HS-QFNet: Deep learning-enhanced hyperspectral fluorescence correction for accurate in vivo photosensitizer concentration quantification

Accurate quantification of photosensitizer concentration is essential for effective fluorescence-guided surgery and personalized photodynamic therapy, but it is hindered by tissue-induced fluorescence distortions because existing correction methods have limited accuracy and clinical adaptability. We present a deep-learning-based fluorescence correction algorithm (Hyperspectral Quantitative Fluorescence Network [HS-QFNet]) that integrates hyperspectral fluorescence and diffuse reflectance image features from a phantom array with broad optical properties, combined with an attention mechanism to model nonlinear relationships between signal distortion and tissue optical properties, enabling precise detection of photosensitizer spatial distribution. Validated in phantoms, it achieved a mean absolute error (MAE) of 0.21 μM—a 68% improvement over traditional methods (0.65 μM MAE). In mouse tumor models, it maintained an MAE of 0.31 μM with a 0.957 correlation to true concentration. This advancement in quantitative fluorescence imaging holds significant value for tumor margin delineation and personalized therapy in precision oncology.