A Hybrid Framework for Metal Artifact Suppression in CT Imaging of Metal Lattice Structures via Radon Transform and Attention-Based Super-Resolution Reconstruction  
[PERSON]  
1 School of Materials, Northwestern Polytechnical University, Xi'an 710072, China; [EMAIL_ADDRESS] (B.W. ); [EMAIL_ADDRESS] (R.W.) [PERSON]  
2 AVIC Manufacturing Technology Institute, Beijing 100024, China; [EMAIL_ADDRESS] 22 [EMAIL_ADDRESS] 22 [EMAIL_ADDRESS]. implants in computed tomography, ref. [5] proposed a semi-automatic boundary recognition algorithm based on sinograms and a method for linear interpolation to compensate for missing projection data. By segmenting the metal portions using different thresholds, interpolating, and substituting those projected data parts impacted by metals, ref. [6] was able to achieve their goal of eliminating metal artifacts. Ref. [7] presented a frequency segmentation-based metal artifact suppression (FSMAR) technique. In order to remove metal artifacts, FSMAR combines the high frequencies of uncorrected images (all available data is used for reconstruction) with more dependable low frequencies (adjusted using the MAR method based on image restoration). These approaches frequently have drawbacks, including oversmoothing, loss of structural features, or high computing expense, notwithstanding their effectiveness in some situations. Deep learning (DL) has been used in MAR due to its effectiveness in signal processing and image analysis. Based on the data domain they handle, three primary methods may be distinguished: methods based on sinograms, methods based on images, and methods based on several domains [8]. The main goals of sinogram-based methods are to locate metal areas and restore anatomical structures with content that matches the nearby data. A self-supervised cross-domain learning framework for the metal artifact restoration (MAR) technique was presented [9]. A back-projection was obtained by improving the FBP after a neural network was trained to restore the values of metal trace regions in a provided metal-free sinogram. In order to enhance the accuracy of the finished sinogram data, ref. [10] added two sinusoidal graph feature losses of the projected image, specifically the continuity and consistency of the projected data at different angles, to the conventional U-net. Ref. [11] proposed a new deep learning network to repair the irregular metal traces in the metal damage map of the sinograms in order to reduce the metal artifacts of equidistant fan-beam CT. The most popular image-based methods solely process the CT slices, which minimizes metal artifacts. The authors of [12] proposed a novel approach to lessen metal artifacts that is based on Generative Adversarial Networks (GANs). For multimodal feature fusion representation, the imaging CT (picture) and interaction information (text) are encoded. In conjunction with the creation of interactive information constraint features, the target CT and the corrected CT are guaranteed to have consistent symptoms. Ref. [13] proposed a deeply interpretable convolutional dictionary network (DICDNet). Metal workpieces were encoded using a neural dictionary model, and a proximal gradient-based optimization technique was created. Ref. [14] presented a convolutional neural network-based semi-supervised learning technique for latent features (SLF-CNN), which extracts CT image features in an alternate iteration of the actual artificial learning stage and the artificial synthetic learning stage. Ref. [15] proposed a method based on Normalized Metal Artifact Reduction (NMAR) and used two convolutional neural networks (CNNs) to enhance its performance. A new CT MAR method based on DL was proposed [16]. For dual-domain learning (pictures and sine graphs), this approach presents a novel kind of deep neural network based on the IR formula and a convex optimization technique known as FISTA (Fast Iterative Contraction Threshold Algorithm). However, it is only possible to employ a single objective function.