Abstract #2817
Diffusion Spectrum Imaging from Undersampled Data Using Tensor Fitting
Gabriel Varela-Mattatall 1 , Alexandra Tobisch 2,3 , Tony Stoecker 2,4 , and Pablo Irarrazaval 5,6
1
Biomedical Imaging Center, Pontificia
Universidad Catolica de Chile, Santiago, Metropolitan
District, Chile,
2
German
Center for Neurodegenerative Diseases, North
Rhine-Westphalia, Germany,
3
Department
of Computer Science, University of Bonn, North
Rhine-Westphalia, Germany,
4
Department
of Physics and Astronomy, University of Bonn, North
Rhine-Westphalia, Germany,
5
Biomedical
Imaging Center, Pontificia Universidad Catolica de
Chile, Metropolitan District, Chile,
6
Department
of Electrical Engineering, Pontificia Universidad
Catolica de Chile, Metropolitan District, Chile
Compressed Sensing (CS) has been applied to Diffusion
Spectrum Imaging (DSI) in order to accelerate
acquistion, unfortunately, is difficult to assure high
acceleration factors with the conventional DSI-CS
formulation because CS is thought for high resolution
problems, which is not the case in dMRI in general. In
this work we propose a change over the DSI-CS
formulation to improve reconstruction based in applying
CS to reconstruct the differences from a tensor fitted
to the data. This joint method between tensor fitting
and the reconstruction of the differences allows an
improvement in the reconstruction.
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