Abstract
Diffusionweighted imaging (DWI)-based tractography estimates white matter (WM) fibers in the living brain. This tool enables studying brain connectivity between brain regions and planning operations on the brain. XTRACT is a recently developed program for automatically delineating 42 major WM tracts in high-quality DWI data. However, acquiring such data is time-consuming, limiting its clinical application. In this work, we propose using a deep neural network to enhance low-quality DWI data, representative of clinical imaging protocols. We hypothesized that such image enhancement would lead to a substantially more accurate tractography with XTRACT. Our results show that the method increases the correlation of tracts extracted from a clinical dataset with the ones extracted from a high-quality one from 64% to 83%. Clinically, this implies that both more tracts are detected and that the details of detected tracts are higher.
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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Sánchez, M.B. et al. (2023). Improved Tractography by Means of DL-based DWI Image Enhancement. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_17
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DOI: https://doi.org/10.1007/978-3-658-41657-7_17
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