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,...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
John Wiley and Sons Inc.
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/5848 |
| _version_ | 1848744910605254656 |
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| author | Pagnozzi, A. Shen, K. Doecke, J. Boyd, Roslyn Bradley, A. Rose, S. Dowson, N. |
| author_facet | Pagnozzi, A. Shen, K. Doecke, J. Boyd, Roslyn Bradley, A. Rose, S. Dowson, N. |
| author_sort | Pagnozzi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T06:08:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-5848 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:08:58Z |
| publishDate | 2016 |
| publisher | John Wiley and Sons Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-58482017-09-13T14:39:53Z Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy Pagnozzi, A. Shen, K. Doecke, J. Boyd, Roslyn Bradley, A. Rose, S. Dowson, N. 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. 2016 Journal Article http://hdl.handle.net/20.500.11937/5848 10.1002/hbm.23276 John Wiley and Sons Inc. restricted |
| spellingShingle | Pagnozzi, A. Shen, K. Doecke, J. Boyd, Roslyn Bradley, A. Rose, S. Dowson, N. Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy |
| title | Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy |
| title_full | Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy |
| title_fullStr | Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy |
| title_full_unstemmed | Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy |
| title_short | Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy |
| title_sort | using ventricular modeling to robustly probe significant deep gray matter pathologies: application to cerebral palsy |
| url | http://hdl.handle.net/20.500.11937/5848 |