Meeting Banner
Abstract #0775

Validation of DSI compressed sensing reconstruction in ex vivo human brain

Robert Jones1, Giorgia Grisot1,2, Jean Augustinack1, David A. Boas1,3, Bruce Fischl1,4, Hui Wang1, Berkin Bilgic1, and Anastasia Yendiki1

1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 3Boston University, Boston, MA, United States, 4Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

Compressed sensing algorithms for accelerating DSI acquisitions (DSI-CS) have helped bring DSI into the realm of clinical feasibility. Here, we assess the efficacy of dictionary-based CS methods in reconstructing high resolution ex vivo DSI of human brain blocks, and provide validation of ex vivo DSI-CS with ground truth optical imaging. We find that reconstruction accuracy, computation time and inter-subject dictionary generalizability are comparable to in vivo results, and that SNR appears influential in determining the limit of attainable reconstruction quality. We also show that fiber orientation estimates of reconstructed data are as accurate as fully-sampled estimates at a microscopic level.

This abstract and the presentation materials are available to members only; a login is required.

Join Here