Chapter title |
Personalized Optimal Planning for the Surgical Correction of Metopic Craniosynostosis
|
---|---|
Chapter number | 8 |
Book title |
Clinical Image-Based Procedures. Translational Research in Medical Imaging
|
Published in |
Clinical image-based procedures : from planning to intervention : international workshop, CLIP ..., held in conjunction with MICCAI ... : revised selected papers. CLIP (Workshop), October 2016
|
DOI | 10.1007/978-3-319-46472-5_8 |
Pubmed ID | |
Book ISBNs |
978-3-31-946471-8, 978-3-31-946472-5
|
Authors |
Antonio R. Porras, Dženan Zukic, Andinet Equobahrie, Gary F. Rogers, Marius George Linguraru |
Abstract |
We introduce a quantitative and automated method for personalized cranial shape remodeling via fronto-orbital advancement surgery. This paper builds on an objective method for automatic quantification of malformations caused by metopic craniosynostosis in children and presents a framework for personalized interventional planning. First, skull malformations are objectively quantified using a statistical atlas of normal cranial shapes. Then, we propose a method based on poly-rigid image registration that takes into account both the clinical protocol for fronto-orbital advancement and the physical constraints in the skull to plan the creation of the optimal post-surgical shape. Our automated surgical planning technique aims to minimize cranial malformations. The method was used to calculate the optimal shape for 11 infants with age 3.8±3.0 month old presenting metopic craniosynostosis and cranial malformations. The post-surgical cranial shape provided for each patient presented a significant average malformation reduction of 49% in the frontal cranial bones, and achieved shapes whose malformations were within healthy ranges. To our knowledge, this is the first work that presents an automatic framework for an objective and personalized surgical planning for craniosynostosis treatment. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 35 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 26% |
Other | 4 | 11% |
Student > Ph. D. Student | 4 | 11% |
Student > Doctoral Student | 3 | 9% |
Student > Bachelor | 3 | 9% |
Other | 3 | 9% |
Unknown | 9 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 12 | 34% |
Medicine and Dentistry | 5 | 14% |
Computer Science | 4 | 11% |
Neuroscience | 2 | 6% |
Immunology and Microbiology | 1 | 3% |
Other | 2 | 6% |
Unknown | 9 | 26% |