Free-Structure based Efficient NIR-II FMT: A Solution for Human Hepatocellular Carcinoma Detection

Current intraoperative hepatocellular carcinoma (HCC) detection primarily relies on surgeon visual inspection, palpation, and intraoperative ultrasound, which struggle to precisely localize tumor 3D position and morphology for surgical guidance. Fluorescence molecular to mography (FMT) offers a promising solution by visualizing the 3D quantitative distribution of fluorescent biomarkers. However, existing FMT techniques remain confined to preclinical research due to challenges in dynamically acquiring anatomical structures, obtaining accurate human optical parameters, and developing robust reconstruction algorithms, hindering clinical translation. To address these challenges, we proposed a novel free-structure based efficient NIR-II FMT solution. This solution features three key innovations: 1) A Free-Structural Multimodal Fusion Imaging (FSMFI) system utilizing a 3D scanner to dynamically acquire intraoperative anatomy; 2) Calculation of accurate human HCC optical parameters for precise light transport modeling; 3) An Adaptive Gaussian Weighted Smoothing (AGWS) method incorporating an energy-intensity difference prior and a smooth-solving strategy to ensure stable and accurate source reconstruction. Simulation experiments confirmed the accuracy of the AGWS method. Porcine liver phantom experiments validated the overall efficacy of the solution. Ex vivo and in vivo human HCC experiments demonstrated its clinical efficacy and translational potential. This work achieves the first breakthrough in intraoperative FMT reconstruction by overcoming preoperative structural constraints, advancing 3D surgical navigation strategies, and accelerating FMT's clinical translation.