CDMRI 2014 - International Workshop on MICCAI 2014 WORKSHOP ON COMPUTATIONAL DIFFUSION MRI

Over the last decade interest in diffusion MRI has exploded. The technique provides a unique insight into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Microstructural changes are often the earliest signs of disease or tissue regeneration, as well as being manifestation of physiological processes in normal tissue functioning. Tractography and connectivity mapping give fundamental new insights in neuroscience and neuroanatomy. The variety of clinical applications is expanding rapidly and includes detection of lesions and damaged tissue, prognosis of functional impairment and neurosurgical planning.
Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. The workshop will give a snapshot of the current state of the art.
We are pleased to announce that the proceedings of CDMRI'14 will be published by Springer as a volume in the Mathematics and Visualization series.
This year we are also holding a "Sparse Reconstruction Challenge for dMRI" (SPARC dMRI) to allow the community to test their algorithms on physical phantom data. Visit the challenge website.
WORKSHOP TOPICS
Full-length papers are invited in (but not limited to) the following areas:
Acquisition protocol design
High angular resolution and general q-space sampling techniques
Biophysical models
Numerical simulation of diffusion process
Tissue microstructure imaging
Tractography and connectivity mapping
Network analysis
Registration, segmentation, and classification
Multimodality modeling of diffusion and functional or genetic data
Visualization
Validation
Post-processing
Group studies and statistical analysis
Clinical applications