Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution

We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE–VM–VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For es...

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Vydáno v:PloS one Ročník 15; číslo 12; s. e0244745
Hlavní autoři: Gomi, Tsutomu, Hara, Hidetake, Watanabe, Yusuke, Mizukami, Shinya
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 31.12.2020
Public Library of Science (PLoS)
Témata:
Algorithms
Aluminum
Artificial neural networks
Biology and Life Sciences
Chest
Computer simulation
Decomposition
Deep learning
Digital imaging
Engineering and Technology
Health sciences
Humans
Image contrast
Image processing
Image Processing, Computer-Assisted - methods
Image quality
Image reconstruction
Image resolution
Lung cancer
Lung Neoplasms - diagnostic imaging
Medicine and Health Sciences
Modulation transfer function
Neural networks
Neural Networks, Computer
Nodules
Noise
Opacity
Optical communication
Phantoms, Imaging
Physical Sciences
Projection
Radiography
Radiography, Thoracic - methods
Research and Analysis Methods
Ripples
Stochasticity
Tomography, X-Ray Computed - methods
Japan
ISSN:1932-6203, 1932-6203
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Abstract We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE–VM–VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE–VM–VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage–thresholding algorithm (FISTA); SART–TV–FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE–VM–VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART–TV–FISTA, and DE–VM–SART–TV–FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE–VM–VDSR with BF improved the overall performance in terms of SDNR (DE–VM–VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART–TV–FISTA: 0.0984; and DE–VM–SART–TV–FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE–VM–VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE–VM–VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2–0.3 cycles/mm). Finally, based on the overall image quality, DE–VM–VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.
AbstractList We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE–VM–VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE–VM–VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage–thresholding algorithm (FISTA); SART–TV–FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE–VM–VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART–TV–FISTA, and DE–VM–SART–TV–FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE–VM–VDSR with BF improved the overall performance in terms of SDNR (DE–VM–VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART–TV–FISTA: 0.0984; and DE–VM–SART–TV–FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE–VM–VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE–VM–VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2–0.3 cycles/mm). Finally, based on the overall image quality, DE–VM–VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.
We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE-VM-VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE-VM-VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage-thresholding algorithm (FISTA); SART-TV-FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE-VM-VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART-TV-FISTA, and DE-VM-SART-TV-FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE-VM-VDSR with BF improved the overall performance in terms of SDNR (DE-VM-VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART-TV-FISTA: 0.0984; and DE-VM-SART-TV-FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE-VM-VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE-VM-VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2-0.3 cycles/mm). Finally, based on the overall image quality, DE-VM-VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE-VM-VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE-VM-VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage-thresholding algorithm (FISTA); SART-TV-FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE-VM-VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART-TV-FISTA, and DE-VM-SART-TV-FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE-VM-VDSR with BF improved the overall performance in terms of SDNR (DE-VM-VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART-TV-FISTA: 0.0984; and DE-VM-SART-TV-FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE-VM-VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE-VM-VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2-0.3 cycles/mm). Finally, based on the overall image quality, DE-VM-VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.
