Model and Data Dual-driven Joint Limited-Angle CT Reconstruction and Metal Artifact Reduction Method

SHI Baoshun; CHENG Shizhan; JIANG Ke; FU Zhaoran

doi:10.11999/JEIT240703

Volume 47 Issue 5

May 2025

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SHI Baoshun, CHENG Shizhan, JIANG Ke, FU Zhaoran. Model and Data Dual-driven Joint Limited-Angle CT Reconstruction and Metal Artifact Reduction Method[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1569-1581. doi: 10.11999/JEIT240703

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SHI Baoshun, CHENG Shizhan, JIANG Ke, FU Zhaoran. Model and Data Dual-driven Joint Limited-Angle CT Reconstruction and Metal Artifact Reduction Method[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1569-1581. doi: 10.11999/JEIT240703

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Model and Data Dual-driven Joint Limited-Angle CT Reconstruction and Metal Artifact Reduction Method

doi: 10.11999/JEIT240703 cstr: 32379.14.JEIT240703

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Department of Stomatology, Qinhuangdao First Hospital, Qinhuangdao 066001, China

Funds: The National Natural Science Foundation of China (62371414), The Natural Science Foundation of Hebei Province (F2023203043), The Key Laboratory of Hebei Province Funding Project (202250701010046)

Received Date: 2024-08-12
Rev Recd Date: 2025-04-14

Available Online: 2025-05-09

Publish Date: 2025-05-01

Abstract

Abstract

Objective Computed Tomography (CT) is widely used in medical diagnostics due to its non-destructive, non-contact imaging capabilities. To lower cancer risk from radiation exposure, clinical practice often limits scanning angles, referred to as Limited-Angle CT (LACT). Incomplete projection data in LACT leads to wedge-shaped artifacts in reconstructions using Filtered Back-Projection (FBP) algorithms. These artifacts worsen in the presence of metallic implants. Although LACT reconstruction without metal and full-angle CT Metal Artifact Reduction (MAR) have been extensively studied, the joint task of Limited-Angle and Metal Artifact Reduction (LAMAR) has received limited attention. This study proposes a model- and data-driven CT network that integrates a Task Selection (TS) module to apply appropriate gradient descent steps for different tasks. This enables simultaneous processing of LACT and LAMAR. The network also incorporates dual-domain information interaction during alternating iterations to reconstruct high-quality CT images. Methods First, a dual-domain reconstruction model integrating both model- and data-driven model is constructed to address the joint task of LACT reconstruction and LAMAR. The model comprises four components: an image-domain data fidelity term, a projection-domain data fidelity term, an image-domain regularization term, and a projection-domain regularization term. These terms are solved using an alternating iteration strategy. The image- and projection-domain subproblems are addressed using the proximal gradient descent algorithm, with the iterative process unrolled into a Deep Neural Network (DNN). Each stage of the deep unrolling network includes three components: a TS module, a projection-domain subnetwork, and an image-domain subnetwork. The TS module dynamically determines gradient descent step sizes for the LACT and LAMAR tasks by comparing image-domain FBP reconstruction results with predefined thresholds. The projection-domain subnetwork is shared by both tasks. Finally, the data-driven proximal network comprises the projection-domain and image-domain subnetworks. The projection-domain subnetwork includes an encoder, a dual-branch structure, and a decoder. The encoder has two stages, each consisting of a convolutional layer followed by an activation function; the decoder mirrors this architecture. A Transformer-based long-range branch incorporates non-metal trace information into a self-attention mechanism to guide correction of metal trace data using contextual information from non-metal regions. A short-range branch, composed of six residual blocks, extracts local features. The outputs of the two branches are fused using a weighted strategy before being passed to the decoder. The image-domain subnetwork is implemented as an attention-based U-Net. Channel and spatial attention mechanisms are applied before each of the four downsampling operations in the U-Net encoder. This design allows the decoder to more effectively leverage encoded information for high-quality CT image reconstruction without increasing the number of network parameters. Results and Discussions Experimental results on both LACT reconstruction and LAMAR tasks show that the proposed method outperforms existing CT reconstruction algorithms in both qualitative and quantitative evaluations. Quantitative comparisons (Table 1) indicate that the proposed method achieves higher average Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and lower Root Mean Square Error (RMSE) for both tasks across three angular ranges. Specifically, average PSNR improvements for the LAMAR and LACT tasks reach 2.78 dB, 2.88 dB, and 2.32 dB, respectively, compared with the best-performing baseline methods. Qualitative comparisons (Fig 4 and Fig 5) show that reconstructing CT images and projection data through alternating iterations, combined with dual-domain information interaction, enables the network to effectively suppress composite artifacts and improve the reconstruction of soft tissue regions and fine structural details. These results consistently exceed those of existing approaches. Visual assessment of reconstruction performance on the clinical dataset for the LAMAR task (Fig 6) further demonstrates the method’s effectiveness in reducing metal artifacts around implants. The reconstructed images exhibit clearer structural boundaries and improved tissue visibility, indicating strong generalization to previously unseen clinical data. Conclusions To address the combined task of LACT reconstruction and LAMAR, this study proposes a dual-domain, model- and data-driven reconstruction framework. The optimization problem is solved using an alternating iteration strategy and unfolded into a model-driven CT reconstruction network, with each subnetwork trained in a data-driven manner. In the projection-domain network, a TS module identifies the presence of metallic implants in the initial CT estimates, allowing a single model to simultaneously handle cases with and without metal. A trace-aware projection-domain proximal subnetwork, integrating Transformer and convolutional neural network architectures, is designed to capture both local and non-local contextual features for restoring metal-corrupted regions. In the image-domain network, a U-Net architecture enhanced with channel and spatial attention mechanisms is used to maximize spatial feature utilization and improve reconstruction quality. Experimental results on the AAPM and DeepLesion datasets confirm that the proposed method consistently outperforms existing algorithms under various limited-angle conditions and in the presence of metal artifacts. Further evaluation on the SpineWeb dataset demonstrates the network’s generalization capability across clinical scenarios.
- Limited-Angle Computed Tomography (LACT),
- Metal Artifact Reduction (MAR),
- Model and data dual-driven,
- Dual-domain network

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References(33)

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