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Abstract #0275

Subtraction free arterial spin labeling: a new Bayesian-inference based approach for gaining perfusion data from time encoded data

Federico C A von Samson-Himmelstjerna 1,2 , Michael A Chappell 3 , Jan Sobesky 2 , and Matthias Gnther 1

1 Fraunhofer MEVIS, Bremen, Bremen, Germany, 2 Center for Stroke Research (CSB), Charit University Medicine Berlin, Berlin, Berlin, Germany, 3 Institute of Biomedical Engineering & FMRIB Centre, University of Oxford, Oxforshire, United Kingdom

A new signal model for time-encoded ASL-data in combination with Bayesian inference is proposed. It allows gaining kinetic perfusion information like cerebral blood flow and arterial transit time without subtraction and/or addition of images, even from incomplete or corrupted datasets. The model was tested in vivo using a 7x8 Walsh-Hadamard matrix for encoding the bolus. The resultant maps were then compared to reference maps from a classical multi-TI measurement. A very good agreement, even for data from an incomplete dataset was found. This makes the approach especially suited for clinical setups where data corruption e.g. by motion is common.

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