Audience Academic
Author Gomi, Tsutomu
Watanabe, Yusuke
Hara, Hidetake
Mizukami, Shinya
AuthorAffiliation School of Allied Health Sciences, Kitasato University, Sagamihara, Kanagawa, Japan
Korea National University of Transportation, REPUBLIC OF KOREA
AuthorAffiliation_xml – name: Korea National University of Transportation, REPUBLIC OF KOREA
– name: School of Allied Health Sciences, Kitasato University, Sagamihara, Kanagawa, Japan
Author_xml – sequence: 1
  givenname: Tsutomu
  orcidid: 0000-0002-2322-714X
  surname: Gomi
  fullname: Gomi, Tsutomu
– sequence: 2
  givenname: Hidetake
  surname: Hara
  fullname: Hara, Hidetake
– sequence: 3
  givenname: Yusuke
  surname: Watanabe
  fullname: Watanabe, Yusuke
– sequence: 4
  givenname: Shinya
  surname: Mizukami
  fullname: Mizukami, Shinya
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33382766$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1109/TIT.2006.871582
10.1148/radiology.147.3.6342037
10.1118/1.4903283
10.1148/radiology.145.2.7134457
10.1109/MSP.2007.914731
10.1118/1.595958
10.1109/TMI.2017.2708987
10.1137/080716542
10.1016/j.cmpb.2020.105615
10.1002/mp.12843
10.1118/1.1561285
10.1088/0031-9155/53/17/021
10.1148/radiology.179.2.2014294
10.1371/journal.pone.0150985
10.1088/0031-9155/61/24/8425
10.1148/98.2.249
10.1016/j.acra.2013.08.006
10.1002/mp.12933
10.1109/JSTSP.2007.910281
10.1137/S0097539792240406
10.1007/s10278-017-0033-z
10.1148/radiol.2492071956
10.1056/NEJMoa1113205
10.1118/1.4725171
10.3322/caac.21492
10.1109/TIP.2009.2028250
10.1118/1.2207127
10.1118/1.3097632
10.1088/0031-9155/21/5/002
10.1088/1361-6560/aacdd4
10.3390/s19143234
10.1002/cpa.20042
10.1177/016173468400600107
10.1109/CVPR.2016.182
10.1148/radiology.160.2.3726105
10.1148/radiology.161.3.3786710
10.1148/107.3.669
10.1088/0031-9155/48/19/R01
10.1109/CVPR.2016.207
10.1109/ICCVW.2015.17
10.1016/j.acra.2010.09.021
10.1148/rg.302095097
10.1109/TIP.2017.2662206
10.1118/1.4928603
10.1148/radiology.148.3.6308709
10.1148/radiology.217.1.r00oc33251
10.1118/1.3056554
10.1016/j.compmedimag.2019.101647
10.1109/TMI.2017.2715284
10.1016/j.ejmp.2018.07.011
10.1148/radiographics.11.5.1947318
10.1259/bjr.20160555
10.1148/radiology.135.1.7360972
10.1016/j.compbiomed.2018.10.011
10.1148/radiol.2019181960
10.1148/rg.2016150081
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References S Hamidian (pone.0244745.ref040) 2017
A Halder (pone.0244745.ref041) 2020
MAT Figueiredo (pone.0244745.ref062) 2007; 1
O Ozan (pone.0244745.ref056) 2016; 2016
pone.0244745.ref044
DL Donoho (pone.0244745.ref050) 2006; 52
EJ Chae (pone.0244745.ref019) 2008; 249
BK Natarajan (pone.0244745.ref060) 1995; 24
EJ Candes (pone.0244745.ref061) 2008; 25
J Choe (pone.0244745.ref043) 2019; 292
S Xu (pone.0244745.ref025) 2015; 42
S Sone (pone.0244745.ref012) 1991; 11
CE Cann (pone.0244745.ref021) 1982; 145
A Beck (pone.0244745.ref030) 2009; 18
T Gomi (pone.0244745.ref015) 2013; 20
pone.0244745.ref049
T Gomi (pone.0244745.ref046) 2018; 53
K Zhang (pone.0244745.ref031) 2017; 26
EY Sidky (pone.0244745.ref051) 2008; 53
W Zhao (pone.0244745.ref020) 2018; 45
SS Siegelman (pone.0244745.ref004) 1986; 160
G Ma (pone.0244745.ref017) 2016; 11
pone.0244745.ref034
A Otrakji (pone.0244745.ref018) 2016; 36
K Umehara (pone.0244745.ref036) 2018; 31
WA Kalender (pone.0244745.ref023) 1986; 13
K Murata (pone.0244745.ref008) 1986; 161
H Chen (pone.0244745.ref032) 2017; 36
X Liu (pone.0244745.ref066) 2009; 36
M Gerlinger (pone.0244745.ref006) 2012; 366
Z Wang (pone.0244745.ref037) 2020
T Gomi (pone.0244745.ref014) 2011; 18
Y Du (pone.0244745.ref064) 2016; 61
Y Lu (pone.0244745.ref026) 2015; 42
H Zhang (pone.0244745.ref055) 2019; 19
RA Kruger (pone.0244745.ref011) 1983; 147
E Samei (pone.0244745.ref054) 2003; 30
K Imai (pone.0244745.ref052) 2009; 36
T Wu (pone.0244745.ref065) 2006; 33
K Miyata (pone.0244745.ref028) 2017; 90
DI Eun (pone.0244745.ref038) 2020; 196
