Abstract #2135

Automated measurement of peri-organ visceral adipose tissue in MRI as a powerful biomarker of metabolic profile

Mohamed ZARAI¹, Karine CLEMENT², Judith ARON², Nadjia KACHENOURA³, Khaoula BOUAZIZI³, and Alban REDHEUIL⁴

¹Institute of Cardiometabolism and Nutrition (ICAN), Paris, France, Paris, France, ²Unité de recherche sur les maladies cardiovasculaires, du métabolisme et de la nutrition (UMRS 1166), NutriOmics team 6, and Nutrition department, Pitié-Salpêtrière Hospital, Paris, France, Institute of Cardiometabolism and Nutrition (ICAN), Paris, France, Paris, France, ³Laboratoire d’Imagerie Biomédicale (LIB), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France, Paris, France, ⁴Unité de recherche sur les maladies cardiovasculaires, du métabolisme et de la nutrition (UMRS 1166), NutriOmics team 6, and Nutrition department, Pitié-Salpêtrière Hospital, Paris, France Département d’Imagerie CardioVasculaire et de Radiologie Interventionnelle et Thoracique (DICVRIT), Pitié-Salpêtrière Hospital, Paris, France, Institute of Cardiometabolism and Nutrition (ICAN), Paris, France Département d’Imagerie CardioVasculaire et de Radiologie Interventionnelle et Thoracique (DICVRIT), Pitié-Salpêtrière Hospital, Paris, France, Paris, France

The aim of this work is to develop an automatic segmentation algorithm to classify truncular adipose tissue into different compartments. MRI acquisitions including cine-SSFP and DIXON imaging were performed at 1.5 T in 117 individuals (metabolic patients and healthy controls). Fat maps were filtered with a top-hat filter to correct intensity inhomogeneities. An active contour and a k-means algorithms were used to discriminate the SAT and the VAT. Accurate and reproducible quantification of the adipose tissue is crucial for metabolic studies since they serve as good indicators of metabolic and associated cardiovascular risks.

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