18039 – Computational Framework for Intraoperative Fluorescence Imaging

Fluorescence-guided imaging is often used during surgery to aid surgeons in differentiating between healthy and diseased tissues. However, current commercial fluorescence imaging systems can be subject to variabilities in fluorescence measurements caused by imaging system geometry, camera response, and tissue topography. Such limitations could lead to inaccuracies in identifying surgical margins for tumour resection.

Here, an image-guided framework has been developed to compensate for measurement uncertainties encountered during fluorescence-guided surgery. The computational algorithm leverages surgical navigation to model light propagation variations due to illumination structure, tissue topography and camera response. Image guidance is performed using an intraoperative cone-beam CT (CBCT) C-Arm and optical tracker, where the tracker-to-camera registration enables fusion of fluorescence imaging with CBCT. The fluorescence system is used with an open-field camera lens as well as two endoscopes.

First, a triangular surface mesh representation of the surgical field topography is generated with CBCT. Next, a ray-triangle intersection algorithm is applied, providing dynamic mapping of light rays between tracked fluorescence system and the surface mesh. The radiometric algorithm finally converts arbitrary camera counts to measurements of optical transport on the tissue surface. In oral cavity phantom experiments, the computational framework quantified the effects of illumination inhomogeneity and surface topography, demonstrating up to 4-fold variation in endoscopic images. Moreover, segmentations of fluorescence intensity with and without image-guided compensation resulted in miss rates of up to 6% and 28%, respectively. These results suggest a potential role for this novel technology in enabling more objective clinical decision making in surgical applications.

Potential Applications

  • Surgical oncology such as tumour imaging, lymph node mapping, vascular angiography

Contact Information

Name: Noah Schwartz

Email: Noah.Schwartz@uhnresearch.ca

Phone: 0000000000