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Abstract #3727

Comparison of Bayesian and Linear Regression-Based Partial Volume Correction in Single Time Point ASL

Ruth Oliver1, Michael A. Chappell2, 3, David Thomas1, Xavier Golay1

1Institute of Neurology, University College London, London, United Kingdom; 2Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom; 3FMRIB Centre, University of Oxford, Oxford, United Kingdom

Partial volume effects due to low spatial resolution are known to introduce errors in quantification of perfusion estimates using ASL. This is particularly problematic in patients where atrophy is present and cortical thinning occurs. A comparison is made for single-TI data of an existing method that employs adaptive spatial priors, and a linear regression approach that assumes constant perfusion for each tissue over a specified kernel area. Both methods offer good correction, increasing GM perfusion by a factor of 1.7 on average, although the adaptive spatial prior method introduces less smoothing and better preserves detail.