The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer
Abstract
Convolutional neural networks (CNNs) have been extremely successful in various medical imaging tasks. However, because the size of the convolutional kernel used in a CNN is much smaller than the image size, CNN has a strong spatial inductive bias and lacks a global understanding of the input images. Vision Transformer, a recently emerged network structure in computer vision, can potentially overcome the limitations of CNNs for image-reconstruction tasks. In this work, we proposed a slice-by-slice Transformer network (SSTrans-3D) to reconstruct cardiac SPECT images from 3D few-angle data. To be specific, the network reconstructs the whole 3D volume using a slice-by-slice scheme. By doing so, SSTrans-3D alleviates the memory burden required by 3D reconstructions using Transformer. The network can still obtain a global understanding of the image volume with the Transformer attention blocks. Lastly, already reconstructed slices are used as the input to the network so that SSTrans-3D can potentially obtain more informative features from these slices. Validated on porcine, phantom, and human studies acquired using a GE dedicated cardiac SPECT scanner, the proposed method produced images with clearer heart cavity, higher cardiac defect contrast, and more accurate quantitative measurements on the testing data as compared with a deep U-net.
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Self-citation
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Metadata
- Authors
- Xie, H., Thorn, S., Liu, Y.-H., Lee, S., Liu, Z., Wang, G., Sinusas, A. J., & Liu, C.
- Venue
- IEEE Transactions on Radiation and Plasma Medical Sciences
- Published
- 2023-01
- Volume
- 7
- Issue
- 1
- Pages
- 33-40
- ISSN
- 2469-7311
- 2469-7303
- Cited by
- 16
- References
- 20
- Licence
- Funders
- NIH (R01HL154345, S10RR025555)
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