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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| Veröffentlicht in: | PloS one 2020-12, Vol.15 (12), p.e0244745-e0244745 |
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| creator | Gomi, Tsutomu Hara, Hidetake Watanabe, Yusuke Mizukami, Shinya |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0244745 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0244745</identifier><identifier>PMID: 33382766</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2020-12, Vol.15 (12), p.e0244745-e0244745</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Gomi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Gomi et al 2020 Gomi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-197e4a82a203af65d6ea37227958cb76f431cbe4401e51c3248b6cd70f8019ef3</citedby><cites>FETCH-LOGICAL-c692t-197e4a82a203af65d6ea37227958cb76f431cbe4401e51c3248b6cd70f8019ef3</cites><orcidid>0000-0002-2322-714X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774945/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774945/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33382766$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gwak, Jeonghwan</contributor><creatorcontrib>Gomi, Tsutomu</creatorcontrib><creatorcontrib>Hara, Hidetake</creatorcontrib><creatorcontrib>Watanabe, Yusuke</creatorcontrib><creatorcontrib>Mizukami, Shinya</creatorcontrib><title>Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Algorithms</subject><subject>Aluminum</subject><subject>Artificial neural networks</subject><subject>Biology and Life Sciences</subject><subject>Chest</subject><subject>Computer simulation</subject><subject>Decomposition</subject><subject>Deep learning</subject><subject>Digital imaging</subject><subject>Engineering and Technology</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Image resolution</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>Medicine and Health Sciences</subject><subject>Modulation transfer function</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Nodules</subject><subject>Noise</subject><subject>Opacity</subject><subject>Optical communication</subject><subject>Phantoms, Imaging</subject><subject>Physical Sciences</subject><subject>Projection</subject><subject>Radiography</subject><subject>Radiography, Thoracic - methods</subject><subject>Research and Analysis Methods</subject><subject>Ripples</subject><subject>Stochasticity</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu1DAQhiMEoqXwBggsISG42MWn2MlNparisFKlSpxuLceZZL0k8dZ2tuz78KB4u2nVRb1AuXBsf_8_mZlMlr0keE6YJB9WbvSD7uZrN8AcU84lzx9lx6RkdCYoZo_vvR9lz0JYYZyzQoin2RFjrKBSiOPsz6Jfe7eBGtW2tVF3yCwhRBRd78J2iGljA7K9bgFdjbqzcYuqLRoDINcgjZJ4BSZaN8wqHXY2CZrBAL7doo31MW1R7wZnlt71OlqDjBs2rht3mnQ3wOhvlnjt_C90beMShXENHnkIE_Y8e9LoLsCLaT3Jfnz6-P38y-zi8vPi_OxiZkRJ44yUErguqE4Z60bktQDNJKWyzAtTSdFwRkwFnGMCOTGM8qISppa4KTApoWEn2eu977pzQU0FDoqm0nJRMlIkYrEnaqdXau1TYfxWOW3VzYHzrdI-JdmBApOXRpIqzwXjNaOFaTBLsQxpSskbk7xOp2hj1UNtYIipEgemhzeDXarWbZSUkpc8TwbvJgPvrsbUNdXbYKDr9ABu3H93jjHmLKFv_kEfzm6iWp0SsEPjUlyzM1VngkuSS8poouYPUOmpobepudDYdH4geH8gSEyE37HVYwhq8e3r_7OXPw_Zt_fYJeguLm9_mXAI8j1ovAvBQ3NXZILVbphuq6F2w6SmYUqyV_cbdCe6nR72FwpdHlE</recordid><startdate>20201231</startdate><enddate>20201231</enddate><creator>Gomi, Tsutomu</creator><creator>Hara, Hidetake</creator><creator>Watanabe, Yusuke</creator><creator>Mizukami, Shinya</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2322-714X</orcidid></search><sort><creationdate>20201231</creationdate><title>Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution</title><author>Gomi, Tsutomu ; Hara, Hidetake ; Watanabe, Yusuke ; Mizukami, Shinya</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-197e4a82a203af65d6ea37227958cb76f431cbe4401e51c3248b6cd70f8019ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Aluminum</topic><topic>Artificial neural networks</topic><topic>Biology and Life Sciences</topic><topic>Chest</topic><topic>Computer simulation</topic><topic>Decomposition</topic><topic>Deep learning</topic><topic>Digital imaging</topic><topic>Engineering and Technology</topic><topic>Health sciences</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Image resolution</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Medicine and Health Sciences</topic><topic>Modulation transfer function</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Nodules</topic><topic>Noise</topic><topic>Opacity</topic><topic>Optical communication</topic><topic>Phantoms, Imaging</topic><topic>Physical Sciences</topic><topic>Projection</topic><topic>Radiography</topic><topic>Radiography, Thoracic - methods</topic><topic>Research and Analysis Methods</topic><topic>Ripples</topic><topic>Stochasticity</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gomi, Tsutomu</creatorcontrib><creatorcontrib>Hara, Hidetake</creatorcontrib><creatorcontrib>Watanabe, Yusuke</creatorcontrib><creatorcontrib>Mizukami, Shinya</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gomi, Tsutomu</au><au>Hara, Hidetake</au><au>Watanabe, Yusuke</au><au>Mizukami, Shinya</au><au>Gwak, Jeonghwan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-12-31</date><risdate>2020</risdate><volume>15</volume><issue>12</issue><spage>e0244745</spage><epage>e0244745</epage><pages>e0244745-e0244745</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33382766</pmid><doi>10.1371/journal.pone.0244745</doi><tpages>e0244745</tpages><orcidid>https://orcid.org/0000-0002-2322-714X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2020-12, Vol.15 (12), p.e0244745-e0244745 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2474469318 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | 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 |
| title | Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution |
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