using synthetic MRI-derived T2weighted images (T2WIs) based on its morphological characteristics and quantitative tissue values. The body weight of mice was measured, and the IBAT specimens were excised and weighted. The correlation between IBAT volume and the weight of IBAT gross specimen and between IBAT volume and mouse body weight was analyzed. Results:The T1 values of BAT (330.3±19.57 ms) were higher than those of WAT (304.42±4.14 ms) (P<0.001), whereas the T2 values of BAT (66.06±5.06 ms) were lower than those of WAT (88.23±7.68 ms) (P<0.001).The area under the curve (AUC) values of the T1 and T2 for differentiating BAT from WAT was 0.942 and 0.995, respectively. The AUC of the T2 values was higher than that of T1 (P=0.04) using the DeLong test. The optimal cut-off value for T2 was 76 ms for differentiating BAT from WAT (100% sensitivity, 93.7% specificity).A moderate correlation was observed between IBAT volume and the weight of the IBAT gross specimen (r=0.662, P=0.014), and between IBAT volume and mouse body weight (r=0.653, P=0.016). Conclusions:The quantitative parameters derived using synthetic MRI may be used to detect and differentiate BAT from WAT in vivo. Synthetic MRI may help quantify BAT volume in vivo. 2 Huo et al. Quantifying BAT by synthetic MRI

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Section: Introductionmentioning

confidence: 99%

Quantification of brown adipose tissue in vivo using synthetic magnetic resonance imaging: an experimental study with mice model

Huo¹,

Ye²,

Dong³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…This technique is suitable for longitudinal follow-ups in young adults when compared to the gold standard for abdominal fat measurements, such as CT scans, and is able to assess the abdominal compartment without the risk of unnecessary exposure to radiation. MRI-derived fat compartments are highly sensitive, reliable, and have been reported to be highly accurate [ 43 , 44 ]. MRI supports a greater understanding of the role of body fat accumulation in the physiology and pathophysiology of obesity, aging, and metabolic diseases.…”

Section: Discussionmentioning

confidence: 99%

Waist Circumference and BMI Are Strongly Correlated with MRI-Derived Fat Compartments in Young Adults

Pasanta

Htun

Pan

et al. 2021

Life

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Young adulthood is increasingly considered as a vulnerable age group for significant weight gain, and it is apparent that there is an increasing number of new cases of metabolic syndrome developing among this population. This study included 60 young adult volunteers (18–26 years old). All participants obtained a calculated total abdominal fat percentage, subcutaneous fat percentage, and visceral fat percentage using a semiautomatic segmentation technique from T1-weighted magnetic resonance imaging (MRI) images of the abdomen. The results show strongest correlation between abdominal fat and BMI (r = 0.824) followed by subcutaneous fat (r = 0.768), and visceral fat (r = 0.633) respectively, (p < 0.001 for all, after having been adjusted for age and gender). Among anthropometric measurements, waist circumference showed strong correlation with all fat compartments (r = 0.737 for abdominal, r = 0.707 for subcutaneous fat, and r = 0.512 for visceral fat; p < 0.001 for all). The results obtained from examining the blood revealed that there was a moderate positive correlation relationship between all fat compartments with triglyceride, high-density lipoprotein, and fasting glucose levels (p < 0.05 for all). This study suggests that both BMI and waist circumference could be used to assess the fat compartments and treatment targets to reduce the risk of metabolic disorders and health risks in the young adult population.

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“…Muscle mass quantification is of great clinical importance since sarcopenia correlates with several adverse clinical outcomes (39). This assessment can be performed by means of several imaging technique and using variable methodologies (40)(41)(42)(43)(44)(45)(46). For instance, quantitative evaluation of total skeletal muscle area at L3 level or psoas muscle area only are common approaches, being also these two measures correlated one to another (47).…”

Section: Skeletal Musclementioning

confidence: 99%

Artificial intelligence and abdominal adipose tissue analysis: a literature review

Greco

Mallio²

2021

Quant Imaging Med Surg

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Body composition imaging relies on assessment of tissues composition and distribution. Quantitative data of body composition have been linked to pathogenesis and clinical outcomes of a wide spectrum of diseases, including oncological and cardiovascular.Obesity classification is based on body mass index (BMI) which has the shortcoming not to provide any information on the distribution of adipose tissue and skeletal muscle tissue, nor does it allow to distinguish the two main compartments of abdominal adipose tissue: visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Indeed, patients with the same BMI may have remarkably different amount and distribution of VAT and SAT.Abdominal obesity is a widespread disorder among the population of all ages, related to various pathological

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MRI in the assessment of adipose tissues and muscle composition: how to use it

Cited by 35 publications

References 103 publications

Quantification of brown adipose tissue in vivo using synthetic magnetic resonance imaging: an experimental study with mice model

Quantification of brown adipose tissue in vivo using synthetic magnetic resonance imaging: an experimental study with mice model

Waist Circumference and BMI Are Strongly Correlated with MRI-Derived Fat Compartments in Young Adults

Artificial intelligence and abdominal adipose tissue analysis: a literature review

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