AI Enhanced Cerenkov Luminescence Microscopy
Cerenkov Luminescence Microscopy (CLM) enables the visualization of radiotracer uptake in three-dimensional (3D) tumor spheroids but is limited by low photon statistics and spatial blurring caused by positron transport. Here, we present a Monte Carlo (MC)-guided deep learning framework for CLM image restoration. Using TOPAS/Geant4, we developed a physical model of heterogeneous 3D tumor spheroids to simulate positron transport, Cerenkov photon generation and optical propagation. A dataset of 15,360 paired degraded and idealized ground-truth (GT) images was generated from 80 simulated spheroid geometries and used to train a supervised Content-Aware Image Restoration (CARE) network. Compared with the self-supervised Noise2Void (N2V) denoising method, CARE achieved substantially lower reconstruction errors on synthetic validation data, reducing the mean squared error from 0.0492 to 0.0051. Training stability and noise robustness analyses further demonstrated high reproducibility of the restored images. The trained CARE model was subsequently applied to experimental CLM images acquired from 27 U87 tumor spheroids following 18F-FDG uptake. The restored images exhibited more localized intensity distributions and higher local contrast and signal-to-noise ratios compared with raw and N2V-processed images. These findings, aligned with the 3Rs principles of animal experimentation, demonstrate the potential of physics-based supervised restoration to mitigate both noise and positron-induced spatial broadening in CLM.