Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects

Deqiang Xiao, Li Wang, Hannah H. Deng, Kim-Han Thung, Jihua Zhu, Peng Yuan, Yriu Lourenco Rodrigues, Leoned Perez Jr., Christopher E. Crecelius, Jaime Gateno, T. Kuang, Steve G F Shen, Daeseung Kim, David Alfi, Pew-Thian Yap, James J. Xia, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient’s pre-traumatic photographs. An initial estimation of the patient’s normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient’s normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.

Keywords

  • Adaptive-focus deformable shape model (AFDSM)
  • Craniomaxillofacial (CMF)
  • Facial bone estimation
  • Simulation
  • Sparse representation
  • Surgical planning
  • Three-dimensional facial reconstruction
  • Trauma

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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