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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Deep Learning-Based Image Denoising for Enhanced CT Image Reconstruction
Author(s) Parisa Asadi, Zeyu Zhou, Andriy Andreyev, Matthew Andrew
On-Site Speaker (Planned) Andriy Andreyev
Abstract Scope ges arise from inherent image noise. This study evaluates DeepRecon Pro, a deep learning-based noise-to-noise image reconstruction method, using a digital twin of the Shepp-Logan phantom to simulate varied acquisition conditions. The Deep-Recon model processes projection images or reconstructed volumes to yield enhanced projections or reconstructions, respectively. Employing cone-beam geometry with simulated Poisson noise levels and reduced projections, Deep-Recon Pro utilizes a U-Net architecture to suppress noise and improve image fidelity compared to methods like FDK and non-local means (NLM). The latest version integrates synthetic priors and a two-stage training process, effectively introducing a matching noise model during training. Quantitative metrics, including mean square error (MSE) and structural similarity index (SSIM), underscore its superior performance. Our results demonstrate that Deep-Recon significantly enhances CT image quality, offering faster, more accurate reconstructions crucial for industrial and scientific applications demanding high precision and efficiency.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization, Modeling and Simulation

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