Abstract #2774
ActiveAx using dictionary learning with electron microscopy validation
Farshid Sepehrband 1,2 , Daniel C Alexander 3 , Nyoman D Kurniawan 1 , David C Reutens 1 , and Zhengyi Yang 1,4
1
Centre for Advanced Imaging, University of
Queensland, Brisbane, Queensland, Australia,
2
Queensland
Brain Institute, University of Queensland, Brisbane,
Queensland, Australia,
3
Department
of Computer Science & Centre for Medical Image
Computing, University College London, London, United
Kingdom,
4
School
of Information Technology and Electrical Engineering,
University of Queensland, Brisbane, Queensland,
Australia
The ActiveAx, a model-based technique, fits minimal
white matter model to diffusion MRI data to obtain
orientationally invariant indices of axon diameter and
density. The fitting procedure is a limitation in such
parametric approaches, because various independent
parameters have a similar effect on the acquired signal,
which may affect the precision of the estimated
measures. In this work we propose a dictionary learning
approach to tackle this hurdle. We tested our method
using ex vivo imaging of the mouse brain (with maximum
b-value of 105,000 s/mm
2
), and compared our
estimated values with electron microscopy.
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