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17 October 2022 Residual W-shape network (ResWnet) for dual-energy cone-beam CT imaging
Xiao Jiang, Hehe Cui, Zihao Liu, Lei Zhu
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230427 (2022) https://doi.org/10.1117/12.2646505
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
Deep learning has achieved great success in many medical imaging tasks without explicit solutions. In this work, learning method was applied to dual-energy cone-beam CT imaging. We proposed a Residual W-shape Network (ResWnet). ResWnet consists of three modules: scatter correction module 𝒮, material decomposition module ℳ, decomposition denoising module 𝒟 . Both 𝒮 and 𝒟 use ResWnet architecture, and this lightweight model fuses multi-level features, achieving satisfied performance with a small number of parameters. 𝒮 acts on dual-energy attenuation projections to reduce the scatter contaminations, and 𝒟 acts on material composition projections to suppress the noise. ℳ links the modules 𝒮 and 𝒟, and is used for domain transform from attenuation projections to material projections. This process could be approximated by polynomials with pre-calibrated parameters, that is, ℳ is a known operator in proposed network with no trainable parameters. This helps to reduce model parameters and improve the performance with small training dataset. Using public head CT dataset, we simulated dual-energy cone-beam CT projections and material projections. Proposed ResWnet was trained, validated and tested on this simulated dataset, verifying its effectiveness in projection-domain scatter correction and low-noise decomposition.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Xiao Jiang, Hehe Cui, Zihao Liu, and Lei Zhu "Residual W-shape network (ResWnet) for dual-energy cone-beam CT imaging", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 1230427 (17 October 2022); https://doi.org/10.1117/12.2646505
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KEYWORDS
Bone
Computed tomography
Tissues
Performance modeling
Denoising
Dual energy imaging
X-ray computed tomography
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