Electronic Theses and Dissertations




Aaryani Sajja



Document Type


Degree Name

Doctor of Philosophy


Biomedical Engineering

Committee Chair

Eugene Eckstein

Committee Member

Amy L de Jongh Curry

Committee Member

Esra Roan


Hepatic iron overload is a severe complication in patients receiving chronic blood transfusions for sickle cell disease, beta-thalassemia, and myelosuppression during chemotherapy. Accurate assessment of hepatic iron content (HIC) is thus paramount to quantify excessive iron accumulation and to monitor response to iron removal treatment. Needle biopsies are considered the reference standard to measure HIC. Magnetic resonance imaging (MRI) methods based on the effective transverse relaxation rate (R2*) have become a noninvasive alternative to measure HIC. R2* estimation typically involves 3 major steps - acquiring multiecho gradient echo (GRE) images of the liver under breath-hold, fitting a mono-exponential signal model to quantify R2*, and manually excluding the blood vessels from liver tissue via T2*-thresholding to estimate mean liver R2*. However, there are challenges such as respiratory motion, presence of fat, and manual extraction of liver parenchyma that affect each of these steps respectively, and eventually affect the accuracy, precision, and clinical workflow of R2*/HIC measurements. This dissertation addresses these challenges by evaluating a radial free-breathing multiecho ultra-short echo time (UTE) acquisition technique, a signal model based on Auto regressive moving average (ARMA) modeling that incorporates fat-water separation and R2* quantification, and an automated vessel exclusion technique for extraction of liver parenchyma to provide accurate automated methods for MRI based R2* measurements for the assessment of hepatic iron overload.


Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.