Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

doi:10.1109/TMI.2021.3066318

	Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer
	Zhou, Bo 1; Zhou, S. Kevin 2; Duncan, James S.1,3; Liu, Chi 1,3
	2021-07-01
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
卷号	40 期号:7 页码:1792-1804
摘要	Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of projection views arising from sparse view or limited angle acquisitions that reduce radiation dose or shorten scanning time. However, such a reconstruction suffers from severe artifacts due to the incompleteness of sinogram. To derive quality reconstruction, previous methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction. In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. We evaluate our methods on two datasets. Our experimental results on AAPM Low Dose CT Grand Challenge datasets demonstrate that our algorithm achieves a consistent and substantial improvement over the existing neural network methods on both limited angle reconstruction and sparse view reconstruction. In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types.
关键词	Tomographic reconstruction cascaded network projection data fidelity layer RedSCAN limited angle sparse view
DOI	10.1109/TMI.2021.3066318
收录类别	SCI
语种	英语
资助项目	National Institutes of Health (NIH)[R01EB025468] ; Biomedical Engineering Ph.D. fellowship from Yale University
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000668842500005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：37[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17519
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Bo; Liu, Chi
作者单位	1.Yale Univ, Dept Biomed Engn, New Haven, CT 06511 USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT 06511 USA
推荐引用方式 GB/T 7714	Zhou, Bo,Zhou, S. Kevin,Duncan, James S.,et al. Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2021,40(7):1792-1804.
APA	Zhou, Bo,Zhou, S. Kevin,Duncan, James S.,&Liu, Chi.(2021).Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer.IEEE TRANSACTIONS ON MEDICAL IMAGING,40(7),1792-1804.
MLA	Zhou, Bo,et al."Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer".IEEE TRANSACTIONS ON MEDICAL IMAGING 40.7(2021):1792-1804.