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 3; Zhou, S. Kevin 2; Duncan, James S.1,3; Liu, Chi 1,3
刊名	IEEE TRANSACTIONS ON MEDICAL IMAGING
	2021-07-01
卷号	40 期号:7 页码:1792-1804
关键词	Tomographic reconstruction cascaded network projection data fidelity layer RedSCAN limited angle sparse view
ISSN号	0278-0062
DOI	10.1109/TMI.2021.3066318
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000668842500005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17518]
专题	中国科学院计算技术研究所
通讯作者	Zhou, Bo; Liu, Chi
作者单位	1.Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT 06511 USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Yale Univ, Dept Biomed Engn, 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.