Open AccessRobust head CT image registration pipeline for craniosynostosis skull correction surgery
Author(s) -
Dangi Shusil,
Shah Hina,
Porras Antonio R.,
Paniagua Beatriz,
Linte Cristian A.,
Linguraru Marius,
Enquobahrie Andinet
Publication year - 2017
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2017.0067
Subject(s) - skull , craniosynostosis , image registration , atlas (anatomy) , computer vision , robustness (evolution) , artificial intelligence , computer science , medicine , pipeline (software) , radiology , nuclear medicine , surgery , anatomy , image (mathematics) , biochemistry , chemistry , gene , programming language
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.