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Proceedings Paper

Radiation-free quantification of head malformations in craniosynostosis patients from 3D photography
Author(s): Liyun Tu; Antonio R. Porras; Albert Oh; Natasha Lepore; Manuel Mastromanolis; Deki Tsering; Beatriz Paniagua; Andinet Enquobahrie; Robert Keating; Gary F. Rogers; Marius George Linguraru
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Paper Abstract

The evaluation of cranial malformations plays an essential role both in the early diagnosis and in the decision to perform surgical treatment for craniosynostosis. In clinical practice, both cranial shape and suture fusion are evaluated using CT images, which involve the use of harmful radiation on children. Three-dimensional (3D) photography offers noninvasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. The aim of this study is to develop an automated framework to objectively quantify cranial malformations in patients with craniosynostosis from 3D photography. We propose a new method that automatically extracts the cranial shape by identifying a set of landmarks from a 3D photograph. Specifically, it registers the 3D photograph of a patient to a reference template in which the position of the landmarks is known. Then, the method finds the closest cranial shape to that of the patient from a normative statistical shape multi-atlas built from 3D photographs of healthy cases, and uses it to quantify objectively cranial malformations. We calculated the cranial malformations on 17 craniosynostosis patients and we compared them with the malformations of the normative population used to build the multi-atlas. The average malformations of the craniosynostosis cases were 2.68 ± 0.75 mm, which is significantly higher (p<0.001) than the average malformations of 1.70 ± 0.41 mm obtained from the normative cases. Our approach can support the quantitative assessment of surgical procedures for cranial vault reconstruction without exposing pediatric patients to harmful radiation.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751U (27 February 2018); doi: 10.1117/12.2295374
Show Author Affiliations
Liyun Tu, Children's National Health System (United States)
Antonio R. Porras, Children's National Health System (United States)
Albert Oh, Children's National Health System (United States)
Natasha Lepore, Children's Hospital Los Angeles (United States)
Univ. of Southern California, Los Angeles (United States)
Manuel Mastromanolis, Children's National Health System (United States)
Deki Tsering, Children's National Health System (United States)
Beatriz Paniagua, Kitware, Inc. (United States)
Andinet Enquobahrie, Kitware, Inc. (United States)
Robert Keating, Children's National Health System (United States)
Gary F. Rogers, Children’s National Health System (United States)
Marius George Linguraru, Children’s National Health System (United States)
George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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