Physics-informed sinogram completion for metal artifact reduction in CT imaging.

Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

[An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction].

OBJECTIVE: To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. METHODS: A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. RESULTS: For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). CONCLUSION: The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.

An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction / 南方医科大学学报

OBJECTIVE@#To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction.@*METHODS@#A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods.@*RESULTS@#For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001).@*CONCLUSION@#The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.