Locally Affine Diffeomorphic Surface Registration for Planning of Metopic Craniosynostosis Surgery

Abstract

The outcome of cranial vault reconstruction for the surgical treatment of craniosynostosis heavily depends on the surgeon’s expertise because of the lack of an objective target shape. We introduce a surface-based diffeomorphic registration framework to create the optimal post-surgical cranial shape during craniosynostosis treatment. Our framework estimates and labels where each bone piece needs to be cut using a reference template. Then, it calculates how much each bone piece needs to be translated and in which direction, using the closest normal shape from a multi-atlas as a reference. With our locally affine approach, the method also allows for bone bending, modeling independently the transformation of each bone piece while ensuring the consistency of the global transformation. We evaluated the optimal plan for 15 patients with metopic craniosynostosis. Our results showed that the automated surgical planning creates cranial shapes with a reduction in cranial malformations of 51.43% and curvature discrepancies of 35.09%, which are the two indices proposed in the literature to quantify cranial deformities objectively. In addition, the cranial shapes created were within healthy ranges.

Locally Affine Diffeomorphic Surface Registration and Its Application to Surgical Planning of Fronto-Orbital Advancement

“…In our previous work [18], we proposed a method to incorporate rigid regions in an image-based registration framework based on the transformation model proposed in [16], by incorporating a new weighting scheme controlling the contribution of each local rigid transformation at each region of the image. We then created a preliminary surface-based registration framework in [19] where local regions (i.e. bone pieces) were modeled using affine transformations instead of only rigid.…”

“…We calculate the dissimilarity between the source and target surfaces in the space of currents and we improve the weighting scheme to adjust dynamically to the changes introduced by the transformation at each discretization point during the temporal integration of the velocity field estimated. Compared to [17], our transformation model is region-wise affine and unlike in [19], we model the transformation of each bone piece with reference to its center of mass, which facilitates implementing the transformation of each bone piece to the operating room. We also extend our framework to evaluate different osteotomy plans for the same patient, and we compare the surgical outcome simulated using each osteotomy plan in terms of three different metrics: reduction of malformations, reduction of curvature discrepancies, and minimum bone stress to implement the optimal plan.…”

“…We also extend our framework to evaluate different osteotomy plans for the same patient, and we compare the surgical outcome simulated using each osteotomy plan in terms of three different metrics: reduction of malformations, reduction of curvature discrepancies, and minimum bone stress to implement the optimal plan. We estimated the optimal surgical plan for 8 more patients with respect to our preliminary work presented in [19], evaluating a total of 23 patients with metopic craniosynostosis. Finally, we built a new normative statistical shape multi-atlas with 198 healthy subjects used as a reference to quantify cranial shape abnormalities, thus incorporating 98 healthy cases with respect our previous work [19].…”

“…We estimated the optimal surgical plan for 8 more patients with respect to our preliminary work presented in [19], evaluating a total of 23 patients with metopic craniosynostosis. Finally, we built a new normative statistical shape multi-atlas with 198 healthy subjects used as a reference to quantify cranial shape abnormalities, thus incorporating 98 healthy cases with respect our previous work [19].…”