Segmentectomía toracoscópica guiada por imagen mediante compuerta estadística de un solo disparo de las motas de...

Accurate identification of target lung segments during thoracoscopic surgery is critical for successful lung cancer resections but remains challenging with conventional thoracoscopic imaging techniques. We present a real-time statistical gating method that leverages the single-shot laser speckle pattern generated by an 840 nm laser to isolate the blood-scattered speckle component in lung tissue. This enables background-free measurement of hemoglobin's absorption, thereby markedly improving the sensitivity of tissue oxygen saturation detection. By exploiting differences in speckle decorrelation time, our approach reconstructs blood-scattered intensity images from single-frame thoracoscopic captures in real-time. Clinical trials demonstrated a 2.05-fold increase in boundary slope steepness and a 220% improvement in the mean absolute derivative metric, enabling precise differentiation of the segment's boundary during the inflation-deflation procedure in lung segmentectomy. For novice surgeons, manual segmentation accuracy improved from 0.78 to 0.92 (standard segmentectomy) and from 0.82 to 0.89 (rapid segmentectomy) in terms of DICE coefficients. Compatible with standard thoracoscopic systems, our method offers real-time, high-sensitivity visualization of blood absorption dynamics, enhancing surgical precision and reducing operative time. As a computational imaging method, the proposed statistical gating method can be seamlessly integrated into existing thoracoscopic systems by adding near-infrared laser illumination and an embedded GPU core. This highlights its potential for revolutionizing lung cancer surgeries.