Using ventricular modeling to robustly probe significant deep gray matter pathologies: application to cerebral palsy

Pagnozzi, Alex M., Shen, Kaikai, Doecke, James D., Boyd, Roslyn N., Bradley, Andrew P., Rose, Stephen and Dowson, Nicholas (2016) Using ventricular modeling to robustly probe significant deep gray matter pathologies: application to cerebral palsy. Human Brain Mapping, 37 11: 3795-3809. doi:10.1002/hbm.23276


Author Pagnozzi, Alex M.
Shen, Kaikai
Doecke, James D.
Boyd, Roslyn N.
Bradley, Andrew P.
Rose, Stephen
Dowson, Nicholas
Title Using ventricular modeling to robustly probe significant deep gray matter pathologies: application to cerebral palsy
Journal name Human Brain Mapping   Check publisher's open access policy
ISSN 1097-0193
1065-9471
Publication date 2016-11-01
Sub-type Article (original research)
DOI 10.1002/hbm.23276
Open Access Status Not yet assessed
Volume 37
Issue 11
Start page 3795
End page 3809
Total pages 15
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Collection year 2017
Language eng
Formatted abstract
Understanding the relationships between the structure and function of the brain largely relies on the qualitative assessment of Magnetic Resonance Images (MRIs) by expert clinicians. Automated analysis systems can support these assessments by providing quantitative measures of brain injury. However, the assessment of deep gray matter structures, which are critical to motor and executive function, remains difficult as a result of large anatomical injuries commonly observed in children with Cerebral Palsy (CP). Hence, this article proposes a robust surrogate marker of the extent of deep gray matter injury based on impingement due to local ventricular enlargement on surrounding anatomy. Local enlargement was computed using a statistical shape model of the lateral ventricles constructed from 44 healthy subjects. Measures of injury on 95 age-matched CP patients were used to train a regression model to predict six clinical measures of function. The robustness of identifying ventricular enlargement was demonstrated by an area under the curve of 0.91 when tested against a dichotomised expert clinical assessment. The measures also showed strong and significant relationships for multiple clinical scores, including: motor function (r2 = 0.62, P < 0.005), executive function (r2 = 0.55, P < 0.005), and communication (r2 = 0.50, P < 0.005), especially compared to using volumes obtained from standard anatomical segmentation approaches. The lack of reliance on accurate anatomical segmentations and its resulting robustness to large anatomical variations is a key feature of the proposed automated approach. This coupled with its strong correlation with clinically meaningful scores, signifies the potential utility to repeatedly assess MRIs for clinicians diagnosing children with CP. 
Keyword Cerebral palsy
Magnetic resonance imaging
Statistical shape model
Ventricular enlargement
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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