DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction

Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections...

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Vydané v:	IEEE transactions on medical imaging Ročník 40; číslo 11; s. 3002 - 3014
Hlavní autori:	Wu, Weiwen, Hu, Dianlin, Niu, Chuang, Yu, Hengyong, Vardhanabhuti, Varut, Wang, Ge
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Biomedical measurement compressed sensing Computed tomography Computed tomography (CT) Deep learning Domains Drones Embedding Generative adversarial networks Image processing Image Processing, Computer-Assisted Image quality Image reconstruction Imaging iterative reconstruction Magnetic resonance imaging Medical imaging Modules Optimization Phantoms, Imaging Positron emission Reconstruction algorithms Single photon emission computed tomography sparse-view CT reconstruction Tomography Tomography, X-Ray Computed
ISSN:	0278-0062, 1558-254X, 1558-254X
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Abstract	Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.
AbstractList	Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from ultra-sparse projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed by the image domain networks. After that, the refinement module focuses on the recovery of image details in the residual data and image domains synergistically. Finally, the results from embedding and refinement components in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy. Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy. Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.
Author	Vardhanabhuti, Varut Wang, Ge Wu, Weiwen Hu, Dianlin Niu, Chuang Yu, Hengyong
Author_xml	– sequence: 1 givenname: Weiwen orcidid: 0000-0002-8295-5104 surname: Wu fullname: Wu, Weiwen email: weiwenwu12@gmail.com organization: Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong – sequence: 2 givenname: Dianlin orcidid: 0000-0003-4857-9878 surname: Hu fullname: Hu, Dianlin email: 220171616@seu.edu.cn organization: Laboratory of Image Science and Technology, Southeast University, Nanjing, China – sequence: 3 givenname: Chuang orcidid: 0000-0002-3310-7803 surname: Niu fullname: Niu, Chuang email: niuc@rpi.edu organization: Department of Biomedical Engineering, Biomedical Imaging Center, School of Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA – sequence: 4 givenname: Hengyong orcidid: 0000-0002-5852-0813 surname: Yu fullname: Yu, Hengyong email: hengyong-yu@ieee.org organization: Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA – sequence: 5 givenname: Varut orcidid: 0000-0001-6677-3194 surname: Vardhanabhuti fullname: Vardhanabhuti, Varut email: varv@hku.hk organization: Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong – sequence: 6 givenname: Ge orcidid: 0000-0002-2656-7705 surname: Wang fullname: Wang, Ge email: wangg6@rpi.edu organization: Department of Biomedical Engineering, Biomedical Imaging Center, School of Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
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Cites_doi	10.1137/1.9780898719284 10.1109/TMI.2017.2785879 10.1073/pnas.1907377117 10.1097/RCT.0b013e318231c578 10.1038/s42256-019-0057-9 10.1109/TMI.2012.2195669 10.1109/TMI.2020.3033541 10.1002/mp.12344 10.1109/TMI.2018.2823338 10.1109/TMI.2017.2715284 10.1109/TMI.2020.2964266 10.1088/0031-9155/59/4/1005 10.1088/0031-9155/59/12/3121 10.1023/B:JMIV.0000011321.19549.88 10.1109/TPAMI.2020.3012955 10.7717/peerj.453 10.1088/0031-9155/51/13/R08 10.1109/TRPMS.2020.3011413 10.1364/OE.25.024215 10.1038/s42256-020-00273-z 10.1109/TMI.2018.2805692 10.1117/12.2549444 10.1088/0031-9155/54/9/014 10.1109/TMI.2014.2380993 10.3233/XST-16208 10.1137/080724265 10.1109/TIP.2017.2713099 10.1109/IVMSPW.2018.8448694 10.1118/1.2370508 10.1038/nature25988 10.1109/ACCESS.2016.2624938 10.1109/TMI.2018.2827462 10.1109/TRPMS.2020.2991887 10.1088/0031-9155/60/8/N131 10.1016/j.apm.2018.07.006 10.1109/TMI.2018.2799231 10.1109/TMI.2018.2878226 10.1109/TMI.2018.2823768
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References	ref12 ref37 ref36 ref14 ref31 ref33 ref11 antun (ref13) 2020; 117 ref32 ref10 xia (ref15) 2020 ref2 ref39 ref17 ref38 ref19 hochmuth (ref1) 2002; 23 ref18 sun (ref16) 2016 wu (ref30) 2021; 70 wu (ref47) 2020 johnson (ref34) 2016 kak (ref22) 1988 gottschling (ref27) 2020 ref24 ref45 ref23 ref48 ref26 ref25 ref20 ref42 gulrajani (ref35) 2017 ref44 ref21 ref43 ref28 ref29 ref8 ref7 kingma (ref41) 2014 ref9 ref4 ref3 ref6 ref5 xu (ref46) 2012; 31 ref40
