Xiaoming Yin1,2, Saurabh Shah3,
Andrew C. Larson1,2
1Radiology, Northwestern University,
Chicago, IL, United States; 2Electrical Engineering and Computer
Science, Northwestern University, Evanston , IL, United States; 3Siemens
Medical Solutions, Chicago, IL, United States
R2*
is typically estimated via mono-exponential fitting of signal decay within a
series of GRE images combined by the root sum-of-square (RSS) approach. However,
RSS approaches rectify and bias noise in later TEs, resulting in systematic
fitting errors for R2* estimation. Our work investigated the accuracy of low
SNR R2* measurements for RSS reconstructed data. Through phantom, ex vivo, and volunteer
studies, we compared the accuracy of R2* measurement using SNR-weighted
least-square regression and SNR-based truncation methods. We found
SNR-weighted least-square regression to be a simple and reliable approach to
reduce R2* measurement error.