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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015-11-18

AUTHORS

Vladimir Golkov , Alexey Dosovitskiy , Philipp Sämann , Jonathan I. Sperl , Tim Sprenger , Michael Czisch , Marion I. Menzel , Pedro A. Gómez , Axel Haase , Thomas Brox , Daniel Cremers

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. More... »

PAGES

37-44

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_5

DOI

http://dx.doi.org/10.1007/978-3-319-24553-9_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015907065


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