Craniosynostosis is the premature fusion of cranial sutures and occurs in approximately one in 2000 live births. It results in cranial malformation that can lead to elevated intra-cranial pressure, brain growth impairment, and developmental deficiency. The most common treatment option for craniosynostosis is surgery. Currently, surgical treatment planning of craniosynostosis is mostly qualitative, subjective and irreproducible guided mainly by the surgeon's experience. While virtual planning has been successfully introduced in niche areas of craniofacial surgery, such as corrective jaw surgery applications, clinical tools that provide intuitive and reproducible evaluation of cranial morphology to guide cranial vault remodeling do not yet exist. To cover this gap in current clinical practice, we are developing a personalized preoperative planning for infants with craniosynostosis that allows for decreased operative time and blood loss, thereby reducing perioperative morbidity, but also facilitates an optimized and more durable long-term outcome. In our Phase I project, we designed and developed the first prototype of iCSPlan, an intelligent surgical graphic interface for optimal planning of cranial vault remodeling. Using iCSPlan, we enabled quantitative surgical outcome analysis and compared pre- and post-operative images from patients with different types of craniosynostosis to determine the change in cranial malformations using specific clinical reconstructive techniques, and compared with our simulated results. Experimental results showed that our method gives consistent evaluation with the observed clinical outcome. Results also indicated that with quantitative assistance, less invasive surgery can be performed. The Phase I proof-of concept study successfully achieved its aims, and the results obtained have provided a much needed understanding of the quantitative challenges and opportunities in cranial remodeling. In this Phase II submission, we will refine and implement the studies and tools needed to translate our proof-of-concept results for multi-center clinical trials. We will extend the iCSPlan prototype to incorporate a model of th normal brain growth, integrate quantitative guidance for bone cutting and replacement, and develop a radiation-free post-operative outcome assessment module. A clinical feasibility study will be conducted at Children's National Health System. In summary, our software technology enables intuitive and precise surgical planning to guide surgeons to obtain optimal and reproducible post-operative outcomes in the treatment of craniosynostosis. The technology allows quantitative surgical outcome analysis to determine the efficacy and durability of specific reconstructive technique. By integrating surgical planning and evaluation, iCSPlan will enable more efficient surgery with improved patient outcome. At the successful completion of these aims, we will have completed the groundwork needed to launch the commercialization effort.
Craniosynostosis is the premature fusion of cranial sutures and occurs in approximately one in 2000 live births. It results in cranial malformation that can lead to elevated intra-cranial pressure, brain growth impairment, and developmental deficiency. The most common treatment option for craniosynostosis is surgery. The standard techniques for surgical treatment of craniosynostosis are qualitative, subjective and guided mainly by surgeon's experience. For precise, efficient and reproducible outcomes, we will create and validate a software technology that enables optimal surgical guidance for craniosynostosis and quantitative evaluation of outcomes using image analysis techniques.
|Porras, Antonio R; Paniagua, Beatriz; Ensel, Scott et al. (2018) Locally Affine Diffeomorphic Surface Registration and Its Application to Surgical Planning of Fronto-Orbital Advancement. IEEE Trans Med Imaging 37:1690-1700|
|Tu, Liyun; Porras, Antonio R; Ensel, Scott et al. (2017) Intracranial Volume Quantification from 3D Photography. Comput Assist Robot Endosc Clin Image Based Proced (2017) 10550:116-123|
|Porras, Antonio R; Paniagua, Beatriz; Enquobahrie, Andinet et al. (2017) Locally affine diffeomorphic surface registration for planning of metopic craniosynostosis surgery. Med Image Comput Comput Assist Interv 10434:479-487|
|Porras, Antonio R; Zukic, D; Equobahrie, A et al. (2016) Personalized Optimal Planning for the Surgical Correction of Metopic Craniosynostosis. Clin Image Based Proced 2016:60-67|