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.
The presence of metal often heavily degrades the computed tomography (CT) image quality and inevitably affects the subsequent clinical diagnosis and therapy. With the rapid development of deep learning (DL), a lot of DL-based methods have been proposed for metal artifact reduction (MAR) task in CT imaging, including image domain, projection domain and dual-domain based MAR methods. Recently, view-by-view backprojection tensor (VVBP-Tensor) domain is developed as the intermediary domain between image domain and projection domain, while VVBP-Tensor also has many good mathematical properties, such as low-rank property and structural self-similarity. Therefore, we present a VVBP-Tensor based deep neural network (DNN) framework for better MAR performance in CT imaging. Specifically, the original projection is separately pre-processed by the linear interpolation completion algorithm and the clipping algorithm, to quickly remove most metal artifacts and preserve structural information. Then, the clipped projection is restored by one sinogram recovery network to smooth the projection values in and out of the metal trajectory. In addition, two pre-processed projections are separately transferred to two tensors by filtering, backprojecting and sorting, and two sorted tensors are simultaneously rolled into the MAR reconstruction network for further improving reconstructed CT image quality. The proposed method has a good interpretability since the MAR reconstruction network can be considered as a weighted CT image reconstruction process with learnable adaptive weights along the direction of scan views. The superior MAR performance of the presented method is demonstrated on the simulated dataset in terms of qualitative and quantitative measurements.
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