Estimating the sample size required to detect an arterial spin labelling magnetic resonance imaging perfusion abnormality in voxel-wise group analyses.
BACKGROUND
Voxel-based analyses are pervasive across the range of neuroimaging techniques. In the case of perfusion imaging using arterial spin labelling (ASL), a low signal-to-noise technique, there is a tradeoff between the contrast-to-noise required to detect a perfusion abnormality and its spatial localisation. In exploratory studies, the use of an a priori region of interest (ROI), which has the benefit of averaging multiple voxels, may not be justified. Thus the question considered in this study pertains to the sample size that is required to detect a voxel-level perfusion difference between groups and two algorithms are considered.


METHODS
Empirical 3T ASL data were acquired from 25 older adults and simulations were performed based on the group template cerebral blood flow (CBF) images. General linear model (GLM) and permutation-based algorithms were tested for their ability to detect a predefined hypoperfused ROI. Simulation parameters included: inter and intra-subject variability, degree of hypoperfusion and sample size. The true positive rate was used as a measure of sensitivity.


RESULTS
For a modest group perfusion difference, i.e., 10%, 37 participants per group were required when using the permutation-based algorithm, whereas 20 participants were required for the GLM-based algorithm.


METHODS
This study advances the perfusion power calculation literature by considering a voxel-wise analysis with correction for multiple comparison.


CONCLUSIONS
The sample size requirement to detect group differences decreased exponentially in proportion to increased degree of hypoperfusion. In addition, sensitivity to detect a perfusion abnormality was influenced by the choice of algorithm.
Department of Speech Language Pathology, University of Toronto, 160-500 University Avenue, Toronto, ON M5G 1V7, Canada; Canadian Partnership for Stroke Recovery, Canada.
Anna M
AM
Mersov
Canadian Partnership for Stroke Recovery, Canada.
David E
DE
Crane
Institute of Biomedical Engineering, University of Oxford, Headington, Oxford OX3 7DQ, UK.
Michael A
MA
Chappell
Canadian Partnership for Stroke Recovery, Canada; LC Campbell Cognitive Neurology Research Unit, Brain Science Research Program, Canada.
Sandra E
SE
Black
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Canadian Partnership for Stroke Recovery, Canada; Physical Sciences, Sunnybrook Research Institute, Canada. Electronic address: bmac@sri.utoronto.ca.
Bradley J
BJ
MacIntosh
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D016022:Case-Control Studies; D002560:Cerebrovascular Circulation / Q000502:physiology*; D015331:Cohort Studies; D003198:Computer Simulation; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D018401:Sample Size; D013113:Spin Labels
D013113:Spin Labels
Arterial spin labelling; Cerebral blood flow; General linear model; Group comparison; Permutation testing; Region of interest; True positive rate