A Krizhevsky (pone.0244745.ref048) 2012
J Park (pone.0244745.ref035) 2018; 63
D Lee (pone.0244745.ref042) 2018
DF Yankelevitz (pone.0244745.ref001) 2000; 217
ER Miller (pone.0244745.ref009) 1971; 98
J Winter (pone.0244745.ref045) 1980; 135
S Richard (pone.0244745.ref053) 2012; 39
F Bray (pone.0244745.ref005) 2018; 68
D Chao (pone.0244745.ref057) 2016; 2016
JR Muhm (pone.0244745.ref003) 1983; 148
JT Dobbins (pone.0244745.ref013) 2003; 48
I Daubechies (pone.0244745.ref063) 2004; 57
AH Andersen (pone.0244745.ref024) 1984; 6
CV Zwirewich (pone.0244745.ref007) 1991; 179
A Beck (pone.0244745.ref029) 2009; 2
Y Gu (pone.0244745.ref039) 2018; 103
S Choi (pone.0244745.ref027) 2018; 45
CH Pham (pone.0244745.ref059) 2019; 77
RE Alvarez (pone.0244745.ref022) 1976; 21
JM Wolterink (pone.0244745.ref033) 2017; 36
FP Stitik (pone.0244745.ref002) 1978; 16
M Berger (pone.0244745.ref047) 1987
pone.0244745.ref058
NA Baily (pone.0244745.ref010) 1973; 107
H Machida (pone.0244745.ref016) 2010; 30
References_xml – volume: 52
  start-page: 1289
  issue: 4
  year: 2006
  ident: pone.0244745.ref050
  article-title: Compressed sensing
  publication-title: IEEE Transactions on Information Theory
  doi: 10.1109/TIT.2006.871582
– volume: 2016
  start-page: 246
  year: 2016
  ident: pone.0244745.ref056
  article-title: Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks
  publication-title: Medical Image Computing and Computer-Assisted Intervention—MICCAI
– ident: pone.0244745.ref049
– start-page: 1097
  year: 2012
  ident: pone.0244745.ref048
  article-title: Imagenet classification with deep convolutional neural networks
  publication-title: Advances in neural information processing systems
– year: 2020
  ident: pone.0244745.ref041
  article-title: Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review
  publication-title: J Digit Imaging
– start-page: 1
  year: 1987
  ident: pone.0244745.ref047
  article-title: Photon cross sections on a personal computer
  publication-title: Gent Radiat Res
– volume: 147
  start-page: 863
  issue: 3
  year: 1983
  ident: pone.0244745.ref011
  article-title: Dynamic tomographic digital subtraction angiography using temporal filtration
  publication-title: Radiology
  doi: 10.1148/radiology.147.3.6342037
– volume: 42
  start-page: 182
  issue: 1
  year: 2015
  ident: pone.0244745.ref026
  article-title: Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis
  publication-title: Med Phys
  doi: 10.1118/1.4903283
– volume: 145
  start-page: 493
  issue: 2
  year: 1982
  ident: pone.0244745.ref021
  article-title: Quantification of calcium in solitary pulmonary nodules using single- and dual-energy CT
  publication-title: Radiology
  doi: 10.1148/radiology.145.2.7134457
– volume: 25
  start-page: 21
  issue: 2
  year: 2008
  ident: pone.0244745.ref061
  article-title: An Introduction To Compressive Sampling
  publication-title: IEEE Signal Processing Magazine
  doi: 10.1109/MSP.2007.914731
– volume: 13
  start-page: 334
  issue: 3
  year: 1986
  ident: pone.0244745.ref023
  article-title: Evaluation of a prototype dual-energy computed tomographic apparatus. I. Phantom studies
  publication-title: Med Phys
  doi: 10.1118/1.595958
– volume: 36
  start-page: 2536
  issue: 12
  year: 2017
  ident: pone.0244745.ref033
  article-title: Generative Adversarial Networks for Noise Reduction in Low-Dose CT
  publication-title: IEEE Trans Med Imaging
  doi: 10.1109/TMI.2017.2708987
– volume: 2
  start-page: 183
  issue: 1
  year: 2009
  ident: pone.0244745.ref029
  article-title: A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
  publication-title: SIAM Journal on Imaging Sciences
  doi: 10.1137/080716542
– volume: 196
  start-page: 105615
  year: 2020
  ident: pone.0244745.ref038
  article-title: CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels
  publication-title: Comput Methods Programs Biomed
  doi: 10.1016/j.cmpb.2020.105615
– volume: 45
  start-page: 1871
  issue: 5
  year: 2018
  ident: pone.0244745.ref027
  article-title: Development of a chest digital tomosynthesis R/F system and implementation of low-dose GPU-accelerated compressed sensing (CS) image reconstruction
  publication-title: Med Phys
  doi: 10.1002/mp.12843
– volume: 16
  start-page: 347
  issue: 3
  year: 1978
  ident: pone.0244745.ref002
