International audienceThis paper presents an image reconstruction method for X-ray tomography from limited range projections. It makes use of the discrete Radon transform and a set of discrete orthogonal Tchebichef polynomials to define the projection moments and the image moments. By establishing the relationship between these two sets of moments, we show how to estimate the unknown projections from known projections in order to improve the image reconstruction. Simulation results are provided in order to validate the method and to compare its performance with some existing algorithms

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Locating anatomical landmarks in a cephalometric X-ray image is a crucial step in cephalometric analysis. Manual landmark localization suffers from inter-and intra-observer variability, which makes developing automated localization methods urgent in clinics. Most of the existing techniques follow the routine thoughts which estimate numerical values of displacements or coordinates for the target landmarks. Additionally, there are no reported applications of generative adversarial networks (GAN) in cephalometric landmark localization. Motivated by these facts, we propose a new automated cephalometric landmark localization method under the framework of GAN. The principle behind our approach is fundamentally different from the conventional ones. It trained an adversarial network under the framework of GAN to learn the mapping from features to the distance map of a specific target landmark. Namely, the output of the adversarial network in this paper is image data, instead of displacements or coordinates as the conventional approaches. Based on the trained networks, we can predict the distance maps of all target landmarks in a new cephalometric image. Subsequently, the target landmarks are detected from the predicted distance maps by an approach similar to regression voting. Experimental results validate the good performance of our method in localization of cephalometric landmarks in dental X-ray images.INDEX TERMS Adversarial encoder-decoder networks, localization of anatomical landmarks, cephalometric analysis, prediction of distance maps.

Limiting scan views is an efficient way to reduce radiation doses in the cone-beam computed tomography (CBCT) examinations, which unfortunately degrades the reconstructed images. Some methods on the framework of the generative adversarial network (GAN) were developed to improve low-dose CT images after CT reconstruction from the limited-view projections. However, no GAN-based methods were devoted to restoring missing CBCT projections in the sinogram domain before CT reconstruction. To avoid the trade-off between radiation dose and image quality, we propose a limited-view CBCT reconstruction method in the sinogram domain, instead of the image domain. First, this method slices the 3D CBCT projections into multiple 2D pieces. Then, an adversarial autoencoder network is trained to estimate the missing parts of these 2D pieces. To improve the prediction, we apply a joint loss function, including reconstruction loss and adversarial loss to the network. When the new limited-view 3D CBCT projections are acquired, the proposed method uses the trained adversarial autoencoder network to generate the missing parts of the 2D pieces sliced from the current 3D CBCT projections. Then, stacking the completed 2D pieces in order yields full-view 3D CBCT projections. Finally, we reconstruct the CT images from the full-view 3D CBCT projections by using the Feldkamp, Davis, and Kress algorithm. The experiments validate that our method performs well in the prediction of unknown projections and CT reconstruction and are less vulnerable to the number of unknown projections than other methods.INDEX TERMS Limited-view CBCT reconstruction, adversarial autoencoder network, reduction of radiation doses, prediction of missing CBCT projections.