Abstract
In clinical settings, diffusion MRI can be used for extracting biomarkers such as fractional anisotropy or for revealing brain connectivity based on fiber tractography. Both are impacted by the free-water partial volume effect that arises at the border of cerebrospinal fluid or in presence of vasogenic edema. Hence, in order to robustly track white matter fibers close to cerebrospinal fluid and in presence of edema, or to extract consistent biomarkers in these cases, the diffusion MRI signal needs to be corrected for partial volume effects. We present a novel method that reproducibly infers plausible free-water volumes across different diffusion MRI acquisition schemes. Based on simulated data closely following the individual characteristics of each measurement, a neural network is trained on synthetic groundtruth data. According to our evaluation, this methodology produces more consistent and more plausible results than previous approaches.
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Acknowledgments
Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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Weninger, L., Koppers, S., Na, CH., Juetten, K., Merhof, D. (2020). Free-Water Correction in Diffusion MRI: A Reliable and Robust Learning Approach. 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_8
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