Compressed Sensing based Image Reconstruction with Projection Recovery for Limited Angle Cone-Beam CT Imaging.

This paper presents a new 3D CT image reconstruction for limited angle C-arm cone-beam CT imaging system based on total-variation (TV) regularized in image domain and L1-penalty in projection domain. This is motivated by the facts that the CT images are sparse in TV setting and their projections are sinusoid-like forms, which are sparse in the discrete cosine transform (DCT) domain. Furthermore, the artifacts in image domain are directional due to limited angle views, so the anisotropic TV is employed. And the reweighted L1penalty in projection domain is adopted to enhance sparsity. Hence, this paper applied the anisotropic TV-norm and reweighted L1-norm sparse techniques to the limited angle Carm CT imaging system to enhance the image quality in both CT image and projection domains. Experimental results also show the efficiency of the proposed method.Clinical Relevance-This new CT reconstruction approach provides high quality images and projections for practicing clinicians.

NMAR3: Normalized Metal Artifact Reduction for Cone Beam Computed Tomography.

Metal Artifact Reduction (MAR) plays an important role in Computed Tomography (CT) research and application because severe artifacts degrade the image quality and diagnosis value if metal objects are present in the field of measurement. Although there are already many works for MAR, these works are for fan beam CT, not for cone beam CT, which is the trend and receiving much research attention. In this paper, we extend the Normalized Metal Artifact Reduction (NMAR) for fan beam CT to NMAR3 for cone beam CT, by replacing the linear interpolation in the NMAR with bi-linear interpolation. Experiments are carried out on 17 sets of spine phantom CT. 15 of them have reference CT as ground truth and 2 ones not. Both quantitative and qualitative results verified that NMAR3 outperforms the baseline method, i.e., bi-linear interpolation based method.

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting.

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

Alternating direction method for balanced image restoration.

This paper presents an efficient algorithm for solving a balanced regularization problem in the frame-based image restoration. The balanced regularization is usually formulated as a minimization problem, involving an l(2) data-fidelity term, an l(1) regularizer on sparsity of frame coefficients, and a penalty on distance of sparse frame coefficients to the range of the frame operator. In image restoration, the balanced regularization approach bridges the synthesis-based and analysis-based approaches, and balances the fidelity, sparsity, and smoothness of the solution. Our proposed algorithm for solving the balanced optimal problem is based on a variable splitting strategy and the classical alternating direction method. This paper shows that the proposed algorithm is fast and efficient in solving the standard image restoration with balanced regularization. More precisely, a regularized version of the Hessian matrix of the l(2) data-fidelity term is involved, and by exploiting the related fast tight Parseval frame and the special structures of the observation matrices, the regularized Hessian matrix can perform quite efficiently for the frame-based standard image restoration applications, such as circular deconvolution in image deblurring and missing samples in image inpainting. Numerical simulations illustrate the efficiency of our proposed algorithm in the frame-based image restoration with balanced regularization.