Online mobile C-arm calibration using inertial sensors: a preliminary study in order to achieve CBCT.

PURPOSE: Cone beam computed tomography (CBCT) became increasingly popular over the last years. It allows more accurate diagnosis and treatment planning with a lower effective radiation dose. However, volume reconstruction algorithms require a very precise knowledge of the imaging geometry. Due to mechanical instabilities, mobile C-arms are incompatible with existing tomography algorithms. Therefore, C-arm online calibration is essential in order to achieve an accurate volume reconstruction. METHODS: We present an online calibration method for mobile C-arms. It is based on tracking the detector and the X-ray source of the C-arm using three-axis gyroscopes and accelerometers. It aims to be precise and noninvasive. The performance of the calibration algorithm is evaluated in regard to the precision of the sensors and to whether or not dynamic models are considered. In addition, we present an algorithm which propagate the errors from the positions and orientations estimates to the 2D projections on the detector plane. Thus, we can evaluate the impact of the estimation errors on the acquired images. RESULTS: The experiments are conducted on an experimental C-arm. The reached accuracy is [Formula: see text] for orientation and [Formula: see text] for position. These errors propagate as an error of [Formula: see text] for the 2D projections on the detector plane. CONCLUSIONS: The proposed calibration algorithm achieves an accuracy comparable to the precision of existing calibration methods. The required angle accuracy by CBCT algorithms is reached. However, improvements are needed to achieve the required position precision. The in-plane translations of the X-ray source and the detector are the most crucial parameters to estimate in order to conduct CBCT on mobile C-arms.

Joint NDT image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation.

In this paper, we propose a method to simultaneously restore and to segment piecewise homogeneous images degraded by a known point spread function (PSF) and additive noise. For this purpose, we propose a family of nonhomogeneous Gauss-Markov fields with Potts region labels model for images to be used in a Bayesian estimation framework. The joint posterior law of all the unknowns (the unknown image, its segmentation (hidden variable) and all the hyperparameters) is approximated by a separable probability law via the variational Bayes technique. This approximation gives the possibility to obtain practically implemented joint restoration and segmentation algorithm. We will present some preliminary results and comparison with a MCMC Gibbs sampling based algorithm. We may note that the prior models proposed in this work are particularly appropriate for the images of the scenes or objects that are composed of a finite set of homogeneous materials. This is the case of many images obtained in nondestructive testing (NDT) applications.