Toggle Main Menu Toggle Search

Open Access padlockePrints

Multi-parametric classification of traumatic brain injury patients using automatic analysis of quantitative MRI scans

Lookup NU author(s): Dr Benjamin Aribisala, Christopher Cowie, Dr Jiabao He, Josh Wood, Emeritus Professor David Mendelow, Patrick Mitchell, Professor Andrew Blamire

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

Traumatic brain injury (TBI) is ranked as the fourth highest cause of death in the developed world. The majority of patients sustain mild TBI, and a significant number suffer persistent neuropsychological problems. Conventional neuroimaging methods (CT, MRI) do not reveal abnormalities consistent with the cognitive symptoms. Imaging methods offering prognostic information in acutely injured patients are therefore required. Here we applied advanced quantitative MRI techniques (T1, T2 mapping and diffusion tensor MRI) in 24 mild TBI patients and 20 matched controls. We applied a support vector machine (SVM) to classify the quantitative MRI data. Univariate classification was ineffective due to overlap between patient and control values, however multi-parametric classification achieved sensitivity of 88% and specificity of 75%. Future work incorporating neuropsychological outcome into SVM training is expected to improve performance. These results indicate that SVM analysis of multi-parametric MRI data is a promising approach for predicting prognosis following mild TBI. © 2010 Springer-Verlag.


Publication metadata

Author(s): Aribisala B, Cowie C, He J, Wood J, Mendelow A, Mitchell P, Blamire A

Editor(s): Liao, H., Edwards, P.J., Pan, X. Fan, Y., Yang , G.Z.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Medical Imaging and Augmented Reality: 5th International Workshop (MIAR)

Year of Conference: 2010

Pages: 51-59

ISSN: 0302-9743 (print) 1611-3349 (online)

Publisher: Springer

URL: http://dx.doi.org/10.1007/978-3-642-15699-1_6

DOI: 10.1007/978-3-642-15699-1_6

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783642156984


Actions

Find at Newcastle University icon    Link to this publication


Share