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- ASLPrep: A Robust Preprocessing Pipeline for ASL Data — aslprep version . . .
ASLPrep: A Robust Preprocessing Pipeline for ASL Data This pipeline is developed by the Satterthwaite lab at the University of Pennsylvania for use at the The Lifespan Informatics and Neuroimaging Center at the University of Pennsylvania, as well as for open-source software distribution
- Processing pipeline details — aslprep version documentation
ASLPrep can process data from any of the big three manufacturers (Siemens, Philips, GE), but the GE ASL product sequence is unique in that it typically only produces a single deltaM or CBF volume (optionally along with an M0 volume)
- Usage Notes — aslprep version documentation
If you have a question about using ASLPrep, please create a new topic on NeuroStars with the “Software Support” category and the “aslprep” tag The ASLPrep developers follow NeuroStars, and will be able to answer your question there
- aslprep. config module — aslprep version documentation
aslprep config module A Python module to maintain unique, run-wide aslprep settings This module implements the memory structures to keep a consistent, singleton config
- aslprep. interfaces. cbf module — aslprep version documentation
aslprep interfaces cbf module Interfaces for calculating CBF class BASILCBF(**inputs) [source] Bases: FSLCommand Wrapped executable: oxford_asl Apply Bayesian Inference for Arterial Spin Labeling (BASIL)
- aslprep. interfaces. bids module — aslprep version documentation
Bases: DerivativesDataSink Store derivative files A child class of the niworkflows DerivativesDataSink, using aslprep’s configuration files Mandatory Inputs: in_file (a list of items which are a pathlike object or string representing an existing file) – The object to be saved
- aslprep. workflows. asl. hmc module — aslprep version documentation
ASLPrep uses volume type-wise motion correction 1 instead of the zig-zag regression approach 2 because it is unclear how M0 volumes should be treated in the zig-zag method
- ASLPrep: A Robust Preprocessing Pipeline for ASL Data
ASLPrep adapts the preprocessing steps depending on the input dataset and provide results as good as possible independently of scanner make and scanning parameters With the BIDS input, little or no parameter are required allowing ease of operation
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