Anisotropic total variation minimization approach in in-line phase-contrast tomography and its application to correction of ring artifacts

Abstract

In-line phase-contrast computed tomography (IL-PC-CT) imaging is a new physical and biochemical imaging method. IL-PC-CT has advantages compared to absorption CT when imaging soft tissues. In practical applications, ring artifacts which will reduce the image quality are commonly encountered in IL-PC-CT, and numerous correction methods exist to either pre-process the sinogram or post-process the reconstructed image. In this study, we develop an IL-PC-CT reconstruction method based on anisotropic total variation (TV) minimization. Using this method, the ring artifacts are corrected during the reconstruction process. This method is compared with two methods: a sinogram preprocessing correction technique based on wavelet-FFT filter and a reconstruction method based on isotropic TV. The correction results show that the proposed method can reduce visible ring artifacts while preserving the liver section details for real liver section synchrotron data.

Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems

“…The third approach for the ring artifacts reduction are the image-based processing methods. These methods can be further divided, based on the domain of processed data, to sinogram-based (sinogram pre-processing) and tomogram-based (CT data post-processing) methods [ 11 ]. Sinogram-based methods work directly with the sinogram data, where the ring artifacts appear as straight lines in a vertical direction and are therefore easier to detect and to process.…”

“…Therefore, a novel class of methods lying between sinogram-based and tomogram-based approaches has been recently developed. The ring artifacts reduction is addressed directly during the reconstruction process using specific forms of regularizations (e.g., [ 11 , 28 , 29 ]). Such regularizations can, however, be highly computationally demanding, which limits the practical applicability of those methods.…”

Hessian Schatten‐norm and adaptive dictionary for image recovery

“…10 One of the popular ways utilizing sparsity in image recovery is total variation (TV) minimization. [11][12][13] Although TV allows a significant reduction in the number of measurements needed for reconstruction and preserves the edges of the underlying image well, reconstructed image often suffers from undesirable artifacts and image details tend to be over-smoothed. To overcome these drawbacks, Lefkimmiatis et al 14 propose Hessian Schatten (HS)-norm regularization which favors second-order derivative and can potentially restore a wider class of images.…”

“…One of the popular ways utilizing sparsity in image recovery is total variation (TV) minimization 11‐13 . Although TV allows a significant reduction in the number of measurements needed for reconstruction and preserves the edges of the underlying image well, reconstructed image often suffers from undesirable artifacts and image details tend to be over‐smoothed.…”

A simple, pixel-wise response correction for ring artifact removal in both absorption and phase contrast X-ray computed tomography

“…A number of different methods have been proposed for ring artifact correction, most of which are based on post hoc image processing. Many of them isolate and remove rings in the reconstructed image [1][2][3][4] or in the sinogram [5][6][7][8][9][10][11][12][13][14]. Some methods characterize the flat-field images [15,16], while others shift the sample or detector during image acquisition to smear out systematic intensity fluctuations across the reconstruction volume [17][18][19].…”