ASLPrep, A Robust Preprocessing Pipeline for ASL Data
ASLPrep, A Generalizable Platform for Processing of Arterial Spin Labeled MRI and Quantification of Regional Brain Perfusion
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.
Project Lead(s)
Azeez Adebimpe, Maxwell Bertolero, & Matthew Cieslak
Faculty Lead(s)
Satterthwaite
Analytic Replicator
Maxwell Bertolero, Matthew Cieslak
Collaborators
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,
Project Start Date
Early 2020
Current Project Status
Published May 2022 in Nature Methods. Code for figures can be found at PennLINC/aslprep_paper
Dataset
NKI, PNC, AGE, IRR, FTD
Github repo
Path to data on filesystem
/cbica/projects/aslprep
Slack Channel
$pennlinc_asl