Cerenkov luminescence imaging (CLI) is a novel optical imaging method and has been proved to be a potential substitute of the traditional radionuclide imaging such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). This imaging method inherits the high sensitivity of nuclear medicine and low cost of optical molecular imaging. To obtain the depth information of the radioactive isotope, Cerenkov luminescence tomography (CLT) is established and the 3D distribution of the isotope is reconstructed. However, because of the strong absorption and scatter, the reconstruction of the CLT sources is always converted to an ill-posed linear system which is hard to be solved. In this work, the sparse nature of the light source was taken into account and the preconditioning orthogonal matching pursuit (POMP) method was established to effectively reduce the ill-posedness and obtain better reconstruction accuracy. To prove the accuracy and speed of this algorithm, a heterogeneous numerical phantom experiment and an in vivo mouse experiment were conducted. Both the simulation result and the mouse experiment showed that our reconstruction method can provide more accurate reconstruction result compared with the traditional Tikhonov regularization method and the ordinary orthogonal matching pursuit (OMP) method. Our reconstruction method will provide technical support for the biological application for Cerenkov luminescence.
Solution with adjustable sparsity to tomographic imaging of Cerenkov photons is presented in this work. The
sparsity of radionuclides' distribution in tissues is an objective but unknown fact, and the inverse model of
qualitative data is an ill-posed problem. Based on the optimization technique, the uniqueness of numerical
solution to the ill-conditioned compact operator can be guaranteed by use of sparse regularization with the
approximate message-passing (AMP) method. After absorbing formulations with the AMP, we analyzed the
behavior of the hard thresholding operator. Iteratively numerical solutions were used to approximate the real
light source by assuming the number of non-zero solution in manual mode. This modified AMP algorithm was
performed in numerical simulation and physical experiments with 2-[18F]fluoro-2-deoxy-D-glucose. Experimental
results indicated that the proposed method was a kind of low-complexity iterative thresholding algorithms for
reconstructing 3D sparse distribution from a small set of optical measurements.