Reconstruction of the mandible from partial inputs for virtual surgery planning. (January 2023)
- Record Type:
- Journal Article
- Title:
- Reconstruction of the mandible from partial inputs for virtual surgery planning. (January 2023)
- Main Title:
- Reconstruction of the mandible from partial inputs for virtual surgery planning
- Authors:
- Gillingham, Ryan L.
Mutsvangwa, Tinashe E.M.
van der Merwe, Johan - Abstract:
- Highlights: Mirroring the contralateral mandible provides a good, individualized reconstruction. Statistical shape models accurately reconstruct missing contralateral and bilateral geometry. Regression based models directly report clinically relevant morphology. Posterior principal component analysis may guide global surface fits to improve local accuracy. Abstract: Statistical Shape Models (SSMs) and Sparse Prediction Models (SPMs) based on regressions between cephalometric measurements were compared against standard practice in virtual surgery planning for reconstruction of mandibular defects. Emphasis was placed on the ability of the models to reproduce clinically relevant metrics. CT scans of 50 men and 50 women were collected and split into training and testing datasets according to an 80:20 ratio. The scans were segmented, and anatomical landmarks were identified. SPMs were constructed based on direct regressions between measurements derived from the anatomical landmarks. SSMs were developed by establishing correspondence between the segmented meshes, performing alignment, and principal component analysis. Anterior and bilateral defects were simulated by removing sections of the mandibles in the testing set. Measurement errors after reconstruction ranged from 1.07˚ to 2.2˚ and 0.66 mm to 2.02 mm for mirroring, from 0.45˚ to 3.67˚ and 0.66 mm to 2.54 mm for the SSMs, and from 1.74˚ to 5.01˚ and 0.64 mm to 2.89 mm for the SPMs. Surface-to-surface errors ranged fromHighlights: Mirroring the contralateral mandible provides a good, individualized reconstruction. Statistical shape models accurately reconstruct missing contralateral and bilateral geometry. Regression based models directly report clinically relevant morphology. Posterior principal component analysis may guide global surface fits to improve local accuracy. Abstract: Statistical Shape Models (SSMs) and Sparse Prediction Models (SPMs) based on regressions between cephalometric measurements were compared against standard practice in virtual surgery planning for reconstruction of mandibular defects. Emphasis was placed on the ability of the models to reproduce clinically relevant metrics. CT scans of 50 men and 50 women were collected and split into training and testing datasets according to an 80:20 ratio. The scans were segmented, and anatomical landmarks were identified. SPMs were constructed based on direct regressions between measurements derived from the anatomical landmarks. SSMs were developed by establishing correspondence between the segmented meshes, performing alignment, and principal component analysis. Anterior and bilateral defects were simulated by removing sections of the mandibles in the testing set. Measurement errors after reconstruction ranged from 1.07˚ to 2.2˚ and 0.66 mm to 2.02 mm for mirroring, from 0.45˚ to 3.67˚ and 0.66 mm to 2.54 mm for the SSMs, and from 1.74˚ to 5.01˚ and 0.64 mm to 2.89 mm for the SPMs. Surface-to-surface errors ranged from 1.01 mm to 1.29 mm and 1.06 mm to 1.33 mm for mirroring and SSMs, respectively. Based on the results, SSMs are recommended for VSP in the absence of normal patient anatomy. … (more)
- Is Part Of:
- Medical engineering & physics. Volume 111(2023)
- Journal:
- Medical engineering & physics
- Issue:
- Volume 111(2023)
- Issue Display:
- Volume 111, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 111
- Issue:
- 2023
- Issue Sort Value:
- 2023-0111-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-01
- Subjects:
- Mandible reconstruction -- Cephalometric measurements -- Statistical shape model -- Regression -- Virtual surgery planning
GPA generalized Procrustes analysis -- GPMM gaussian process morphable model -- 2D two-dimensional -- 3D three-dimensional -- CT computed tomography -- PCA principal component analysis -- RMSE root mean square error -- SSM statistical shape model -- SPM sparse prediction model -- VSP virtual surgery planning
Biomedical engineering -- Periodicals
Biomedical Engineering -- Periodicals
Physics -- Periodicals
Génie biomédical -- Périodiques
Biomedical engineering
Electronic journals
Periodicals
610.28 - Journal URLs:
- http://www.medengphys.com ↗
http://www.sciencedirect.com/science/journal/13504533 ↗
http://www.clinicalkey.com/dura/browse/journalIssue/13504533 ↗
http://www.clinicalkey.com.au/dura/browse/journalIssue/13504533 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.medengphy.2022.103934 ↗
- Languages:
- English
- ISSNs:
- 1350-4533
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5527.323000
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