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| Home > Publications database > Refining visceral adipose tissue quantification: Influence of sex, age, and BMI on single slice estimation in 3D MRI of the German National Cohort. > print |
| Home > Publications database > Refining visceral adipose tissue quantification: Influence of sex, age, and BMI on single slice estimation in 3D MRI of the German National Cohort. > print |
| 001 | 300113 | ||
| 005 | 20260209101825.0 | ||
| 024 | 7 | _ | |a 10.1016/j.zemedi.2025.02.005 |2 doi |
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| 024 | 7 | _ | |a 0939-3889 |2 ISSN |
| 024 | 7 | _ | |a 1876-4436 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2025-00611 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Haueise, Tobias |b 0 |
| 245 | _ | _ | |a Refining visceral adipose tissue quantification: Influence of sex, age, and BMI on single slice estimation in 3D MRI of the German National Cohort. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2026 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1770628668_645158 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a 2026 Feb;36(1):114-124 |
| 520 | _ | _ | |a High 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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| 650 | _ | 7 | |a Deep learning |2 Other |
| 650 | _ | 7 | |a Magnetic resonance imaging |2 Other |
| 650 | _ | 7 | |a Obesity |2 Other |
| 650 | _ | 7 | |a Single slice quantification |2 Other |
| 650 | _ | 7 | |a Visceral adipose tissue |2 Other |
| 700 | 1 | _ | |a Schick, Fritz |b 1 |
| 700 | 1 | _ | |a Stefan, Norbert |b 2 |
| 700 | 1 | _ | |a Grune, Elena |b 3 |
| 700 | 1 | _ | |a von Itter, Marc-Nicolas |b 4 |
| 700 | 1 | _ | |a Kauczor, Hans-Ulrich |b 5 |
| 700 | 1 | _ | |a Nattenmüller, Johanna |b 6 |
| 700 | 1 | _ | |a Norajitra, Tobias |0 P:(DE-He78)a70f21a2bf78bbc1306c3d432ae08dc7 |b 7 |u dkfz |
| 700 | 1 | _ | |a Nonnenmacher, Tobias |b 8 |
| 700 | 1 | _ | |a Rospleszcz, Susanne |b 9 |
| 700 | 1 | _ | |a Maier-Hein, Klaus H |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 10 |u dkfz |
| 700 | 1 | _ | |a Schlett, Christopher L |b 11 |
| 700 | 1 | _ | |a Weiss, Jakob B |b 12 |
| 700 | 1 | _ | |a Fischer, Beate |b 13 |
| 700 | 1 | _ | |a Jöckel, Karl-Heinz |b 14 |
| 700 | 1 | _ | |a Krist, Lilian |b 15 |
| 700 | 1 | _ | |a Niendorf, Thoralf |b 16 |
| 700 | 1 | _ | |a Peters, Annette |b 17 |
| 700 | 1 | _ | |a Sedlmeier, Anja M |b 18 |
| 700 | 1 | _ | |a Willich, Stefan N |b 19 |
| 700 | 1 | _ | |a Bamberg, Fabian |b 20 |
| 700 | 1 | _ | |a Machann, Jürgen |b 21 |
| 773 | _ | _ | |a 10.1016/j.zemedi.2025.02.005 |g p. S0939388925000352 |0 PERI:(DE-600)2231492-1 |n 1 |p 114-124 |t Zeitschrift für medizinische Physik |v 36 |y 2026 |x 0939-3889 |
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