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Article Dans Une Revue IEEE Transactions on Instrumentation and Measurement Année : 2024

Cas-PRED: A limited-angle cone beam CT reconstruction research based on prior information

Liu Zhang
Wenxue Yu
Shijie Wang
Pinzheng Zhang
Cheng Xue
Chunfeng Yang
Shipeng Xie
Yang Chen

Résumé

Due to limitations in radiation dose or scanning conditions, there are instances where CT data can only be collected within a restricted scanning angle. However, the use of limited-angle projection data often results in reconstructed images that exhibit significant noise and artifacts, which can significantly impact diagnostic accuracy. Conventional iterative reconstruction algorithms, such as the total variation algorithm, consume considerable time simulating the cone beam forward and backward projection processes. Furthermore, these algorithms often struggle to address the challenges of limited-angle reconstruction due to their inherent limitations. To tackle these challenges and achieve superior reconstruction outcomes, we introduce a novel approach known as the Cascaded Prior-Based Residual Encoder-Decoder Network (Cas-PRED). This method employs a multi-level cascaded network as its core architecture, leveraging full-angle CT reconstruction results as prior information to guide the deep learning network’s training process. Our approach consistently delivers superior results compared to existing reconstruction methods, with impressive performance metrics, including a peak signal-to-noise ratio (PSNR) of 33.69±0.90 dB, a structural similarity index (SSIM) of 0.9557±0.0029, and a root mean squared error (RMSE) of 22.73±1.56 for the result of [0, 150°] reconstruction, highlighting its effectiveness in enhancing image quality and accuracy.
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Licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-04467402 , version 1 (25-04-2024)

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Paternité - Pas d'utilisation commerciale

Identifiants

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Yuan Gao, Liu Zhang, Wenxue Yu, Shijie Wang, Pinzheng Zhang, et al.. Cas-PRED: A limited-angle cone beam CT reconstruction research based on prior information. IEEE Transactions on Instrumentation and Measurement, 2024, 73, pp.1-16. ⟨10.1109/TIM.2023.3335513⟩. ⟨hal-04467402⟩
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