Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy.
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. Hum Brain Mapp 37:3795-3809, 2016. © 2016 Wiley Periodicals, Inc.
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia. Alex.Pagnozzi@csiro.au.|School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia. Alex.Pagnozzi@csiro.au.
Alex M
AM
Pagnozzi
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
Kaikai
K
Shen
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
James D
JD
Doecke
Queensland Cerebral Palsy and Rehabilitation Research Centre, School of Medicine, The University of Queensland, Brisbane, Australia.
Roslyn N
RN
Boyd
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
Andrew P
AP
Bradley
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
Stephen
S
Rose
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
Nicholas
N
Dowson
D000293:Adolescent; D019540:Area Under Curve; D002547:Cerebral Palsy / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000523:psychology; D002552:Cerebral Ventricles / Q000000981:diagnostic imaging* / Q000254:growth & development; D002648:Child; D002675:Child, Preschool; D003142:Communication; D056344:Executive Function; D005260:Female; D066128:Gray Matter / Q000000981:diagnostic imaging* / Q000254:growth & development; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008953:Models, Anatomic; D008959:Models, Neurological; D009043:Motor Activity; D009929:Organ Size; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012044:Regression Analysis
cerebral palsy; magnetic resonance imaging; statistical shape model; ventricular enlargement