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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