  article-title: Radiographic screening in the early detection of lung cancer
  publication-title: Radiol Clin North Am
– volume: 30
  start-page: 608
  issue: 4
  year: 2003
  ident: pone.0244745.ref054
  article-title: An experimental comparison of detector performance for direct and indirect digital radiography systems
  publication-title: Med Phys
  doi: 10.1118/1.1561285
– volume: 53
  start-page: 4777
  issue: 17
  year: 2008
  ident: pone.0244745.ref051
  article-title: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization
  publication-title: Phys Med Biol
  doi: 10.1088/0031-9155/53/17/021
– volume: 179
  start-page: 469
  issue: 2
  year: 1991
  ident: pone.0244745.ref007
  article-title: Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation
  publication-title: Radiology
  doi: 10.1148/radiology.179.2.2014294
– volume: 11
  start-page: e0150985
  issue: 3
  year: 2016
  ident: pone.0244745.ref017
  article-title: Improving Image Quality of Bronchial Arteries with Virtual Monochromatic Spectral CT Images
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0150985
– volume: 61
  start-page: 8425
  issue: 24
  year: 2016
  ident: pone.0244745.ref064
  article-title: Evaluation of hybrid SART + OS + TV iterative reconstruction algorithm for optical-CT gel dosimeter imaging
  publication-title: Phys Med Biol
  doi: 10.1088/0031-9155/61/24/8425
– start-page: 10134
  year: 2017
  ident: pone.0244745.ref040
  article-title: 3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT
  publication-title: Proc SPIE Int Soc Opt Eng
– volume: 98
  start-page: 249
  issue: 2
  year: 1971
  ident: pone.0244745.ref009
  article-title: An infinite number of laminagrams from a finite number of radiographs
  publication-title: Radiology
  doi: 10.1148/98.2.249
– volume: 20
  start-page: 1357
  issue: 11
  year: 2013
  ident: pone.0244745.ref015
  article-title: Comparison of chest dual-energy subtraction digital tomosynthesis and dual-energy subtraction radiography for detection of pulmonary nodules: initial evaluations in human clinical cases
  publication-title: Acad Radiol
  doi: 10.1016/j.acra.2013.08.006
– volume: 45
  start-page: 2964
  issue: 7
  year: 2018
  ident: pone.0244745.ref020
  article-title: A unified material decomposition framework for quantitative dual- and triple-energy CT imaging
  publication-title: Med Phys
  doi: 10.1002/mp.12933
– year: 2018
  ident: pone.0244745.ref042
  article-title: Restoration of Full Data from Sparse Data in Low-Dose Chest Digital Tomosynthesis Using Deep Convolutional Neural Networks
  publication-title: J Digit Imaging
– volume: 1
  start-page: 586
  issue: 4
  year: 2007
  ident: pone.0244745.ref062
  article-title: Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
  publication-title: IEEE Journal of Selected Topics in Signal Processing
  doi: 10.1109/JSTSP.2007.910281
– volume: 24
  start-page: 227
  issue: 2
  year: 1995
  ident: pone.0244745.ref060
  article-title: Sparse Approximate Solutions to Linear Systems
  publication-title: SIAM Journal on Computing
  doi: 10.1137/S0097539792240406
– volume: 31
  start-page: 441
  issue: 4
  year: 2018
  ident: pone.0244745.ref036
  article-title: Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT
  publication-title: J Digit Imaging
  doi: 10.1007/s10278-017-0033-z
– volume: 249
  start-page: 671
  issue: 2
  year: 2008
  ident: pone.0244745.ref019
  article-title: Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience
  publication-title: Radiology
  doi: 10.1148/radiol.2492071956
– volume: 366
  start-page: 883
  issue: 10
  year: 2012
  ident: pone.0244745.ref006
  article-title: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing
  publication-title: N Engl J Med
  doi: 10.1056/NEJMoa1113205
– volume: 39
  start-page: 4115
  issue: 7
  year: 2012
  ident: pone.0244745.ref053
  article-title: Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms
  publication-title: Med Phys
  doi: 10.1118/1.4725171
– volume: 68
  start-page: 394
  issue: 6
  year: 2018
  ident: pone.0244745.ref005
  article-title: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
  publication-title: CA Cancer J Clin
  doi: 10.3322/caac.21492
– volume: 18
  start-page: 2419
  issue: 11
  year: 2009
  ident: pone.0244745.ref030
