Robust head CT image registration pipeline for craniosynostosis skull correction surgery
Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image nee...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-08-01
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| Series: | Healthcare Technology Letters |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0067 |
| _version_ | 1818406866934300672 |
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| author | Shusil Dangi Hina Shah Antonio R. Porras Beatriz Paniagua Cristian A. Linte Marius Linguraru Andinet Enquobahrie |
| author_facet | Shusil Dangi Hina Shah Antonio R. Porras Beatriz Paniagua Cristian A. Linte Marius Linguraru Andinet Enquobahrie |
| author_sort | Shusil Dangi |
| collection | DOAJ |
| description | Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image needs to be registered to an atlas of head CT images representative of normal subjects. Here, the authors present a robust multi-stage, multi-resolution registration pipeline to map a patient-specific CT image to the atlas space of normal CT images. The proposed registration pipeline first performs an initial optimisation at very low resolution to yield a good initial alignment that is subsequently refined at high resolution. They demonstrate the robustness of the proposed method by evaluating its performance on 560 head CT images of 320 normal subjects and 240 craniosynostosis patients and show a success rate of 92.8 and 94.2%, respectively. Their method achieved a mean surface-to-surface distance between the patient and template skull of <2.5 mm in the targeted skull region across both the normal subjects and patients. |
| first_indexed | 2024-12-14T09:18:45Z |
| format | Article |
| id | doaj.art-2eff40136449406c84fae545197d75c2 |
| institution | Directory Open Access Journal |
| issn | 2053-3713 |
| language | English |
| last_indexed | 2024-12-14T09:18:45Z |
| publishDate | 2017-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Healthcare Technology Letters |
| spelling | doaj.art-2eff40136449406c84fae545197d75c22022-12-21T23:08:22ZengWileyHealthcare Technology Letters2053-37132017-08-0110.1049/htl.2017.0067HTL.2017.0067Robust head CT image registration pipeline for craniosynostosis skull correction surgeryShusil Dangi0Hina Shah1Antonio R. Porras2Beatriz Paniagua3Cristian A. Linte4Marius Linguraru5Andinet Enquobahrie6Chester F. Carlson Center for Imaging Science, Rochester Institute of TechnologyKitware Inc.Children's National Health SystemKitware Inc.Chester F. Carlson Center for Imaging Science, Rochester Institute of TechnologyChildren's National Health SystemKitware Inc.Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image needs to be registered to an atlas of head CT images representative of normal subjects. Here, the authors present a robust multi-stage, multi-resolution registration pipeline to map a patient-specific CT image to the atlas space of normal CT images. The proposed registration pipeline first performs an initial optimisation at very low resolution to yield a good initial alignment that is subsequently refined at high resolution. They demonstrate the robustness of the proposed method by evaluating its performance on 560 head CT images of 320 normal subjects and 240 craniosynostosis patients and show a success rate of 92.8 and 94.2%, respectively. Their method achieved a mean surface-to-surface distance between the patient and template skull of <2.5 mm in the targeted skull region across both the normal subjects and patients.https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0067image registrationbonesurgerymedical image processingcomputerised tomographydeformationbiomechanicsimage resolutionoptimisationrobust head CT image registration pipelinecraniosynostosis skull correction surgerycongenital malformationinfant skullcorrective surgerydeformationoptimal correction strategypatient-specific skull model extractionpresurgical computed tomography imagerobust multistage multiresolution registration pipelinepatient-specihc CT imagenormal CT imagesinitial optimisationvery low resolutionmean surface-to-surface distancetemplate skulltargeted skull region |
| spellingShingle | Shusil Dangi Hina Shah Antonio R. Porras Beatriz Paniagua Cristian A. Linte Marius Linguraru Andinet Enquobahrie Robust head CT image registration pipeline for craniosynostosis skull correction surgery Healthcare Technology Letters image registration bone surgery medical image processing computerised tomography deformation biomechanics image resolution optimisation robust head CT image registration pipeline craniosynostosis skull correction surgery congenital malformation infant skull corrective surgery deformation optimal correction strategy patient-specific skull model extraction presurgical computed tomography image robust multistage multiresolution registration pipeline patient-specihc CT image normal CT images initial optimisation very low resolution mean surface-to-surface distance template skull targeted skull region |
| title | Robust head CT image registration pipeline for craniosynostosis skull correction surgery |
| title_full | Robust head CT image registration pipeline for craniosynostosis skull correction surgery |
| title_fullStr | Robust head CT image registration pipeline for craniosynostosis skull correction surgery |
| title_full_unstemmed | Robust head CT image registration pipeline for craniosynostosis skull correction surgery |
| title_short | Robust head CT image registration pipeline for craniosynostosis skull correction surgery |
| title_sort | robust head ct image registration pipeline for craniosynostosis skull correction surgery |
| topic | image registration bone surgery medical image processing computerised tomography deformation biomechanics image resolution optimisation robust head CT image registration pipeline craniosynostosis skull correction surgery congenital malformation infant skull corrective surgery deformation optimal correction strategy patient-specific skull model extraction presurgical computed tomography image robust multistage multiresolution registration pipeline patient-specihc CT image normal CT images initial optimisation very low resolution mean surface-to-surface distance template skull targeted skull region |
| url | https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0067 |
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