Abstract #2457

Accelerating T2 Mapping Via Under-Sampled Low-Dimensional-Structure Self-Learning and Thresholding (LOST) Reconstruction

Tri Minh Ngo¹, Haiyan Ding², Mehmet Akakaya³, Elliot R. McVeigh¹, Daniel A. Herzka⁴

¹Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, United States; ²Biomedical Engineering, Tsinghua University, Beijing, China; ³Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; ⁴Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States

Myocardial 3D T2 mapping is useful for differentiating between infarct, edema and normal tissue but currently requires prohibitively long acquisition times to be clinically useful. We apply Low-dimensional-structure self-learning and thresholding (LOST) to reconstruct an under-sampled T2 mapping dataset and compare with SENSE reconstruction of equivalent acceleration rates. For rates R3, R3.9, the mean T2 error is lower for LOST than SENSE. At rate R3 SENSE reconstructs edge details better than LOST even though SENSEs mean T2 error is higher. At rate R3.9, SENSE exhibits high noise amplification while LOST exhibits blurring of high frequency details.