Displacement retrieval for speckle-based X-ray phase-contrast imaging using a convolutional neural network

Abstract

Speckle-based phase-contrast imaging offers enhanced sensitivity towards weakly-attenuating materials and a simple and cheap setup, but requires accurate tracking of sample-induced speckle pattern modulations. We implemented a convolution neural network for speckle tracking in x-ray phase contrast imaging. The model was trained on simulated speckle patterns generated from a wave-optics simulation and then compared against conventional algorithms. Our solution showed comparable bias, substantially improved root mean squared error and spatial resolution, and the shortest computational time. Thus, our approach enhances the performance of speckle-based phase-contrast imaging.