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http://bura.brunel.ac.uk/handle/2438/30817
Title: | Cascaded Frequency-Encoded Multi-Scale Neural Fields for Sparse-View CT Reconstruction |
Authors: | Wu, J Lin, J Pang, Y Jiang, X Li, X Meng, H Luo, Y Yang, L Li, Z |
Keywords: | computed tomography;image reconstruction;iterative unfolding network;neural fields representation;sparse-view |
Issue Date: | 31-Jan-2025 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Wu, J. et al. (2025) 'Cascaded Frequency-Encoded Multi-Scale Neural Fields for Sparse-View CT Reconstruction', IEEE Transactions on Computational Imaging, 0 (early access), pp. 1 - 14. doi: 10.1109/tci.2025.3536078. |
Abstract: | Sparse-view computed tomography aims to reduce radiation exposure but often suffers from degraded image quality due to insufficient projection data. Traditional methods struggle to balance data fidelity and detail preservation, particularly in high-frequency regions. In this paper, we propose a Cascaded Frequency-Encoded Multi-Scale Neural Field (Ca-FMNF) framework. We reformulate the reconstruction task as refining high-frequency residuals upon a high-quality low-frequency foundation. It integrates a pre-trained iterative unfolding network for initial low-frequency estimation with a FMNF to represent high-frequency residuals. The FMNF parameters are optimized by minimizing the discrepancy between the measured projections and those estimated through the imaging forward model, thereby refining the residuals based on the initial estimation. This dual-stage strategy enhances data consistency and preserves fine structures. The extensive experiments on simulated and clinical datasets demonstrate that our method achieves the optimal results in both quantitative metrics and visual quality, effectively reducing artifacts and preserving structural details. |
URI: | https://bura.brunel.ac.uk/handle/2438/30817 |
DOI: | https://doi.org/10.1109/tci.2025.3536078 |
ISSN: | 2573-0436 |
Other Identifiers: | ORCiD: Jia Wu https://orcid.org/0009-0007-6710-4137 ORCiD: Jinzhao Lin https://orcid.org/0000-0001-8165-9007 ORCiD: Yu Pang https://orcid.org/0000-0002-7507-5387 ORCiD: Xiaoming Jiang https://orcid.org/0000-0002-8184-1578 ORCiD: Xinwei Li https://orcid.org/0000-0003-0713-9366 ORCiD: Hongying Meng https://orcid.org/0000-0002-8836-1382 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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