Model-free novelty-based diffusion MRI
Golkov V, Sprenger T, Sperl J, Menzel M, Czisch M, Saemann P, Cremers D (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: IEEE Computer Society
Book Volume: 2016-June
Pages Range: 1233-1236
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Prague, CZE
ISBN: 9781479923502
DOI: 10.1109/ISBI.2016.7493489
Abstract
Many limitations of diffusion MRI are due to the instability of the model fitting procedure. Major shortcomings of the model-based approach are a partial information loss due to model simplicity, long scan time requirements due to fitting instability, and the lack of knowledge about how the parameters of a given model would respond to previously unseen microstructural changes, possibly failing to detect certain previously unseen pathologies. Here we show that diffusion MRI pathology detection is feasible without any models and without any prior knowledge of specific pathological changes whatsoever. Instead, raw q-space measurements are used directly without a model, only healthy population data is used for reference, and any deviations in a patient dataset from the healthy reference database are detected using novelty detection methods. This is done in each voxel independently, i.e. without spatial bias.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
General Electric Global Research
United States (USA) (US)
Max-Planck-Institut für Psychiatrie (Deutsche Forschungsanstalt für Psychiatrie)
Germany (DE)
Germany (DE)
General Electric Global Research
United States (USA) (US)
Max-Planck-Institut für Psychiatrie (Deutsche Forschungsanstalt für Psychiatrie)
Germany (DE)
How to cite
APA:
Golkov, V., Sprenger, T., Sperl, J., Menzel, M., Czisch, M., Saemann, P., & Cremers, D. (2016). Model-free novelty-based diffusion MRI. In Proceedings - International Symposium on Biomedical Imaging (pp. 1233-1236). Prague, CZE: IEEE Computer Society.
MLA:
Golkov, Vladimir, et al. "Model-free novelty-based diffusion MRI." Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, CZE IEEE Computer Society, 2016. 1233-1236.
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