|
In-line X-ray phase contrast imaging (IL-PCI) is a promising technology for clinical diagnosis because of its great advantage in distinguishing low contrast tissues and simple structure to implement. In order to recover the phase projections from the phase contrast measurements, conventional phase retrieval methods were developed based on assumptions such as homogeneous material, weak attenuation, and thus suffered from limited generalizability, practicability and feasibility. Deep learning-based methods have been proposed for phase retrieval and great success has been achieved. While the practical physical model of phase contrast imaging hasn’t been fully considered including the non-ideal effects of finite size of the x-ray micro focal spot, finite pixel size of the detector and the system noise. In this paper, a convolutional network based on generative adversarial network is proposed to retrieve the phase projections with fully considering the non-ideal effects in IL-PCI. The network composed of a generating network from which the phase projections were retrieved and a discriminating network from which the difference between the output of generation network and the reference phase projection is processed and backpropagated to the input of the network. Phase contrast measurements of the microspheres phantom were simulated and retrieved by the conventional methods and the proposed network. Results show the superiority of the proposed network in spatial resolution and noise suppression compared with the conventional method.
|
