The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT
Open accessAbstract
X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.
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“Thus, the U-net architecture is good for the sparse view CT reconstruction to deal with the globally distributed streaking artifacts [16], [17].”
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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Žbontar, J., Knöll, F., Sriram, A., Murrell, T., Huang, Z., Muckley, M. J., Defazio, A., Stern, R., Johnson, P., Bruno, M. T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08839 · Cited by 528
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
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Zhu, X. X., Montazeri, S., Ali, M., Hua, Y., Wang, Y., Mou, L., Shi, Y., Xu, F., & Bamler, R. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3046356 · Cited by 357
Metadata
- Authors
- Han, Y., & Ye, J. C.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-06
- Volume
- 37
- Issue
- 6
- Pages
- 1418-1429
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2963392702
- Cited by
- 588
- References
- 7
- FWCI
- 40.95
- Type
- journal article
- Language
- en
- Open access
- green
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