Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article



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


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

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Golkov, V., Sprenger, T., Sperl, J., Menzel, M., Czisch, M., Saemann, P., et al. (2016). MODEL-FREE NOVELTY-BASED DIFFUSION MRI. 2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 1233-1236.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-678D-1
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.