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Abstract #0257

Automated T2* Estimation with Complex-Signal Based Weighted Least Squares Exponential Fitting

Shreyas S. Vasanawala1, Huanzhou Yu2, Ann Shimakawa2, Michael Jeng3, Jean H. Brittain4

1Department of Radiology, Stanford University, Stanford, CA, United States; 2Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States; 3Department of Pediatrics, Division of Hematology/Oncology, Stanford University, Stanford, CA, United States; 4Applied Science Laboratory, GE Healthcare, Madison, WI, United States

Patients who receive chronic red blood cell transfusion therapy are at risk for iron overload if not receiving appropriate iron chelation. Quantification of iron deposition for therapeutic decision-making is vital. We aim to evaluate a method of automated T2* mapping with a weighted least squares algorithm in pediatric patients with suspected hepatic iron deposition and to compare it with a conventional T2* mapping method. Twenty three patients ages 5 to 17 years were recruited. Good correlation was obtained between the methods with R2 of 0.97. It is noted that the simple exponential fitting technique likely over-estimates T2* at short T2*.