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.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
