q-space deep learning for twelve-fold shorter and model-free diffusion MRI scans

Golkov V, Dosovitskiy A, Saemann P, Sperl JI, Sprenger T, Czisch M, Menzel MI, Gomez PA, Haase A, Brox T, Cremers D (2015)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2015

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Series: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Book Volume: 9349

Pages Range: 37-44

DOI: 10.1007/978-3-319-24553-9_5

Abstract

Diffusion MRI uses a multi-step data processing pipeline. With certain steps being prone to instabilities, the pipeline relies on considerable amounts of partly redundant input data, which requires long acquisition time. This leads to high scan costs and makes advanced diffusion models such as diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) inapplicable for children and adults who are uncooperative, uncomfortable or unwell. We demonstrate how deep learning, a group of algorithms in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This method allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Albert-Ludwigs-Universität Freiburg DE Germany (DE) Max-Planck-Institut für Psychiatrie (Deutsche Forschungsanstalt für Psychiatrie) DE Germany (DE) General Electric Global Research US United States (USA) (US)

How to cite

APA:

Golkov, V., Dosovitskiy, A., Saemann, P., Sperl, J.I., Sprenger, T., Czisch, M.,... Cremers, D. (2015). q-space deep learning for twelve-fold shorter and model-free diffusion MRI scans. In (pp. 37-44). Springer Verlag.

MLA:

Golkov, Vladimir, et al. "q-space deep learning for twelve-fold shorter and model-free diffusion MRI scans." Springer Verlag, 2015. 37-44.

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