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000300113 1001_ $$aHaueise, Tobias$$b0
000300113 245__ $$aRefining visceral adipose tissue quantification: Influence of sex, age, and BMI on single slice estimation in 3D MRI of the German National Cohort.
000300113 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2026
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000300113 500__ $$a2026 Feb;36(1):114-124
000300113 520__ $$aHigh prevalence of visceral obesity and its associated complications underscore the importance of accurately quantifying visceral adipose tissue (VAT) depots. While whole-body MRI offers comprehensive insights into adipose tissue distribution, it is resource-intensive. Alternatively, evaluation of defined single slices provides an efficient approach for estimation of total VAT volume. This study investigates the influence of sex-, age-, and BMI on VAT distribution along the craniocaudal axis and total VAT volume obtained from single slice versus volumetric assessment in 3D MRI and aims to identify age-independent locations for accurate estimation of VAT volume from single slice assessment.This secondary analysis of the prospective population-based German National Cohort (NAKO) included 3D VIBE Dixon MRI from 11,191 participants (screened between May 2014 and December 2016). VAT and spine segmentations were automatically generated using fat-selective images. Standardized craniocaudal VAT profiles were generated. Axial percentage of total VAT was used for identification of reference locations for volume estimation of VAT from a single slice.Data from 11,036 participants (mean age, 52 ± 11 years, 5681 men) were analyzed. Craniocaudal VAT distribution differed qualitatively between men/women and with respect to age/BMI. Age-independent single slice VAT estimates demonstrated strong correlations with reference VAT volumes. Anatomical locations for accurate VAT estimation varied with sex/BMI.The selection of reference locations should be different depending on BMI groups, with a preference for caudal shifts in location with increasing BMI. For women with obesity (BMI >30 kg/m2), the L1 level emerges as the optimal reference location.
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000300113 650_7 $$2Other$$aDeep learning
000300113 650_7 $$2Other$$aMagnetic resonance imaging
000300113 650_7 $$2Other$$aObesity
000300113 650_7 $$2Other$$aSingle slice quantification
000300113 650_7 $$2Other$$aVisceral adipose tissue
000300113 7001_ $$aSchick, Fritz$$b1
000300113 7001_ $$aStefan, Norbert$$b2
000300113 7001_ $$aGrune, Elena$$b3
000300113 7001_ $$avon Itter, Marc-Nicolas$$b4
000300113 7001_ $$aKauczor, Hans-Ulrich$$b5
000300113 7001_ $$aNattenmüller, Johanna$$b6
000300113 7001_ $$0P:(DE-He78)a70f21a2bf78bbc1306c3d432ae08dc7$$aNorajitra, Tobias$$b7$$udkfz
000300113 7001_ $$aNonnenmacher, Tobias$$b8
000300113 7001_ $$aRospleszcz, Susanne$$b9
000300113 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus H$$b10$$udkfz
000300113 7001_ $$aSchlett, Christopher L$$b11
000300113 7001_ $$aWeiss, Jakob B$$b12
000300113 7001_ $$aFischer, Beate$$b13
000300113 7001_ $$aJöckel, Karl-Heinz$$b14
000300113 7001_ $$aKrist, Lilian$$b15
000300113 7001_ $$aNiendorf, Thoralf$$b16
000300113 7001_ $$aPeters, Annette$$b17
000300113 7001_ $$aSedlmeier, Anja M$$b18
000300113 7001_ $$aWillich, Stefan N$$b19
000300113 7001_ $$aBamberg, Fabian$$b20
000300113 7001_ $$aMachann, Jürgen$$b21
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