A Novel Single-View Cerenkov Luminescence Tomography Method Based On Fuzzy C-Means Clustering

By detecting Cerenkov luminescence (CL) signal from the surface of biological tissue, Cerenkov luminescence tomography (CLT) can reconstruct the three-dimensional distribution of radionuclide probes in biological tissues. However, the extremely weak intensity of CL signal, as well as the complexity of optical transmitting in biological tissues, reduces the accuracy of CLT result. In order to obtain better CLT reconstruction results, we propose a novel single-view CLT reconstruction method based on fuzzy C-means clustering and iteratively shrinking permissible strategy. A series of numerical simulations and physical phantom experiments are designed to evaluate the performance of the proposed method. The results demonstrate that the proposed method can improve the reconstruction accuracy efficiently, with good stability and the ability of resolving dual CL source targets.