  article-title: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems
  publication-title: IEEE Trans Image Process
  doi: 10.1109/TIP.2009.2028250
– volume: 33
  start-page: 2461
  issue: 7
  year: 2006
  ident: pone.0244745.ref065
  article-title: Voting strategy for artifact reduction in digital breast tomosynthesis
  publication-title: Med Phys
  doi: 10.1118/1.2207127
– volume: 36
  start-page: 1602
  issue: 5
  year: 2009
  ident: pone.0244745.ref066
  article-title: Quantitative imaging of element composition and mass fraction using dual-energy CT: three-material decomposition
  publication-title: Med Phys
  doi: 10.1118/1.3097632
– volume: 21
  start-page: 733
  issue: 5
  year: 1976
  ident: pone.0244745.ref022
  article-title: Energy-selective reconstructions in X-ray computerized tomography
  publication-title: Phys Med Biol
  doi: 10.1088/0031-9155/21/5/002
– volume: 63
  start-page: 145011
  issue: 14
  year: 2018
  ident: pone.0244745.ref035
  article-title: Computed tomography super-resolution using deep convolutional neural network
  publication-title: Phys Med Biol
  doi: 10.1088/1361-6560/aacdd4
– volume: 19
  issue: 14
  year: 2019
  ident: pone.0244745.ref055
  article-title: A Comparable Study of CNN-Based Single Image Super-Resolution for Space-Based Imaging Sensors
  publication-title: Sensors (Basel)
  doi: 10.3390/s19143234
– volume: 57
  start-page: 1413
  issue: 11
  year: 2004
  ident: pone.0244745.ref063
  article-title: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
  publication-title: Communications on Pure and Applied Mathematics
  doi: 10.1002/cpa.20042
– volume: 6
  start-page: 81
  issue: 1
  year: 1984
  ident: pone.0244745.ref024
  article-title: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm
  publication-title: Ultrason Imaging
  doi: 10.1177/016173468400600107
– ident: pone.0244745.ref044
  doi: 10.1109/CVPR.2016.182
– volume: 160
  start-page: 307
  issue: 2
  year: 1986
  ident: pone.0244745.ref004
  article-title: Solitary pulmonary nodules: CT assessment
  publication-title: Radiology
  doi: 10.1148/radiology.160.2.3726105
– volume: 161
  start-page: 641
  issue: 3
  year: 1986
  ident: pone.0244745.ref008
  article-title: Centrilobular lesions of the lung: demonstration by high-resolution CT and pathologic correlation
  publication-title: Radiology
  doi: 10.1148/radiology.161.3.3786710
– volume: 107
  start-page: 669
  issue: 3
  year: 1973
  ident: pone.0244745.ref010
  publication-title: Electrofluoroplanigraphy. Radiology
  doi: 10.1148/107.3.669
– volume: 48
  start-page: R65
  issue: 19
  year: 2003
  ident: pone.0244745.ref013
  article-title: Digital x-ray tomosynthesis: current state of the art and clinical potential
  publication-title: Phys Med Biol
  doi: 10.1088/0031-9155/48/19/R01
– ident: pone.0244745.ref058
  doi: 10.1109/CVPR.2016.207
– ident: pone.0244745.ref034
  doi: 10.1109/ICCVW.2015.17
– volume: 18
  start-page: 191
  issue: 2
  year: 2011
  ident: pone.0244745.ref014
  article-title: Comparison of chest dual-energy subtraction digital tomosynthesis imaging and dual-energy subtraction radiography to detect simulated pulmonary nodules with and without calcifications a phantom study
  publication-title: Acad Radiol
  doi: 10.1016/j.acra.2010.09.021
– volume: 30
  start-page: 549
  issue: 2
  year: 2010
  ident: pone.0244745.ref016
  article-title: Optimizing parameters for flat-panel detector digital tomosynthesis
  publication-title: Radiographics
  doi: 10.1148/rg.302095097
– volume: 26
  start-page: 3142
  issue: 7
  year: 2017
  ident: pone.0244745.ref031
  article-title: Beyond a gauaaian denoiser: residual learning of deep CNN for image denoising
  publication-title: IEEE Trans Image Process
  doi: 10.1109/TIP.2017.2662206
– volume: 42
  start-page: 5377
  issue: 9
  year: 2015
  ident: pone.0244745.ref025
  article-title: Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis
  publication-title: Med Phys
  doi: 10.1118/1.4928603
– volume: 148
  start-page: 609
  issue: 3
  year: 1983
  ident: pone.0244745.ref003
  article-title: Lung cancer detected during a screening program using four-month chest radiographs
  publication-title: Radiology
  doi: 10.1148/radiology.148.3.6308709
– volume: 217
  start-page: 251
  issue: 1
  year: 2000
  ident: pone.0244745.ref001
