A Neural Network Approach for Image Reconstruction from a Single X-Ray Projection

Abstract

Time-resolved imaging becomes popular in radiotherapy in that it significantly reduces blurring artifacts in volumetric images reconstructed from a set of 2D X-ray projection data. We aim at developing a neural network (NN) based machine learning algorithm that allows for reconstructing an instantaneous image from a single projection. In our approach, each volumetric image is represented as a deformation of a chosen reference image, in which the deformation is modeled as a linear combination of a few basis functions through principal component analysis (PCA). Based on this PCA deformation model, we train an ensemble of neural networks to find a mapping from a projection image to PCA coefficients. For image reconstruction, we apply the learned mapping on an instantaneous projection image to obtain the PCA coefficients, thus getting a deformation. Then, a volumetric image can be reconstructed by applying the deformation on the reference image. Experimentally, we show promising results on a set of simulated data.