Simulating Sinogram-Domain Motion and Correcting Image-Domain Artifacts Using Deep Learning in HR-pQCT Bone Imaging

• URL: http://arxiv.org/abs/2509.10961v1
• Date: Sat, 13 Sep 2025 19:43:05 GMT
• Title: Simulating Sinogram-Domain Motion and Correcting Image-Domain Artifacts Using Deep Learning in HR-pQCT Bone Imaging
• Authors: Farhan Sadik, Christopher L. Newman, Stuart J. Warden, Rachel K. Surowiec,
• Abstract summary: We optimize a conventional sinogram-based method to simulate motion artifacts in HR-pQCT images.<n>We propose an Edge-enhanced Self-attention Wasserstein Generative Adversarial Network with Gradient Penalty (ESWGAN-GP) to address motion artifacts in both simulated (source) and real-world (target) datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rigid-motion artifacts, such as cortical bone streaking and trabecular smearing, hinder in vivo assessment of bone microstructures in high-resolution peripheral quantitative computed tomography (HR-pQCT). Despite various motion grading techniques, no motion correction methods exist due to the lack of standardized degradation models. We optimize a conventional sinogram-based method to simulate motion artifacts in HR-pQCT images, creating paired datasets of motion-corrupted images and their corresponding ground truth, which enables seamless integration into supervised learning frameworks for motion correction. As such, we propose an Edge-enhanced Self-attention Wasserstein Generative Adversarial Network with Gradient Penalty (ESWGAN-GP) to address motion artifacts in both simulated (source) and real-world (target) datasets. The model incorporates edge-enhancing skip connections to preserve trabecular edges and self-attention mechanisms to capture long-range dependencies, facilitating motion correction. A visual geometry group (VGG)-based perceptual loss is used to reconstruct fine micro-structural features. The ESWGAN-GP achieves a mean signal-to-noise ratio (SNR) of 26.78, structural similarity index measure (SSIM) of 0.81, and visual information fidelity (VIF) of 0.76 for the source dataset, while showing improved performance on the target dataset with an SNR of 29.31, SSIM of 0.87, and VIF of 0.81. The proposed methods address a simplified representation of real-world motion that may not fully capture the complexity of in vivo motion artifacts. Nevertheless, because motion artifacts present one of the foremost challenges to more widespread adoption of this modality, these methods represent an important initial step toward implementing deep learning-based motion correction in HR-pQCT.

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