Improved neural network tomography by initial learning with coarse reconstructed image

This paper presents a stable learning method of the neural network tomography, in case of asymmetrical few view projection. The neural network collocation method (NNCM) is one of effective reconstruction tools for symmetrical few view tomography. But in cases of asymmetrical few view, the learning process of NNCM tends to be unstable and fails to reconstruct appropriate tomographic images. We solve the unstable learning problem of NNCM by introducing a coarse reconstructed image in the initial learning stage of NNCM. The numerical simulation with an assumed tomographic image shows the effectiveness of the proposed method.