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Project 1

A novel toolkit for motion-robust multi-parametric quantitative Magnetic Resonance Imaging of human adipose tissue

Despite the severe global health and socioeconomic effects of obesity, the identification of people at risk of developing obesity-related metabolic complications can be problematic using currently available biomarkers. In addition, lifestyle interventions may not be equally effective in preventing the metabolic complications of obesity in all people. Adipose tissue has become an increasingly important target both in research and in clinical medicine of metabolic dysfunction. White adipose tissue dysfunction plays a central role in the incidence of metabolic complications in obesity and brown adipose tissue has been considered as the target organ for the improvement of cardiometabolic health in persons with obesity.
Magnetic Resonance Imaging (MRI) is increasingly considered a popular approach for metabolic phenotyping and has been used in the evaluation of adipose tissue distribution and ectopic lipid content. Yet, little is known on how to characterize white adipose tissue properties beyond its volume and distribution, and existing MR techniques are limited in reliably detecting the presence of brown adipose tissue. In addition, adipose tissue MRI is affected by numerous sources of respiratory motion-induced artifacts, requires high spatial resolution and previously proposed MR parameters can only deliver limited information.
The overarching goal of the present project is to develop an imaging toolkit for motion-robust multi-parametric high-resolution MR imaging methodologies to improve the specificity of the MRI biomarkers of white and brown adipose tissue. The present research program aims to develop a motion-robust multi parametric MRI methodology for non-invasive in vivo assessment a) of the visceral and subcutaneous adipose for the characterization of white adipose tissue quality (based on fat fraction and T2* mapping) and b) of the supraclavicular adipose tissue for the determination of brown adipose tissue content (based on dynamic fat fraction, T2*and water T2 mapping).

Power in Numbers

Project Leader


Prof. Dr. Dimitrios Karampinos

Technical University of Munich


Years of experience





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