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Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx.

Ze Wang1, Geoffrey K Aguirre, Hengyi Rao

  • 1Center for Functional Neuroimaging and Department of Neurology, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. zewang@mail.med.upenn.edu <zewang@mail.med.upenn.edu>

Magnetic Resonance Imaging
|September 11, 2007
PubMed
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Optimizing arterial spin labeling (ASL) perfusion fMRI analysis requires specific processing strategies. Key factors include using at least 10 bits for data storage and performing cerebral blood flow calculations before spatial normalization for improved signal detection.

Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)

Background:

  • Arterial spin labeling (ASL) perfusion fMRI data require distinct processing compared to blood oxygen level-dependent (BOLD) fMRI.
  • Understanding optimal ASL data analysis is crucial for accurate interpretation of brain activity.

Purpose of the Study:

  • To investigate factors influencing ASL data analysis for optimal signal detection.
  • To identify best practices for preprocessing ASL data to enhance cerebral blood flow (CBF) calculations and task activation detection.

Main Methods:

  • ASL perfusion fMRI data were collected at 3 Tesla from 10 subjects performing a sensorimotor task.
  • Analysis involved systematic variations of data storage bit resolution, motion correction, CBF calculation timing, and nuisance covariate modeling.
  • Evaluated simple subtraction versus sinc subtraction and independent realignment of label/control images.

Related Experiment Videos

Main Results:

  • Using at least 10 bits for data storage significantly improved statistical power at 3 T.
  • Simple subtraction showed a slightly larger peak t value in visual cortex compared to sinc subtraction.
  • Performing CBF calculations before spatial normalization and modeling global fluctuations significantly increased peak t values in motor cortex.

Conclusions:

  • Optimal ASL data processing involves specific strategies for data storage, CBF calculation timing, and nuisance covariate modeling.
  • The open-source ASLtbx toolbox facilitates the implementation of these recommended ASL data processing approaches.