Abstract #2740

T2 Estimation for Small Lesions Using a Model-Based Reconstruction with Sparsifying Penalty Functions and Highly Under-Sampled Radial FSE Data

Chuan Huang¹, Ali Bilgin², Christian Graff³, Maria I. Altbach⁴

¹Dept of Mathematics, University of Arizona, Tucson, AZ, USA; ²Department of Electrical and Computer Engineering, University of Arizona; ³Program in Applied Mathematics, University of Arizona; ⁴Dept of Radiology, University of Arizona

T2 weighted MRI is used clinically for characterization of various pathologies. It has been demonstrated that quantitative methods for measuring T2 values are superior to visual evaluation. However, many of the proposed methods to measure T2 values suffer from long acquisition times, motion-induced errors, low spatial resolution and/or low number of measured points on the T2 relaxation curve, etc. To overcome the problems we proposed a model-based approach with sparsifying penalty functions for estimating T2s from radial FSE data which yields accurate T2 estimates of small lesions from highly undersampled data. This novel method has great potential for characterization of small neoplasms in the body where the acquisition time is restricted to a breath hold.