Abstract
A novel diffusion anisotropy metric is presented. It is based on dissimilarity between the acquired diffusion signal and its isotropic equivalent. Using the inner product of signals, a closed form expression is obtained, which allows its computation using spherical harmonics from a reduced set of acquired data, compatible with most popular diffusion MRI acquisition protocols. Results show that the proposed metric (1) is able to discriminate among different microstructure scenarios; (2) shows a robust behaviour in clinical studies.
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Acknowledgements
This work was supported by Ministerio de Ciencia e Innovación of Spain with research grants RTI2018-094569-B-I00 and PRX18/00253 (Estancias de profesores e investigadores senior en centros extranjeros).
The authors acknowledge Rutger H.J. Fick for providing his personal implementation of the MAP-MRI to estimate the PA. The authors are also grateful to Maryam Afzali from Cardiff University Brain Research Imaging Center for interesting discussion about MAP-MRI model and the multichamber experiment.
Data collection and sharing for this work was provided by (1) the Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California; (2) the High-quality diffusion-weighted imaging of Parkinson’s disease data base, Cyclotron Research Centre, University of Liège.
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Aja-Fernández, S., Tristán-Vega, A., de Luis-García, R., Jones, D.K. (2020). Alternative Diffusion Anisotropy Metric from Reduced MRI Acquisitions. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_2
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