Showing papers by "Jean-Baptiste Thibault published in 2014"

Model-Based Iterative Reconstruction for Dual-Energy X-Ray CT Using a Joint Quadratic Likelihood Model

Abstract: Dual-energy X-ray CT (DECT) has the potential to improve contrast and reduce artifacts as compared to traditional CT. Moreover, by applying model-based iterative reconstruction (MBIR) to dual-energy data, one might also expect to reduce noise and improve resolution. However, the direct implementation of dual-energy MBIR requires the use of a nonlinear forward model, which increases both complexity and computation. Alternatively, simplified forward models have been used which treat the material-decomposed channels separately, but these approaches do not fully account for the statistical dependencies in the channels. In this paper, we present a method for joint dual-energy MBIR (JDE-MBIR), which simplifies the forward model while still accounting for the complete statistical dependency in the material-decomposed sinogram components. The JDE-MBIR approach works by using a quadratic approximation to the polychromatic log-likelihood and a simple but exact nonnegativity constraint in the image domain. We demonstrate that our method is particularly effective when the DECT system uses fast kVp switching, since in this case the model accounts for the inaccuracy of interpolated sinogram entries. Both phantom and clinical results show that the proposed model produces images that compare favorably in quality to previous decomposition-based methods, including FBP and other statistical iterative approaches.

Statistical X-ray Computed Tomography Imaging from Photon-starved Measurements

Abstract: Dose reduction in clinical X-ray computed tomography (CT) causes low signal-to-noise ratio (SNR) in photonsparse situations. Statistical iterative reconstruction algorithms have the advantage of retaining image quality while reducing input dosage, but they meet their limits of practicality when significant portions of the sinogram near photon starvation. The corruption of electronic noise leads to measured photon counts taking on negative values, posing a problem for the log() operation in preprocessing of data. In this paper, we propose two categories of projection correction methods: an adaptive denoising filter and Bayesian inference. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. Bayesian inference is a point-wise estimation based on measurements and prior information. Both approaches help improve diagnostic image quality at dramatically reduced dosage.

Analysis of statistical models for iterative reconstruction of extremely low-dose CT data

Abstract: In order to reduce CT radiation dose, there have been numerous efforts to develop low-dose acquisition protocols as well as noise reduction methods such as data denoising and iterative reconstruction. In this study, we analyze the first and second order statistics of post-log CT data and the resulting impact on iterative image reconstruction for extremely low-dose CT acquisitions. We performed a CT simulation incorporating polychromatic forward projection and realistic levels of quantum and electronic noise. We performed N=1000 simulations of a chest phantom to analyze the impact of processing steps on the statistics of post-log data. We investigated the impact of two non-positivity correction methods, threshold and mean-preserving filter. And, we analyzed the bias and variance of different weighting terms and performed weighted least squares reconstruction with these different weights. For the simulation of an extremely low dose chest acquisition with 80 kVp and 0.5 mAs, the mean-preserving filter reduced the mean bias of post-log sinogram by roughly seven times compared to the threshold method. The WLS reconstructed images using simple weighting terms that ignored the effect of non-positive correction lead to limited improvements in image quality. Accurate weighting terms including electronic noise and the variance change from MPF provided superior images, especially in highly attenuating regions where bias reductions of ∼17% were achieved compared to simple weighting matrices. Appropriate selection of the non-positivity correction method is essential for low flux CT data processing. The proposed method for estimating the weighting matrix with electronic noise and the effect of pre-corrections leads to some improvements in variance estimation for post-log CT data, although it has potential for further improvement.

Methods and systems for correcting table deflection

Abstract: Various methods and systems are provided for estimating and compensating for table deflection in reconstructed images. In one embodiment, a method for computed tomography (CT) imaging comprises reconstructing images from data acquired during a helical CT scan where table deflection parameters are estimated and the reconstruction is adjusted based on the table deflection parameters. In this way, images may be reconstructed without artifacts caused by table deflection.

Systems and methods for guided de-noising for computed tomography

Abstract: A method includes obtaining spectral computed tomography (CT) information via an acquisition unit having an X-ray source and a CT detector. The method also includes, generating, with one or more processing units, using at least one image transform, a first basis image and a second basis image using the spectral CT information. Further, the method includes performing, with the one or more processing units, guided processing on the second basis image using the first basis image as a guide to provide a modified second basis image. Also, the method includes performing at least one inverse image transform using the first basis image and the modified second basis image to generate at least one modified image.