Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering

Abstract

High-density objects such as metal prostheses, surgical clips, or dental fillings generate streak-like artifacts in computed tomography images. We present a novel method for metal artifact reduction by in-painting missing information into the corrupted sinogram. The information is provided by a tissue-class model extracted from the distorted image. To this end the image is first adaptively filtered to reduce the noise content and to smooth out streak artifacts. Consecutively, the image is segmented into different material classes using a clustering algorithm. The corrupted and missing information in the original sinogram is completed using the forward projected information from the tissue-class model. The performance of the correction method is assessed on phantom images. Clinical images featuring a broad spectrum of metal artifacts are studied. Phantom and clinical studies show that metal artifacts, such as streaks, are significantly reduced and shadows in the image are eliminated. Furthermore, the novel approach improves detectability of organ contours. This can be of great relevance, for instance, in radiation therapy planning, where images affected by metal artifacts may lead to suboptimal treatment plans.

Single-energy metal artefact reduction with CT for carbon-ion radiation therapy treatment planning

“…19 The linearly interpolated raw data are reconstructed to generate an image with the metal objects removed (second-pass image). The second-pass image is classified by HU thresholds for air, water and bone (tissue-class modelling, 20 ) which are forward projected onto the metal's path. The tissue-classified forward projection is linearly integrated to the interpolated raw data and reconstructed again to obtain a third-pass image.…”

Metal Artifact Reduction for Ct-Based Luggage Screening

“…Despite the advances, there are no widely accepted solutions, and MAR continues to be a challenging research problem. There are three main approaches -sinogram replacement [4][5][6][7][8][9][10][11][12][13][14][15], energy decomposition with multiple scanning spectra, e.g., [16], and iterative reconstruction (IR) [17][18][19][20]. All these methods operate in Radon space (also called projections or sinograms).…”

Computed tomographic beam-hardening artefacts: mathematical characterization and analysis

“…Most of these methods are based on inpainting-based methods (e.g. interpolation [3][4][5][6][7]), normalized interpolation methods [2], Poisson inpainting [8], wavelet [9][10][11], tissue-class models [12] and total variation [13], Euler's elastica [14], iterative reconstruction methods [15][16][17][18] (e.g. iterative FBP, weighted least-square methods) and hybrid methods that combine the first two methods.…”

“…Despite various efforts to reduce metal artefacts, most existing methods for single-energy CT have been insufficiently effective to achieve widespread clinical use [2,12,19]. Dual-energy CT [20] provides satisfactory reconstruction via beam-hardening correction, but it requires a longer postprocessing time and a higher dose of radiation compared with single-energy CT [21].…”