Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation

Abstract

Projection incompleteness in x-ray computed tomography (CT) often relates to sparse sampling or detector gaps and leads to degraded reconstructions with severe streak and ring artifacts. To suppress these artifacts, this study develops a new sinogram inpainting strategy based on sinusoid-like curve decomposition and eigenvector-guided interpolation, where each missing sinogram point is considered located within a group of sinusoid-like curves and estimated from eigenvector-guided interpolation to preserve the sinogram texture continuity. The proposed approach is evaluated on real two-dimensional fan-beam CT data, for which the projection incompleteness, due to sparse sampling and symmetric detector gaps, is simulated. A Compute Unified Device Architecture (CUDA)-based parallelization is applied on the operations of sinusoid fittings and interpolations to accelerate the algorithm. A comparative study is then conducted to evaluate the proposed approach with two other inpainting methods and with a compressed sensing iterative reconstruction. Qualitative and quantitative performances demonstrate that the proposed approach can lead to efficient artifact suppression and less structure blurring.

Three-Dimensional Digital Tomosynthesis

“…A similar technique has also been proposed for noise reduction of low-dose CT data (Happonen 2007b). Another paper was published in 2012 which proposes an inpainting based on sinusiod-like curve decomposition and uses an eigenvector-guided interpolation (Li 2012). A method based on a similarity comparison within the neighborhood of locus-signals in stackgram has been proposed for alignment of tomographic data in dynamic PET (Happonen 2003, Happonen 2004, Kostopoulos 2006.…”

Artifact Reduction Based on Sinogram Interpolation for the 3D Reconstruction of Nanoparticles Using Electron Tomography

“…However, these methods are not always applicable, due to the lack of prior knowledge. An alternative method to improve the reconstruction quality is by increasing the sampling rate through sinogram interpolation . For X‐ray computed tomography, sinogram interpolation methods were already successfully applied to minimize streak and ring artifacts .…”

Computed Tomography Sinogram Inpainting With Compound Prior Modelling Both Sinogram and Image Sparsity

“…The inpainting techniques, which exploit the implicit sparsity or other priors of the sinogram, are used to recover the missing data [22][23][24][25][26][27][28][29]. These methods may be based on total variation (TV) inpainting [22], fractional-order inpainting [23], wavelet domain sparse regularization [24], curvelet inpainting [25], or curvature-based sinogram inpainting [26][27][28]. These methods can yield a better visual effect in inpainting results, but the reconstructed images may also sustain some artifacts.…”