Sequentially reweighted TV minimization for CT metal artifact reduction

Abstract

Purpose: Metal artifact reduction has long been an important topic in x-ray CT image reconstruction. In this work, the authors propose an iterative method that sequentially minimizes a reweighted total variation (TV) of the image and produces substantially artifact-reduced reconstructions. Methods: A sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradients (IG). The authors first formulate a constrained optimization model that minimizes a weighted TV of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available projection measurements, with image non-negativity enforced. The authors then solve a sequence of weighted TV minimization problems where weights used for the next iteration are computed from the current solution. Using the complete projection data, the algorithm first reconstructs an image from which a binary metal image can be extracted. Forward projection of the binary image identifies metal traces in the projection space. The metal-free background image is then reconstructed from the metal-trace-excluded projection data by employing a different set of weights. Each minimization problem is solved using a gradient method that alternates projection-onto-convex-sets and steepest descent. A series of simulation and experimental studies are performed to evaluate the proposed approach. Results: Our study shows that the sequentially reweighted scheme, by altering a single parameter in the weighting function, flexibly controls the sparsity of the IG and reconstructs artifacts-free images in a two-stage process. It successfully produces images with significantly reduced streak artifacts, suppressed noise and well-preserved contrast and edge properties. Conclusions: The sequentially reweighed TV minimization provides a systematic approach for suppressing CT metal artifacts. The technique can also be generalized to other "missing data" problems in CT image reconstruction.

Metal About the Hip and Artifact Reduction Techniques: From Basic Concepts to Advanced Imaging

“…14). [66][67][68] When compared to conventional volume rendering postprocessing techniques, 69 cinematic rendering is a recently introduced 3D visualization method of volumetric CT and MRI data that applies a highly sophisticated lighting model, enabling the generation of photorealistic images resembling gross anatomical specimens (►Fig. 15).…”

A prior-based metal artifact reduction algorithm for x-ray CT

“…The problem has existed for many years, and various metal artifact reduction (MAR) algorithms have been proposed. They can be roughly classified into sinogram in-painting methods [1][2][3][4][5][6][7][8][9][10][11], iterative methods [12][13][14][15], and hybrid methods [16][17][18][19].…”

A prior-interpolation based metal artifact reduction algorithm in computed tomography

“…Iterative reconstruction [5][6][7][8][9][10] methods are less susceptible to metal artifacts, but these methods have other drawbacks due to their high computational cost. In sinogram correction methods, it is first to determine the metal-corrupted region in projection domain and then replace them with surrogate data via different interpolation techniques, such as linear interpolation [11], multi-resolution interpolation [12], prior-interpolation [13][14][15].…”