ASLPrep, A Robust Preprocessing Pipeline for ASL Data
ASLPrep is an Arterial Spin Labeling (ASL) data preprocessing and Cerebral Blood FLow (CBF) computation pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.), CBF computation, denoising CBF, CBF partial volume correction and providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state CBF, graph theory measures, surface or volume-based statistics, etc.
Azeez Adebimpe, Maxwell Bertolero, & Matthew Cieslak
Maxwell Bertolero, Matthew Cieslak
Maxwell Bertolero, Sudipto Dolui, Matthew Cieslak, Kristin Murtha, Erica B. Baller, Bradley Boeve, Adam Boxer, Ellyn R. Butler, Phil Cook, Stan Colcombe, Sydney Covitz, Christos Davatzikos, Diego G. Davila, Mark A. Elliott, Matthew W. Flounders, Alexandre R. Franco, Raquel E. Gur, Ruben C. Gur, Basma Jaber, Corey McMillian, the ALLFTD Consortium, Michael Milham, Henk J.M.M. Mutsaerts, Desmond J. Oathe, Christopher A. Olm, Jeffrey S. Phillips, Will Tackett, David R. Roalf, Howard Rosen, Tinashe M. Tapera, M. Dylan Tisdall, Oscar Esteban, Russell A. Poldrack, John A. Detre,
Published May 2022 in Nature Methods. Code for figures can be found at PennLINC/aslprep_paper
NKI, PNC, AGE, IRR, FTD