Toward automatic atlas-based surgical planning for septoplasty.

Area of Science:

• Medical Imaging
• Computational Anatomy
• Surgical Planning

Background:

• Nasal airway obstruction (NAO) surgery has high failure rates, with persistent symptoms in up to 50% of patients.
• Current virtual surgery planning methods are labor-intensive, limiting widespread adoption.
• Improving surgical outcomes for NAO requires efficient and accurate planning tools.

Purpose of the Study:

• To introduce an automatic atlas-based approach for virtual septoplasty.
• To automate the process of virtual surgical planning for nasal airway obstruction.
• To overcome the limitations of manual virtual surgery planning methods.

Main Methods:

• Developed an automatic atlas-based virtual septoplasty method using a multi-stage registration approach.
• Constructed a healthy nasal geometry atlas.
• Applied the method to a cohort of 26 NAO patients and 47 healthy subjects.

Main Results:

• The automated virtual septoplasty successfully straightened the septum in 18 out of 26 NAO patients (69%).
• Nasal airway symmetry improved, with the cross-sectional area ratio decreasing from 1.47 to 1.16 in the deviation region.
• Demonstrated increased symmetry in nasal airways after virtual septoplasty.

Conclusions:

• The automated virtual septoplasty technique can significantly reduce the effort for computational fluid dynamics (CFD) analysis in NAO surgical planning.
• This automated approach holds potential for improving surgical planning and outcomes in NAO patients.
• Further research is needed to correlate virtual surgery planning predictions with subjective patient outcomes post-septoplasty.