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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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