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Abstract #2058

Novel methods for correcting motion regression errors caused by global intensity changes in scans of cerebrovascular function

Ryan Beckerleg1, Joseph Whittaker1,2, Daniel Gallichan3, and Kevin Murphy1
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom, 2Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 3Cardiff University Brain Research Imaging Centre (CUBRIC), School of Engineering, Cardiff University, Cardiff, United Kingdom


Previously we showed that when global intensity changes (GIC’s) are present in data (e.g., CO2 stimuli during measurement of CVR, ASL tagging), volume registration algorithms misrepresent such signal change as motion. Here we look at motion estimates derived from VRA’s, an external marker-less camera, and novel data-derived techniques, to evaluate if this problem can be overcome. We determined that in most cases where GIC’s are present, the use of an ICA in correction can improve erroneous results from VRA estimates. This isn’t the case in all scans however and the GIC causing this error should be removed prior to correction.

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