Project: A New Traumatic Axonal Injury Classification Scheme based on Clinical and Improved MR Imaging Biomarkers

Acronym	TAI-MRI
Duration	01/09/2017 - 31/08/2020
Project Topic	The global aim is to develop a novel classification for Traumatic Axonal Injury (TAI) using data from multimodal MR Imaging and to determine its clinical value for characterization of injury severity and prediction of outcome. Methods of deep learning techniques will be developed and used for lesion mapping. The joint venture will utilize MRI dataset from CENTER-TBI and two local studies (, 1400 patients), hence be the largest MRI study. All studies have comprehensive collection of acute phase variables and outcome. Several training sets will be used for model selection, and methods for automated image analyses will be refined and validated. Finally, the model for a possible novel MRI classification will be tested in a large data set. With the development an improved MRI-based classification scheme of TAI lesions, we aim to provide a better assessment of injury severity in the acute phase, and to better predict long-term outcome. Hence, we will provide a timely new tool for neuroradiologists, clinicians and researchers for more precise TBI diagnosis allowing for tailored and cost-efficient treatment and rehabilitation. Furthermore, this project will bring the field forward by increasing our understanding of the pathophysiology in TBI, the link between level of consciousness and injury mechanisms, type and location, and long-term outcome. The recent advancement in automated image analyses provide unique opportunities for a classification with great clinical applicability.
Network	NEURON Cofund
Call	Call for Proposals for "European Research Projects on External Insults to the Nervous System"

Project partner

Number	Name	Role	Country
1	Norwegian University of Science and Technology	Coordinator	Norway
2	University of Antwerp	Partner	Belgium
3	Radboud University Medical Center	Partner	Netherlands
4	University of Cambridge	Partner	United Kingdom