@inproceedings{3972ef9522164b42a7e9506279725651,
title = "Whole-leg chemical-shift encoded MRI analysis reveals differential subcutaneous adipose tissue accumulation in lipedema",
abstract = "Lipedema is a painful connective tissue disease involving excessive subcutaneous adipose tissue (SAT) accumulation in the lower extremities. Lipedema remains poorly recognized as a unique clinical entity and is often misdiagnosed as obesity. Whole-body magnetic resonance imaging (MRI) acquisitions could provide insight into the unique body composition of lipedema, yet methodologies for multi-slice analyses are lacking. In this work, a semi-automated processing workflow was developed to segment and quantify adiposity from whole-leg chemical-shift encoded (CSE) MRI to distinguish lipedema. Patients with lipedema (N=15) and controls (N=13) matched for age and body mass index underwent a CSE MRI exam in eight stacks from the head-to-ankles. Slices from thighs-to-ankles were segmented via Chan-Vese segmentation, clustering, and morphological techniques to separate SAT and skeletal muscle. SAT and muscle volume per slice and the SAT-to-muscle volume ratio were recorded in decades of slices and compared between groups using Mann-Whitney U test with two-sided significance criteria p<0.05. SAT volume was significantly elevated in participants with lipedema in all decades (p<0.001), while muscle volume was not significantly different. SAT-to-muscle volume ratio was elevated in lipedema compared to controls (p<0.001), with the greatest effect size (rrb = 0.74) observed in the eighth decade corresponding to the mid-thigh region. These findings reveal SAT distribution is uniquely elevated throughout the legs of participants with lipedema as discerned from whole-leg CSE MRI. CSE MRI and analysis methods developed herein for SAT quantification could inform the diagnosis of lipedema, which suffers from few objective strategies to differentiate the disease from obesity.",
keywords = "body composition, chemical-shift encoded MRI, connective tissue disease, lipedema, segmentation, subcutaneous adipose tissue, whole-body MRI",
author = "Taylor, {Shannon L.} and Donahue, {Paula M.C.} and Michael Pridmore and Maria Garza and Patel, {Niral J.} and Lee, {Chelsea A.} and Aday, {Aaron W.} and Beckman, {Joshua A.} and Donahue, {Manus J.} and Crescenzi, {Rachelle L.}",
note = "Funding Information: Imaging experiments were performed in the Vanderbilt Human Imaging Core, using research resources supported by the National Institutes of Health (NIH) grant 1S10OD021771-01. We are grateful for Philips support from Charles Nockowski and Ryan Robinson, and to Human Imaging Core MRI technologists. Recruitment through www.ResearchMatch.org and services at the Clinical Research Center are supported by the National Center for Advancing Translational Sciences (NCATS) Clinical Translational Science Award (CTSA) Program, award number 5UL1TR002243-03. Funding was provided by the Lipedema Foundation (LF) Postdoctoral Research Fellowship, LF Collaborative Grant #12, the NIH/NINR 1R01NR015079, the NIH/NHLBI 1R01HL157378, and the Institutional National Research Service Award (NRSA) T32 EB001628. The content is solely the responsibility of the authors and does not necessarily represent the official views of the IN.H Publisher Copyright: {\textcopyright} 2022 SPIE; Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2611240",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gimi, {Barjor S.} and Andrzej Krol",
booktitle = "Medical Imaging 2022",
}