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.

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 L-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.

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.

Frame-based compressive sensing MR image reconstruction with balanced regularization.

This paper addresses the frame-based MR image reconstruction from undersampled k-space measurements by using a balanced ℓ(1)-regularized approach. Analysis-based and synthesis-based approaches are two common methods in ℓ(1)-regularized image restoration. They are equivalent under the orthogonal transform, but there exists a gap between them under redundant transform such as frame.

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.