Single-Operator Cancer Vision Goggles for Quantitative Near-Infrared Fluorescence-Guided Oncologic Surgery.

OBJECTIVE

Near-infrared fluorescence (NIRF) imaging systems often require multiple operators and lack standardized acquisition constraints, limiting reproducibility across users and sites. We present a single-operator, wearable Cancer Vision Goggles (CVG) platform for hands-free NIRF guidance while preserving a radiometrically faithful reference stream for quantitative analysis.

METHODS

The head-mounted binocular CVG integrates synchronized visible/NIR cameras, real-time co-registration to an optical see-through display, green alignment lasers converging at a preset 50-cm distance to standardize geometry, and a posture-dependent laser safety interlock. A Bluetooth foot pedal and graphical user interface enable hands-free paired laser-on/laser-off snapshot capture. Performance was characterized using USAF targets and ICG Intralipid phantoms, and validated in vivo in a 4T1 murine tumor model using LS301-HSA and in the operating room by ex vivo imaging of head and neck cancer specimens from patients injected with Pegsitacianine.

RESULTS

At 50 cm, spatial resolution was 281 μm; the excitation field exhibited peak irradiance of 26.4 ± 2.2 mW/cm2 with 73.8 ± 3.2 mm FWMH. Phantom studies achieved signal-to-background ratio (SBR) >1 at 100 pM (raw Bayer) and 300 pM (Y-luminance), with linear behavior at low-to-moderate concentrations. Murine tumors and human specimens demonstrated consistent tumor-associated NIRF localization. Real-time dynamic thresholding enhanced tumor-background delineation and on-display reporting of fluorescence metrics for data-driven guidance.

CONCLUSION

This CVG platform offers a wearable, single-operator NIRF imaging system that combines distance-enforced acquisition, integrated safety, a hands-free workflow, and dual-spectral imaging, preserving a radiometrically linear reference for quantitative analysis.

SIGNIFICANCE: Standardized acquisition supports reproducible fluorescence imaging and analyzable translational datasets.