Image Correction in Emission Tomography Using Deep Convolution Neural Network

Abstract

We propose a new approach using Deep Convolution Neural Network (DCNN) to correct for image degradations due to statistical noise and photon attenuation in Emission Tomography (ET). The proposed approach first reconstructs an image by the standard Filtered Backprojection (FBP) without correcting for the degradations followed by inputting the degraded image into DCNN to obtain an improved image. We consider two different scenarios. The first scenario inputs an ET image only into DCNN, whereas the second scenario inputs a pair of degraded ET image and CT/MRI image to improve accuracy of the correction. The simulation result demonstrates that both the scenarios can improve image quality compared to the FBP without correction, and, in particular, accuracy of the second scenario is comparable to that of the standard iterative reconstruction such as Maximum Likelihood Expectation Maximization (MLEM) and Ordered-Subsets EM (OSEM) methods. The proposed method is able to output an image in very short time, because it does not rely on iterative computations.