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q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

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Saemann,  Philipp
Max Planck Institute of Psychiatry, Max Planck Society;

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Czisch,  Michael
Max Planck Institute of Psychiatry, Max Planck Society;

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Citation

Golkov, V., Dosovitskiy, A., Saemann, P., Sperl, J. I., Sprenger, T., Czisch, M., et al. (2015). q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. In MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT I (pp. 37-44).


Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-341C-D
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.