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Healthc Technol Lett. 2017 Sep 14;4(5):174-178. doi: 10.1049/htl.2017.0067. eCollection 2017 Oct.

Robust head CT image registration pipeline for craniosynostosis skull correction surgery.

Author information

1
Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.
2
Kitware Inc., Carrboro, NC, USA.
3
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Health System, Washington, DC, USA.
4
Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA.
5
School of Medicine and Health Sciences, George Washington University, Washington, DC, USA.

Abstract

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.

KEYWORDS:

biomechanics; bone; computerised tomography; congenital malformation; corrective surgery; craniosynostosis skull correction surgery; deformation; image registration; image resolution; infant skull; initial optimisation; mean surface-to-surface distance; medical image processing; normal CT images; optimal correction strategy; optimisation; patient-specific skull model extraction; patient-specihc CT image; presurgical computed tomography image; robust head CT image registration pipeline; robust multistage multiresolution registration pipeline; surgery; targeted skull region; template skull; very low resolution

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