  article-title: Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation
  publication-title: Radiology
  doi: 10.1148/radiology.217.1.r00oc33251
– volume: 36
  start-page: 492
  issue: 2
  year: 2009
  ident: pone.0244745.ref052
  article-title: Statistical characteristics of streak artifacts on CT images: relationship between streak artifacts and mA s values
  publication-title: Med Phys
  doi: 10.1118/1.3056554
– volume: 77
  start-page: 101647
  year: 2019
  ident: pone.0244745.ref059
  article-title: Multiscale brain MRI super-resolution using deep 3D convolutional networks
  publication-title: Comput Med Imaging Graph
  doi: 10.1016/j.compmedimag.2019.101647
– volume: 36
  start-page: 2524
  issue: 12
  year: 2017
  ident: pone.0244745.ref032
  article-title: Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
  publication-title: IEEE Trans Med Imaging
  doi: 10.1109/TMI.2017.2715284
– volume: 53
  start-page: 4
  year: 2018
  ident: pone.0244745.ref046
  article-title: Development of a novel algorithm for metal artifact reduction in digital tomosynthesis using projection-based dual-energy material decomposition for arthroplasty: A phantom study
  publication-title: Phys Med
  doi: 10.1016/j.ejmp.2018.07.011
– volume: 2016
  start-page: 391
  year: 2016
  ident: pone.0244745.ref057
  article-title: Accelerating the Super-Resolution Convolutional Neural Network
  publication-title: Computer Vision–ECCV
– volume: 11
  start-page: 807
  issue: 5
  year: 1991
  ident: pone.0244745.ref012
  article-title: Development of a high-resolution digital tomosynthesis system and its clinical application
  publication-title: Radiographics
  doi: 10.1148/radiographics.11.5.1947318
– volume: 90
  start-page: 20160555
  issue: 1071
  year: 2017
  ident: pone.0244745.ref028
  article-title: A phantom study for ground-glass nodule detectability using chest digital tomosynthesis with iterative reconstruction algorithm by ten observers: association with radiation dose and nodular characteristics
  publication-title: Br J Radiol
  doi: 10.1259/bjr.20160555
– volume: 135
  start-page: 234
  issue: 1
  year: 1980
  ident: pone.0244745.ref045
  article-title: Edge enhancement of computed tomograms by digital unsharp masking
  publication-title: Radiology
  doi: 10.1148/radiology.135.1.7360972
– volume: 103
  start-page: 220
  year: 2018
  ident: pone.0244745.ref039
  article-title: Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs
  publication-title: Comput Biol Med
  doi: 10.1016/j.compbiomed.2018.10.011
– year: 2020
  ident: pone.0244745.ref037
  article-title: Deep Learning for Image Super-resolution: A Survey
  publication-title: IEEE Trans Pattern Anal Mach Intell
– volume: 292
  start-page: 365
  issue: 2
  year: 2019
  ident: pone.0244745.ref043
  article-title: Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses
  publication-title: Radiology
  doi: 10.1148/radiol.2019181960
– volume: 36
  start-page: 38
  issue: 1
  year: 2016
  ident: pone.0244745.ref018
  article-title: Dual-Energy CT: Spectrum of Thoracic Abnormalities
  publication-title: Radiographics
  doi: 10.1148/rg.2016150081
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Snippet We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm...
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StartPage e0244745
SubjectTerms Algorithms
Aluminum
Artificial neural networks
Biology and Life Sciences
Chest
Computer simulation
Decomposition
Deep learning
Digital imaging
Engineering and Technology
Health sciences
Humans
Image contrast
Image processing
Image Processing, Computer-Assisted - methods
Image quality
Image reconstruction
Image resolution
Lung cancer
Lung Neoplasms - diagnostic imaging
Medicine and Health Sciences
Modulation transfer function
Neural networks
Neural Networks, Computer
Nodules
Noise
Opacity
Optical communication
Phantoms, Imaging
Physical Sciences
Projection
Radiography
Radiography, Thoracic - methods
Research and Analysis Methods
Ripples
Stochasticity
Tomography, X-Ray Computed - methods
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Title Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution
URI https://www.ncbi.nlm.nih.gov/pubmed/33382766
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Volume 15
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