References_xml	– ident: ref21 doi: 10.1137/1.9780898719284 – ident: ref37 doi: 10.1109/TMI.2017.2785879 – volume: 117 start-page: 30088 year: 2020 ident: ref13 article-title: On instabilities of deep learning in image reconstruction and the potential costs of AI publication-title: Proc Nat Acad Sci USA doi: 10.1073/pnas.1907377117 – ident: ref43 doi: 10.1097/RCT.0b013e318231c578 – year: 2020 ident: ref15 article-title: MAGIC: Manifold and graph integrative convolutional network for low-dose CT reconstruction publication-title: arXiv 2008 00406 – ident: ref42 doi: 10.1038/s42256-019-0057-9 – start-page: 694 year: 2016 ident: ref34 article-title: Perceptual losses for real-time style transfer and super-resolution publication-title: Proc Eur Conf Comput Vis – volume: 31 start-page: 1682 year: 2012 ident: ref46 article-title: Low-dose X-ray CT reconstruction via dictionary learning publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2012.2195669 – ident: ref20 doi: 10.1109/TMI.2020.3033541 – ident: ref10 doi: 10.1002/mp.12344 – year: 2020 ident: ref27 article-title: The troublesome kernel: Why deep learning for inverse problems is typically unstable publication-title: arXiv 2001 01258 – ident: ref6 doi: 10.1109/TMI.2018.2823338 – ident: ref5 doi: 10.1109/TMI.2017.2715284 – year: 2014 ident: ref41 article-title: Adam: A method for stochastic optimization publication-title: arXiv 1412 6980 – ident: ref12 doi: 10.1109/TMI.2020.2964266 – ident: ref4 doi: 10.1088/0031-9155/59/4/1005 – ident: ref3 doi: 10.1088/0031-9155/59/12/3121 – ident: ref38 doi: 10.1023/B:JMIV.0000011321.19549.88 – ident: ref18 doi: 10.1109/TPAMI.2020.3012955 – volume: 70 start-page: 1 year: 2021 ident: ref30 article-title: A high-quality photon-counting CT technique based on weight adaptive total-variation and image-spectral tensor factorization for small animals imaging publication-title: IEEE Trans Instrum Meas – ident: ref39 doi: 10.7717/peerj.453 – ident: ref48 doi: 10.1088/0031-9155/51/13/R08 – ident: ref33 doi: 10.1109/TRPMS.2020.3011413 – ident: ref28 doi: 10.1364/OE.25.024215 – ident: ref7 doi: 10.1038/s42256-020-00273-z – ident: ref14 doi: 10.1109/TMI.2018.2805692 – ident: ref40 doi: 10.1117/12.2549444 – year: 2020 ident: ref47 article-title: Stabilizing deep tomographic reconstruction networks publication-title: arXiv 2008 01846 – ident: ref29 doi: 10.1088/0031-9155/54/9/014 – ident: ref31 doi: 10.1109/TMI.2014.2380993 – ident: ref26 doi: 10.3233/XST-16208 – ident: ref45 doi: 10.1137/080724265 – year: 1988 ident: ref22 publication-title: Principles of Computerized Tomographic Imaging – ident: ref8 doi: 10.1109/TIP.2017.2713099 – ident: ref19 doi: 10.1109/IVMSPW.2018.8448694 – volume: 23 start-page: 1199 year: 2002 ident: ref1 article-title: Comparison of three-dimensional rotational angiography with digital subtraction angiography in the assessment of ruptured cerebral aneurysms publication-title: Amer J Neuroradiol – start-page: 10 year: 2016 ident: ref16 article-title: Deep ADMM-Net for compressive sensing MRI publication-title: Proc Adv Neural Inf Process Syst – ident: ref2 doi: 10.1118/1.2370508 – ident: ref11 doi: 10.1038/nature25988 – ident: ref25 doi: 10.1109/ACCESS.2016.2624938 – ident: ref36 doi: 10.1109/TMI.2018.2827462 – ident: ref32 doi: 10.1109/TRPMS.2020.2991887 – ident: ref44 doi: 10.1088/0031-9155/60/8/N131 – ident: ref23 doi: 10.1016/j.apm.2018.07.006 – year: 2017 ident: ref35 article-title: Improved training of Wasserstein GANs publication-title: arXiv 1704 00028 – ident: ref17 doi: 10.1109/TMI.2018.2799231 – ident: ref24 doi: 10.1109/TMI.2018.2878226 – ident: ref9 doi: 10.1109/TMI.2018.2823768
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Snippet	Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and...
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SubjectTerms	Algorithms Biomedical measurement compressed sensing Computed tomography Computed tomography (CT) Deep learning Domains Drones Embedding Generative adversarial networks Image processing Image Processing, Computer-Assisted Image quality Image reconstruction Imaging iterative reconstruction Magnetic resonance imaging Medical imaging Modules Optimization Phantoms, Imaging Positron emission Reconstruction algorithms Single photon emission computed tomography sparse-view CT reconstruction Tomography Tomography, X-Ray Computed
Title	DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction
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