Personalized Optimal Planning for the Surgical Correction of Metopic Craniosynostosis

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.

Locally Affine Diffeomorphic Surface Registration for Planning of Metopic Craniosynostosis Surgery

“…The proposed registration framework estimates one single transformation between a patient’s cranial shape and its closest normal shape, while it models individually the repositioning and bending of each local bone piece. This work overcomes the previously described shortcomings of [4] by: (1) allowing bone bending in addition to translation and rotation; (2) creating a cranial shape that is not only closer to normality in terms of point-to-point distances but also in terms of global shape and curvature; (3) allowing the surgeons to consider different interventional approaches (i.e. different bone cuts) by introducing bone cut templates to guide the subdivision of the cranial bones into smaller bone pieces.…”

“…In that framework, no single region of the image was constrained to present the same affine behavior, but a weighted combination of them. In [4], the transformation at local bone regions was constrained to be strictly rigid. To that end, signed distance functions (SDF) were calculated for each region on the source image, and the weights w i associated to each local transformation were defined using a logistic regression function applied to each SDF.…”

“…In [4], the registration framework was designed to reduce malformations (defined as the distance between the patient’s cranium and its closest normal shape from a multi-atlas), while curvature information was not considered. In our work, we introduce a global surface-based dissimilarity measure based on currents [9] to compare the patient’s cranium and its closest normal shape.…”

“…A fully automatic method to find the optimal post-surgical shape to target during cranial vault reconstruction surgery was presented in [4], where an image registration framework to calculate an interventional plan that minimizes the malformations in the patient’s cranium was presented. That framework had three shortcomings: (1) the frontal bones were considered as two different rigid objects, but cranial vault remodeling to correct metopic craniosynostosis very often involves bone bending/reshaping [5, 6]; (2) the algorithm, based on volumetric image registration, only took into account the Euclidean distance between the patient’s cranium and its closest normal shape, without considering smoothness and curvature (which is critical to obtain esthetically acceptable surgical results); (3) the method did not consider the division of the frontal bones into smaller bone pieces, which is typically needed to surgically correct cases with severe cranial deformities.…”

Intracranial Volume Quantification from 3D Photography

“…The cranial bones were extracted from CT using the approach described in [3][11]. In summary, a binary image with the bone structures was obtained from CT by thresholding at HU >100.…”