Trauma and pathological involvement can result in major disruption of the face and skull’s complex 3D bony and soft tissue structure. Accurate craniofacial reconstruction is challenging when there is insufficient patient-specific 3D face and skeletal shape information, especially for severe bilateral cases. The objective of the research in this thesis is to develop computer-assisted pre-operative planning and intra-operative workflow for ‘outside-in’ craniofacial reconstruction to restore pre-injury/pathology morphology, including improving accuracy in related nasal reconstruction, rhinoplasty and facial reanimation procedures.

To estimate 3D facial morphology from 2D photos for bilateral cases, the accuracy of morphable models was investigated to quantify the spatial distribution of error by evaluating a database of 100 subjects’ 3D faces.

To account for a patient’s smile and provide guidance to facial reanimation, a 3D analysis quantified lip and cheek movement at 4 increasing smile intensities, where small differences were found between sexes and races.

Applying 3D pre-operative planning to surgical workflow, a newly developed CAD-CAM process was utilized in a case series of five patients undergoing nasal reconstruction. 3D digital noses were flattened to create 2D templates, printed 1:​1 scale and successfully used as an intra-operative guide to shape forehead flaps for nasal reconstruction.

With the aim of improving the assessment of nasal symmetry, a deviation measurement algorithm was developed and validated on a controlled asymmetrical nose model. The average maximum nasal deviation for 100 subjects was evaluated to provide context for clinical assessment.

To infer the underlying 3D skull morphology from a 3D facial surface, a forensics tissue depth model was ‘reverse-engineered’ on 33 head CT images. A new algorithm was developed to improve measuring tissue depths perpendicular to the face and demonstrated that a 3D skull shape can be inferred with low error for the outermost craniofacial skeleton.

Overall, this thesis presents the development and validation of new algorithms to improve pre-operative planning and intra-operative workflow in craniofacial reconstruction. This research leverages personal photos, repurposes non-clinical software and generates physical templates in order to improve the accuracy of surgeons to restore craniofacial soft tissue and skeletal structures to their pre-injury/ pathology state.