Dr. Aaryani Sajja, assistant professor in Biomedical Engineering, recently received a Trailblazer Award from the National Institute of Health (NIH). This award will fund her research focused on developing an MRI technique for noninvasive, accurate and simultaneous quantification of iron and fat deposits, replacing biopsy to ensure that early diagnosis, close monitoring and effective treatment is given to patients to prevent disease progression and long-term complications.
Iron overload, either inherited or acquired through chronic blood transfusions, affects about 16 million Americans. Steatosis (‘fat overload’) affects one-third of the U.S. population and is linked with obesity, insulin resistance and metabolic syndromes. Co-occurrence of hepatic iron overload and steatosis is a common manifestation of diffuse liver diseases, chronic hepatopathies, and cancer therapy and can cause iron- and lipo-toxicity leading to progressive fibrosis, irreversible cirrhosis, and ultimately, organ failure. Magnetic resonance imaging (MRI) is a clinically important non-invasive tool for assessing hepatic iron overload and steatosis independently. However, in co-existing conditions, MRI quantification is often inaccurate due to confounding effects of iron and fat on MRI signal. Multi-spectral signal models accounting for these confounding effects have been proposed for simultaneous quantification of transverse relaxation rate (R2*), a predictor for iron content, and fat fraction (FF). However, these models were optimized and validated in only patients with steatosis and failed in different co-existing hepatic iron and fat overload conditions.
The models assume either single or dual R2* for water and fat protons, and any incorrect assumptions or instabilities in the signal model produce errors in R2* and FF calculations, leading to misdiagnosis. The assumption to use single or dual R2* depends on the dephasing effects of in vivo iron deposits on water and fat protons, and these effects, in turn, depend on the size and distribution of iron and fat deposits on the microscopic scale. Previous simulation and phantom studies investigating the performances of multi-spectral signal models did not use a realistic tissue model. In simulation study, the sizes and distribution of iron and fat molecules were not considered, and in phantom studies, the sizes of iron particles and fat droplets did not match the scales of in vivo iron and fat deposits. Hence, there is a void in our understanding of how the true microscopic arrangement of iron and fat deposits in vivo will cause susceptibility-induced inhomogeneities and affect the macroscopic MRI signal relaxation.
In this research, the team will perform a rigorous investigation for evaluating the contribution of size and distribution of iron and fat deposits on MRI signal via simulations, phantom experiments, and in vivo studies to determine an accurate MRI signal model for simultaneous and accurate assessment of iron overload and steatosis. They will (a) develop a Monte Carlo–based approach for creating virtual liver models with iron overload, steatosis, or both and simulating iron-proton interactions; (b) construct realistic phantoms with different particle sizes mimicking in vivo iron and fat deposits; and (c) validate MRI signal behavior in phantoms and retrospective patients by using biopsy assessments as a reference standard. This research will aid our understanding and quantification of iron and fat mediated relaxivity in tissues and therefore, will help us to develop and validate accurate signal models that can simultaneously quantify R2* and FF, thus enabling the noninvasive diagnosis of both iron overload and steatosis.
For more information on this project, contact Sajja at tpirneni@memphis.edu.