PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

V. Golkov, A. Dosovitskiy, P. Saemann, JI. Sperl, T. Sprenger, M. Czisch, MI. Menzel, PA. Gómez, A. Haase, T. Brox, D. Cremers
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Munich, Germany, October 2015 (bib)

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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif