Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study
Objective Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back p...
Saved in:
| Published in | European radiology Vol. 33; no. 1; pp. 368 - 379 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1432-1084 0938-7994 1432-1084 |
| DOI | 10.1007/s00330-022-08983-1 |
Cover
| Abstract | Objective
Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.
Materials and methods
Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey’s HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.
Results
For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (
p
< 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (
p
< 0.05).
Conclusion
Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.
Key Points
• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05).
• Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05).
• Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05). |
|---|---|
| AbstractList | Objective
Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.
Materials and methods
Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey’s HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.
Results
For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (
p
< 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (
p
< 0.05).
Conclusion
Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.
Key Points
• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05).
• Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05).
• Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05). ObjectiveUltra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.Materials and methodsLung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey’s HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.ResultsFor each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05).ConclusionScan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.Key Points• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05).• Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05).• Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05). Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.OBJECTIVEUltra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.MATERIALS AND METHODSLung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05).RESULTSFor each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05).Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.CONCLUSIONScan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05).KEY POINTS• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05). Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA. Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness. For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05). Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT. • Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05). |
| Author | Toyama, Hiroshi Ida, Yoshihiro Fujisawa, Yasuko Shigemura, Chika Murayama, Kazuhiro Ohno, Yoshiharu Fujii, Kenji Kimata, Hirona Oshima, Yuka Obama, Yuki Ueda, Takahiro Hamabuchi, Nayu Ikeda, Hirotaka Watanabe, Ayumi Hanamatsu, Satomu Akino, Naruomi Ito, Yuya Kataoka, Yumi |
| Author_xml | – sequence: 1 givenname: Yoshiharu orcidid: 0000-0002-4431-1084 surname: Ohno fullname: Ohno, Yoshiharu email: yohno@fujita-hu.ac.jp organization: Department of Radiology, Fujita Health University School of Medicine, Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine – sequence: 2 givenname: Naruomi surname: Akino fullname: Akino, Naruomi organization: Canon Medical Systems Corporation – sequence: 3 givenname: Yasuko surname: Fujisawa fullname: Fujisawa, Yasuko organization: Canon Medical Systems Corporation – sequence: 4 givenname: Hirona surname: Kimata fullname: Kimata, Hirona organization: Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine – sequence: 5 givenname: Yuya surname: Ito fullname: Ito, Yuya organization: Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine – sequence: 6 givenname: Kenji surname: Fujii fullname: Fujii, Kenji organization: Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine – sequence: 7 givenname: Yumi surname: Kataoka fullname: Kataoka, Yumi organization: Department of Radiology, Fujita Health University Hospital – sequence: 8 givenname: Yoshihiro surname: Ida fullname: Ida, Yoshihiro organization: Department of Radiology, Fujita Health University Hospital – sequence: 9 givenname: Yuka surname: Oshima fullname: Oshima, Yuka organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 10 givenname: Nayu surname: Hamabuchi fullname: Hamabuchi, Nayu organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 11 givenname: Chika surname: Shigemura fullname: Shigemura, Chika organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 12 givenname: Ayumi surname: Watanabe fullname: Watanabe, Ayumi organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 13 givenname: Yuki surname: Obama fullname: Obama, Yuki organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 14 givenname: Satomu surname: Hanamatsu fullname: Hanamatsu, Satomu organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 15 givenname: Takahiro surname: Ueda fullname: Ueda, Takahiro organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 16 givenname: Hirotaka surname: Ikeda fullname: Ikeda, Hirotaka organization: Department of Radiology, Fujita Health University School of Medicine – sequence: 17 givenname: Kazuhiro surname: Murayama fullname: Murayama, Kazuhiro organization: Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine – sequence: 18 givenname: Hiroshi surname: Toyama fullname: Toyama, Hiroshi organization: Department of Radiology, Fujita Health University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35841417$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1TAQRiNURB_wB1ggS2xYNGAnTpwsS8SjUiWEdFlHjj25ceXYwQ-q-99Z4OSWVxddeSydM_6smfPsxFgDWfaS4LcEY_bOY1yWOMdFkeOmbcqcPMnOCC2LnOCGnvxTn2bn3t9ijFtC2bPstKwaSihhZ9nPzs4Ld8pbg-yIdDR71O2QifMADnEjEVfujh-QVDMYrxIGP7iOPKyl4AsflFZBgV_1qIPj-aT2U-7AWx03qttdouhV6izVOIIDE5AX3KDZyuStjzgQ1vjgotiMGcJkpUfKCB3lZgIsSAN3Zr39j1-iOxUmlBQPa4o5xVC5hAAiWLd-RxnE0dfr91dombgJdkY-RHl4nj0dufbw4v68yL59_LDrPuc3Xz5dd1c3uShZFfK64gMZKW1lwaisCCcAsm4aBnWLgdRMFrQhgg0wCiHrgTMp-NCWFWsGQjmUF9mbY9_F2e8RfOhn5QVozQ3Y6PuibgmmbUuLhL5-gN7a6ExK1xesonVFClYm6tU9FYcZZL84NXN36H_PNQHFERDOeu9g_IMQ3K_L0x-Xp0_L02_L05MkNQ8kocI26DRVpR9Xy6Pq0ztmD-5v7EesX0r93uk |
| CitedBy_id | crossref_primary_10_1007_s00062_023_01348_1 crossref_primary_10_3390_diagnostics13152518 crossref_primary_10_1007_s11604_023_01470_7 crossref_primary_10_2463_mrms_mp_2023_0068 crossref_primary_10_1016_j_ejrad_2023_110969 |
| Cites_doi | 10.1513/AnnalsATS.201710-820OC 10.1016/j.ejrad.2018.01.030 10.1007/s11604-020-01000-9 10.1007/s11604-019-00823-5 10.1183/13993003.02214-2018 10.1118/1.4747342 10.1007/s00330-019-06170-3 10.1371/journal.pone.0137165 10.1016/j.resinv.2018.07.008 10.3174/ajnr.A6377 10.1007/s11604-019-00816-4 10.1186/1465-9921-15-52 10.1007/s00330-018-5491-2 10.1016/j.ejrad.2020.109033 10.1007/s11604-018-0796-2 10.1016/S0140-6736(86)90837-8 10.1253/circj.CJ-17-1281 10.1373/clinchem.2016.268870 10.1016/j.acra.2019.09.008 10.1002/mp.12087 10.1007/s00330-019-06183-y 10.1016/j.ejrad.2019.108687 10.1007/s00234-017-1927-7 10.1007/s00330-019-06635-5 10.1007/s11604-020-00956-y 10.1148/radiol.2483071434 10.1016/j.acra.2017.11.017 10.1007/s00330-020-06704-0 10.1016/j.ejmp.2019.12.025 10.1016/S0140-6736(95)91748-9 10.1002/mp.13949 10.1007/s11604-020-01045-w |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to European Society of Radiology 2022 2022. The Author(s), under exclusive licence to European Society of Radiology. The Author(s), under exclusive licence to European Society of Radiology 2022. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to European Society of Radiology 2022 – notice: 2022. The Author(s), under exclusive licence to European Society of Radiology. – notice: The Author(s), under exclusive licence to European Society of Radiology 2022. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7RV 7X7 7XB 88E 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. KB0 LK8 M0S M1P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 |
| DOI | 10.1007/s00330-022-08983-1 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection Proquest Central Technology Collection Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Biological Sciences ProQuest Health & Medical Collection Medical Database ProQuest Biological Science Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Advanced Technologies & Aerospace Database Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | ProQuest Central Student MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1432-1084 |
| EndPage | 379 |
| ExternalDocumentID | 35841417 10_1007_s00330_022_08983_1 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Canon Medical Systems Corporation – fundername: Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology grantid: 18K07675; 20K08037; 20K08037; 20K08037 – fundername: Canon Medical Systems Corpoeation – fundername: Smoking Research Foundation funderid: http://dx.doi.org/10.13039/501100004330 – fundername: Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology grantid: 18K07675 – fundername: Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology grantid: 20K08037 |
| GroupedDBID | --- -53 -5E -5G -BR -EM -Y2 -~C .86 .VR 04C 06C 06D 0R~ 0VY 1N0 1SB 2.D 203 28- 29G 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3V. 4.4 406 408 409 40D 40E 53G 5GY 5QI 5VS 67Z 6NX 6PF 7RV 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQBU ABQSL ABSXP ABTEG ABTKH ABTMW ABULA ABUWG ABUWZ ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHVE ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACUDM ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADJJI ADKNI ADKPE ADOJX ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFJLC AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGVAE AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AZFZN B-. BA0 BBNVY BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BKEYQ BMSDO BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBD EBLON EBS ECF ECT EIHBH EIOEI EJD EMB EMOBN EN4 ESBYG EX3 F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ IMOTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW KPH LAS LK8 LLZTM M1P M4Y M7P MA- N2Q N9A NAPCQ NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RRX RSV RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SDM SHX SISQX SJYHP SMD SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZ9 SZN T13 T16 TEORI TSG TSK TSV TT1 TUC U2A U9L UDS UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK WK8 WOW YLTOR Z45 Z7R Z7U Z7X Z7Y Z7Z Z82 Z83 Z85 Z87 Z88 Z8M Z8O Z8R Z8S Z8T Z8V Z8W Z8Z Z91 Z92 ZMTXR ZOVNA ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADKFA AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 7QO 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 |
| ID | FETCH-LOGICAL-c375t-65ab1f449d274d51a1eed6887e690e167d2481c7befccd6ba7dcab93578b14ae3 |
| IEDL.DBID | AGYKE |
| ISSN | 1432-1084 0938-7994 |
| IngestDate | Fri Sep 05 07:40:45 EDT 2025 Tue Oct 07 06:06:21 EDT 2025 Wed Feb 19 02:26:00 EST 2025 Wed Oct 01 03:47:49 EDT 2025 Thu Apr 24 22:59:34 EDT 2025 Fri Feb 21 02:43:48 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Multi-detector computed tomography Diagnostic imaging Algorithm Lung Phantoms |
| Language | English |
| License | 2022. The Author(s), under exclusive licence to European Society of Radiology. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c375t-65ab1f449d274d51a1eed6887e690e167d2481c7befccd6ba7dcab93578b14ae3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4431-1084 |
| PMID | 35841417 |
| PQID | 2754651273 |
| PQPubID | 54162 |
| PageCount | 12 |
| ParticipantIDs | proquest_miscellaneous_2691049942 proquest_journals_2754651273 pubmed_primary_35841417 crossref_primary_10_1007_s00330_022_08983_1 crossref_citationtrail_10_1007_s00330_022_08983_1 springer_journals_10_1007_s00330_022_08983_1 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
| PublicationTitle | European radiology |
| PublicationTitleAbbrev | Eur Radiol |
| PublicationTitleAlternate | Eur Radiol |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | Nagata, Murayama, Suzuki (CR8) 2019; 37 Higaki, Nakamura, Tatsugami, Nakaura, Awai (CR17) 2019; 37 Oelsner, Smith, Hoffman (CR34) 2018; 15 Takagi, Tanaka, Nagata (CR3) 2018; 101 Tatsugami, Higaki, Nakamura (CR18) 2019; 29 Iwasawa, Sato, Yamaya (CR12) 2020; 38 CR31 Arjomandi, Zeng, Barjaktarevic (CR35) 2019; 54 CR30 Murayama, Suzuki, Nagata (CR10) 2020; 41 Yanagawa, Hata, Honda (CR6) 2018; 28 Tsubamoto, Hata, Yanagawa (CR13) 2020; 128 Oostveen, Boedeker, Brink, Prokop, de Lange, Sechopoulos (CR16) 2020; 30 Madani, Van Muylem, de Maertelaer, Zanen, Gevenois (CR32) 2008; 248 Kawashima, Ichikawa, Takata, Nagata, Hoshika, Akagi (CR15) 2020; 47 Sieren, Newell, Judy (CR22) 2021; 39 Higaki, Nakamura, Zhou (CR20) 2020; 27 Kakinuma, Moriyama, Muramatsu (CR1) 2015; 10 Hata, Yanagawa, Honda (CR2) 2018; 25 Kim, Davey, Comellas (CR33) 2014; 15 Akagi, Nakamura, Higaki (CR19) 2019; 29 Miyata, Yanagawa, Hata (CR11) 2020; 30 Mikayama, Shirasaka, Yabuuchi (CR25) 2020; 70 Motoyama, Ito, Sarai (CR5) 2018; 82 Tanabe, Shima, Sato (CR9) 2019; 120 CR26 Yoshioka, Tanaka, Takagi (CR4) 2018; 60 Matsukiyo, Ohno, Matsuyama (CR21) 2021; 39 Morita, Yamashiro, Tsuchiya, Tsubakimoto, Murayama (CR14) 2020; 38 Ohno, Fujisawa, Fujii (CR24) 2019; 37 Bland, Altman (CR27) 1986; 1 Bland, Altman (CR28) 1995; 346 Tanabe, Oguma, Sato (CR7) 2018; 56 Chen-Mayer, Fuld, Hoppel (CR23) 2017; 44 Altman, Bland (CR29) 2017; 63 N Tanabe (8983_CR9) 2019; 120 M Akagi (8983_CR19) 2019; 29 M Arjomandi (8983_CR35) 2019; 54 F Tatsugami (8983_CR18) 2019; 29 Y Ohno (8983_CR24) 2019; 37 LJ Oostveen (8983_CR16) 2020; 30 S Motoyama (8983_CR5) 2018; 82 JM Bland (8983_CR27) 1986; 1 T Miyata (8983_CR11) 2020; 30 K Murayama (8983_CR10) 2020; 41 8983_CR26 H Kawashima (8983_CR15) 2020; 47 A Hata (8983_CR2) 2018; 25 M Tsubamoto (8983_CR13) 2020; 128 EC Oelsner (8983_CR34) 2018; 15 R Matsukiyo (8983_CR21) 2021; 39 JP Sieren (8983_CR22) 2021; 39 JM Bland (8983_CR28) 1995; 346 M Yanagawa (8983_CR6) 2018; 28 A Madani (8983_CR32) 2008; 248 V Kim (8983_CR33) 2014; 15 H Takagi (8983_CR3) 2018; 101 Y Morita (8983_CR14) 2020; 38 HH Chen-Mayer (8983_CR23) 2017; 44 8983_CR31 N Tanabe (8983_CR7) 2018; 56 T Higaki (8983_CR20) 2020; 27 K Yoshioka (8983_CR4) 2018; 60 H Nagata (8983_CR8) 2019; 37 8983_CR30 R Kakinuma (8983_CR1) 2015; 10 T Higaki (8983_CR17) 2019; 37 T Iwasawa (8983_CR12) 2020; 38 R Mikayama (8983_CR25) 2020; 70 DG Altman (8983_CR29) 2017; 63 |
| References_xml | – volume: 15 start-page: 718 issue: 6 year: 2018 end-page: 727 ident: CR34 article-title: Prognostic significance of large airway dimensions on computed tomography in the general population. The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study publication-title: Ann Am Thorac Soc doi: 10.1513/AnnalsATS.201710-820OC – volume: 101 start-page: 30 year: 2018 end-page: 37 ident: CR3 article-title: Diagnostic performance of coronary CT angiography with ultra-high-resolution CT: comparison with invasive coronary angiography publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2018.01.030 – volume: 38 start-page: 953 issue: 10 year: 2020 end-page: 959 ident: CR14 article-title: Automatic bronchial segmentation on ultra-HRCT scans: advantage of the 1024-matrix size with 0.25-mm slice thickness reconstruction publication-title: Jpn J Radiol doi: 10.1007/s11604-020-01000-9 – volume: 37 start-page: 399 issue: 5 year: 2019 end-page: 411 ident: CR24 article-title: Effects of acquisition method and reconstruction algorithm for CT number measurement on standard-dose CT and reduced-dose CT: a QIBA phantom study publication-title: Jpn J Radiol doi: 10.1007/s11604-019-00823-5 – volume: 54 start-page: 1802214 issue: 4 year: 2019 ident: CR35 article-title: Radiographic lung volumes predict progression to COPD in smokers with preserved spirometry in SPIROMICS publication-title: Eur Respir J doi: 10.1183/13993003.02214-2018 – volume: 39 start-page: 5757 issue: 9 year: 2021 end-page: 5767 ident: CR22 article-title: Reference standard and statistical model for intersite and temporal comparisons of CT attenuation in a multicenter quantitative lung study publication-title: Med Phys doi: 10.1118/1.4747342 – volume: 29 start-page: 6163 issue: 11 year: 2019 end-page: 6171 ident: CR19 article-title: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT publication-title: Eur Radiol doi: 10.1007/s00330-019-06170-3 – ident: CR30 – volume: 10 issue: 9 year: 2015 ident: CR1 article-title: Ultra-high-resolution computed tomography of the lung: image quality of a prototype scanner publication-title: PLoS One doi: 10.1371/journal.pone.0137165 – volume: 56 start-page: 489 issue: 6 year: 2018 end-page: 496 ident: CR7 article-title: Quantitative measurement of airway dimensions using ultra-high resolution computed tomography publication-title: Respir Investig doi: 10.1016/j.resinv.2018.07.008 – volume: 41 start-page: 219 issue: 2 year: 2020 end-page: 223 ident: CR10 article-title: Visualization of lenticulostriate arteries on CT angiography using ultra-high-resolution CT compared with conventional-detector CT publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A6377 – volume: 37 start-page: 283 issue: 4 year: 2019 end-page: 291 ident: CR8 article-title: Initial clinical experience of a prototype ultra-high-resolution CT for assessment of small intracranial arteries publication-title: Jpn J Radiol doi: 10.1007/s11604-019-00816-4 – volume: 15 start-page: 52 issue: 1 year: 2014 ident: CR33 article-title: Clinical and computed tomographic predictors of chronic bronchitis in COPD: a cross sectional analysis of the COPDGene study publication-title: Respir Res doi: 10.1186/1465-9921-15-52 – volume: 28 start-page: 5060 issue: 12 year: 2018 end-page: 5068 ident: CR6 article-title: Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs publication-title: Eur Radiol doi: 10.1007/s00330-018-5491-2 – volume: 128 year: 2020 ident: CR13 article-title: Ultra high-resolution computed tomography with 1024-matrix: comparison with 512-matrix for the evaluation of pulmonary nodules publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2020.109033 – volume: 37 start-page: 73 issue: 1 year: 2019 end-page: 80 ident: CR17 article-title: Improvement of image quality at CT and MRI using deep learning publication-title: Jpn J Radiol doi: 10.1007/s11604-018-0796-2 – volume: 1 start-page: 307 issue: 8476 year: 1986 end-page: 310 ident: CR27 article-title: Statistical methods for assessing agreement between two methods of clinical measurement publication-title: Lancet. doi: 10.1016/S0140-6736(86)90837-8 – volume: 82 start-page: 1844 issue: 7 year: 2018 end-page: 1851 ident: CR5 article-title: Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis publication-title: Circ J doi: 10.1253/circj.CJ-17-1281 – volume: 63 start-page: 1653 issue: 10 year: 2017 end-page: 1654 ident: CR29 article-title: Assessing agreement between methods of measurement publication-title: Clin Chem doi: 10.1373/clinchem.2016.268870 – volume: 27 start-page: 82 issue: 1 year: 2020 end-page: 87 ident: CR20 article-title: Deep learning reconstruction at CT: phantom study of the image characteristics publication-title: Acad Radiol doi: 10.1016/j.acra.2019.09.008 – volume: 44 start-page: 974 issue: 3 year: 2017 end-page: 985 ident: CR23 article-title: Standardizing CT lung density measure across scanner manufacturers publication-title: Med Phys doi: 10.1002/mp.12087 – volume: 29 start-page: 5322 issue: 10 year: 2019 end-page: 5329 ident: CR18 article-title: Deep learning-based image restoration algorithm for coronary CT angiography publication-title: Eur Radiol doi: 10.1007/s00330-019-06183-y – volume: 120 year: 2019 ident: CR9 article-title: Direct evaluation of peripheral airways using ultra-high-resolution CT in chronic obstructive pulmonary disease publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2019.108687 – ident: CR31 – volume: 60 start-page: 109 issue: 1 year: 2018 end-page: 115 ident: CR4 article-title: Ultra-high-resolution CT angiography of the artery of Adamkiewicz: a feasibility study publication-title: Neuroradiology. doi: 10.1007/s00234-017-1927-7 – volume: 30 start-page: 2552 issue: 5 year: 2020 end-page: 2560 ident: CR16 article-title: Physical evaluation of an ultra-high-resolution CT scanner publication-title: Eur Radiol doi: 10.1007/s00330-019-06635-5 – volume: 38 start-page: 394 issue: 5 year: 2020 end-page: 398 ident: CR12 article-title: Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia publication-title: Jpn J Radiol doi: 10.1007/s11604-020-00956-y – volume: 248 start-page: 1036 issue: 3 year: 2008 end-page: 1041 ident: CR32 article-title: Pulmonary emphysema: size distribution of emphysematous spaces on multidetector CT images--comparison with macroscopic and microscopic morphometry publication-title: Radiology doi: 10.1148/radiol.2483071434 – volume: 25 start-page: 869 issue: 7 year: 2018 end-page: 876 ident: CR2 article-title: Effect of matrix size on the image quality of ultra-high-resolution CT of the lung: comparison of 512 512, 1024 1024, and 2048 2048 publication-title: Acad Radiol doi: 10.1016/j.acra.2017.11.017 – volume: 30 start-page: 3324 issue: 6 year: 2020 end-page: 3333 ident: CR11 article-title: Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT publication-title: Eur Radiol doi: 10.1007/s00330-020-06704-0 – volume: 70 start-page: 102 year: 2020 end-page: 108 ident: CR25 article-title: Effect of scan mode and focal spot size in airway dimension measurements for ultra-high-resolution computed tomography of chronic obstructive pulmonary disease: a COPDGene phantom study publication-title: Phys Med doi: 10.1016/j.ejmp.2019.12.025 – ident: CR26 – volume: 346 start-page: 1085 issue: 8982 year: 1995 end-page: 1087 ident: CR28 article-title: Comparing methods of measurement: why plotting difference against standard method is misleading publication-title: Lancet. doi: 10.1016/S0140-6736(95)91748-9 – volume: 47 start-page: 488 issue: 2 year: 2020 end-page: 497 ident: CR15 article-title: Technical Note: Performance comparison of ultra-high-resolution scan modes of two clinical computed tomography systems publication-title: Med Phys doi: 10.1002/mp.13949 – volume: 39 start-page: 186 issue: 2 year: 2021 end-page: 197 ident: CR21 article-title: Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions publication-title: Jpn J Radiol doi: 10.1007/s11604-020-01045-w – volume: 82 start-page: 1844 issue: 7 year: 2018 ident: 8983_CR5 publication-title: Circ J doi: 10.1253/circj.CJ-17-1281 – volume: 47 start-page: 488 issue: 2 year: 2020 ident: 8983_CR15 publication-title: Med Phys doi: 10.1002/mp.13949 – volume: 30 start-page: 2552 issue: 5 year: 2020 ident: 8983_CR16 publication-title: Eur Radiol doi: 10.1007/s00330-019-06635-5 – volume: 27 start-page: 82 issue: 1 year: 2020 ident: 8983_CR20 publication-title: Acad Radiol doi: 10.1016/j.acra.2019.09.008 – ident: 8983_CR30 – volume: 29 start-page: 5322 issue: 10 year: 2019 ident: 8983_CR18 publication-title: Eur Radiol doi: 10.1007/s00330-019-06183-y – ident: 8983_CR26 – volume: 56 start-page: 489 issue: 6 year: 2018 ident: 8983_CR7 publication-title: Respir Investig doi: 10.1016/j.resinv.2018.07.008 – volume: 248 start-page: 1036 issue: 3 year: 2008 ident: 8983_CR32 publication-title: Radiology doi: 10.1148/radiol.2483071434 – volume: 15 start-page: 52 issue: 1 year: 2014 ident: 8983_CR33 publication-title: Respir Res doi: 10.1186/1465-9921-15-52 – volume: 37 start-page: 73 issue: 1 year: 2019 ident: 8983_CR17 publication-title: Jpn J Radiol doi: 10.1007/s11604-018-0796-2 – volume: 60 start-page: 109 issue: 1 year: 2018 ident: 8983_CR4 publication-title: Neuroradiology. doi: 10.1007/s00234-017-1927-7 – volume: 128 year: 2020 ident: 8983_CR13 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2020.109033 – volume: 1 start-page: 307 issue: 8476 year: 1986 ident: 8983_CR27 publication-title: Lancet. doi: 10.1016/S0140-6736(86)90837-8 – volume: 37 start-page: 283 issue: 4 year: 2019 ident: 8983_CR8 publication-title: Jpn J Radiol doi: 10.1007/s11604-019-00816-4 – volume: 44 start-page: 974 issue: 3 year: 2017 ident: 8983_CR23 publication-title: Med Phys doi: 10.1002/mp.12087 – volume: 29 start-page: 6163 issue: 11 year: 2019 ident: 8983_CR19 publication-title: Eur Radiol doi: 10.1007/s00330-019-06170-3 – volume: 39 start-page: 186 issue: 2 year: 2021 ident: 8983_CR21 publication-title: Jpn J Radiol doi: 10.1007/s11604-020-01045-w – volume: 54 start-page: 1802214 issue: 4 year: 2019 ident: 8983_CR35 publication-title: Eur Respir J doi: 10.1183/13993003.02214-2018 – ident: 8983_CR31 – volume: 28 start-page: 5060 issue: 12 year: 2018 ident: 8983_CR6 publication-title: Eur Radiol doi: 10.1007/s00330-018-5491-2 – volume: 38 start-page: 953 issue: 10 year: 2020 ident: 8983_CR14 publication-title: Jpn J Radiol doi: 10.1007/s11604-020-01000-9 – volume: 101 start-page: 30 year: 2018 ident: 8983_CR3 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2018.01.030 – volume: 41 start-page: 219 issue: 2 year: 2020 ident: 8983_CR10 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A6377 – volume: 39 start-page: 5757 issue: 9 year: 2021 ident: 8983_CR22 publication-title: Med Phys doi: 10.1118/1.4747342 – volume: 70 start-page: 102 year: 2020 ident: 8983_CR25 publication-title: Phys Med doi: 10.1016/j.ejmp.2019.12.025 – volume: 10 issue: 9 year: 2015 ident: 8983_CR1 publication-title: PLoS One doi: 10.1371/journal.pone.0137165 – volume: 25 start-page: 869 issue: 7 year: 2018 ident: 8983_CR2 publication-title: Acad Radiol doi: 10.1016/j.acra.2017.11.017 – volume: 37 start-page: 399 issue: 5 year: 2019 ident: 8983_CR24 publication-title: Jpn J Radiol doi: 10.1007/s11604-019-00823-5 – volume: 346 start-page: 1085 issue: 8982 year: 1995 ident: 8983_CR28 publication-title: Lancet. doi: 10.1016/S0140-6736(95)91748-9 – volume: 15 start-page: 718 issue: 6 year: 2018 ident: 8983_CR34 publication-title: Ann Am Thorac Soc doi: 10.1513/AnnalsATS.201710-820OC – volume: 38 start-page: 394 issue: 5 year: 2020 ident: 8983_CR12 publication-title: Jpn J Radiol doi: 10.1007/s11604-020-00956-y – volume: 30 start-page: 3324 issue: 6 year: 2020 ident: 8983_CR11 publication-title: Eur Radiol doi: 10.1007/s00330-020-06704-0 – volume: 120 year: 2019 ident: 8983_CR9 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2019.108687 – volume: 63 start-page: 1653 issue: 10 year: 2017 ident: 8983_CR29 publication-title: Clin Chem doi: 10.1373/clinchem.2016.268870 |
| SSID | ssj0009147 |
| Score | 2.4273815 |
| Snippet | Objective
Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has... Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become... ObjectiveUltra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has... |
| SourceID | proquest pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 368 |
| SubjectTerms | Algorithms Chest Computed tomography Deep Learning Diagnostic Radiology Diameters Evaluation High resolution Humans Image reconstruction Imaging Internal Medicine Interventional Radiology Iterative methods Lung - diagnostic imaging Lungs Medical diagnosis Medicine Medicine & Public Health Neuroradiology Phantoms, Imaging Radiation Dosage Radiographic Image Interpretation, Computer-Assisted - methods Radiology Respiratory tract Thickness measurement Thorax Tomography, X-Ray Computed - methods Ultrasound |
| SummonAdditionalLinks | – databaseName: Proquest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELbKVkJcEG9SChokbqxFnNhxckCoXbUqSF0BaqXeIr_SrrRklyariv_OgbHzWEFFz_ErmrFnxjP-PkLe2UrG1jBOc2s15bmOKYbNikqmjU2ES3mQ9Ok8OznnXy7ExQ6ZD29hfFnlcCaGg9qujL8j_5DIQNuN1vbT-if1rFE-uzpQaKieWsF-DBBj98hu4pGxJmT38Gj-9fsWhpcFyjEM43Mqi4L3z2jCYzpPaxZTX90e50WeUva3qbrlf97KnQaTdPyIPOx9STjohP-Y7Lj6Cbl_2mfLn5Lfs5FkEFYVLHFfw-wMOhIQULUFtbi-Ub_Aeoh_f20GW_BvMGhGQ-UsxtK--2bZXivq4Y0phui9xuJ4U_C185cwUK200KC4wFPsNGGSEHOPOLXQcVY3sKjNcmNDT-fW0NNXXP7TfAr-phiwS-P8KkIBJLWuDdkG_zuLGhR8-3x4AOsrz4j8AwJk7jNyfnx0NjuhPdsDNakULc2E0qzivLAYKFvBFEPzneEZ6DCAdyyTNuE5M1K7yhibaSWtUbrwaD2aceXS52RSr2r3kkAR60pVWlaJMVwYVggjuZAucaIQVaEiwgbBlqaHQveMHMtyBHEOylCiMpRBGUoWkfdjn3UHBHJn6_1BX8r-UGjKrQpH5O34Gbezz9Go2q022CZD_w2jUJ5E5EWnZ-N0KTqLjDMZkemgeNvB_7-WvbvX8oo8SNBx666V9skE5eteo6PV6jf97vkDNIgp5A priority: 102 providerName: ProQuest |
| Title | Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study |
| URI | https://link.springer.com/article/10.1007/s00330-022-08983-1 https://www.ncbi.nlm.nih.gov/pubmed/35841417 https://www.proquest.com/docview/2754651273 https://www.proquest.com/docview/2691049942 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1432-1084 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: AFBBN dateStart: 19970101 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1432-1084 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: 7X7 dateStart: 20210101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1432-1084 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: BENPR dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1432-1084 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: 8FG dateStart: 19970101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1432-1084 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1432-1084 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lj9MwELagKyEuvFkKSzVI3KhXderEybGt2l1AWwHaSuUU-ZWloqTVJhWC386BsfOo2AWkvbSHeBJHMxnPeMbfR8hrk4mB0YzT2BhFeawGFNNmSQVT2gShHXKv6bN5dLrg75bhsj4UVjTd7k1J0nvq9rCbox0bUNd9PoiTeEgx5znweFsdcjA6-fx-ugfbZVzUB2T-LvnnInQtsrxWFfWLzew-WTTTrHpMvh7vSnWsf15BcLzpezwg9-roE0aVuTwkt2z-iNw5q-vrj8mvSUtLCJsM1ugJYHIOFW0IyNyAXF1-lz_AOFIAt9EGe7hw0Ljw-l5bzL6d-G5dXkrqAJEpJvW1jeP9-uC67S-gIWcpoUAFgyPlKfxDfJbeIttCxXJdwCrX653xktZuoSa8uLgyvA9ubxlQpLBuFr5lkhpb-vqEe51VDhI-vh2PYPvFcSh_Aw-y-4QsZtPzySmt-SGoHoqwpFEoFcs4Twym1iZkkuGCH6HXtJjyWxYJE_CYaaFsprWJlBRGS5U4fB_FuLTDp6STb3L7jEAyUJnMlMgCrXmoWRJqwUNhAxsmYZbILmGNwaS6Bk93HB7rtIV99mpNUa2pV2vKuuRNK7OtoEP-O_qoscO0diNFGgjPVY8hZpe8ai-jA3BVHZnbzQ7HRBjxYd7Kgy45rOy3fdwQw0vGmeiSfmOL-5v_ey7Pbzb8BbkbYOhXbUwdkQ7q277EUK1UPXJbLAX-xrOTHn6ls_F43qu_VvwfT-cfPuHVRTD6Da8-QRc |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELZKKwEXxJtAgUGCE7FY73rX8aFCbWiV0CYClEq9bb22t0QKm5BNVfXP8cs4MPY-IqjorefYXkfz8Ixn_H2EvDW5CIxmnPaMySjvZQHFtFlRwTJtwthG3Et6NE4Gx_zzSXyyQX41b2FcW2XjE72jNnPt7sg_hMLTduNp-3HxkzrWKFddbSg0VE2tYHY8xFj9sOPQXl5gClfuDD-hvN-F4cH-pD-gNcsA1ZGIVzSJVcZyzqXBBM3ETDE8NhK0PYuJo2WJMCHvMS0ym2ttkkwJo1UmHUpMxriyEa57i2zxiEtM_rb29sdfvq1hf5mnOAskuhUhJa-f7fjHe45GLaCumz7oyV5E2d9H45V490qt1h-BB_fJvTp2hd1K2R6QDVs8JLdHdXX-Efndb0kNYZ7DDP0I9CdQkY6AKgyo6fJCXYJxlALumg7WYOOg8dj2nbqYu7vp57PVUlEHp0yXtrEQXK8Lrlf_DBpqlxWUqB7gKH1K_xGf47e4uFBxZJcwLfTs3PiZ1i6gpss4-2d4F9zNNOCU0rpd-IZLauzKVzfc35kWoODrcG8XFt8dA_MP8BC9j8nxjcj9Cdks5oV9RkAGWa7yTOSh1jzWTMZa8FjY0MYyzqXqENYINtU19LpjAJmlLWi0V4YUlSH1ypCyDnnfzllUwCPXjt5u9CWtnVCZrk2mQ960P6P7cDUhVdj5OY5JMF7ErJeHHfK00rP2cxEGp4wz0SHdRvHWi_9_L8-v38trcmcwGR2lR8Px4QtyN8SgsbrS2iabKGv7EoO8VfaqtiQgpzdtvH8Ap1FoPg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6VIlVcEO8GCgwSnMiqXnvtzR4QKilRQ2kFUivlZvblEik4IU5V9a_x2zgwu35EUNFbz96XNe-d2fkIeW0LEVnDOB1Yqykf6Ihi2KyoYNrYOHUJD5Q-Os4OTvmnSTrZIL_atzC-rLLViUFR27nxd-S7sQiw3Whtd4umLOLL_uj94if1CFI-09rCadQscuguLzB8q96N95HWb-J49PFkeEAbhAFqEpGuaJYqzQrOpcXgzKZMMTQZGcqdw6DRsUzYmA-YEdoVxthMK2GN0tJ3iNGMK5fgurfIbZEk0pcTiolYN_xlAdwskqhQhJS8ebATnu15ALWI-jr6aCAHCWV_G8Urnu6VLG0wfqN75G7jtcJezWb3yYYrH5CtoyYv_5D8HnZwhjAvYIYaBIYnUMONgCotqOnyQl2C9WAC_oIO1m3GwaDBDjW6GLX76eez1VJR30iZLl0rG7heH3yV_hm0oC4rqJAxwIP5VGGTEN13HXGhRseuYFqa2bkNM51bQAOUcfbP8D74O2nAKZXzpwilltS6Vchr-N-ZlqDg6_jDHiy-e-zlHxCa8z4ipzdC9cdks5yXbpuAjHShCi2K2BieGiZTI3gqXOxSmRZS9QhrCZubpum6x_6Y5V276MAMOTJDHpghZz3ytpuzqFuOXDt6p-WXvFE_Vb4Wlh551X1GxeGzQap083Mck6GniPEuj3vkSc1n3XYJuqWMM9Ej_Zbx1ov__yxPrz_LS7KFIpt_Hh8fPiN3YvQW67usHbKJpHbP0btb6RdBjIB8u2m5_QPZuGXY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparison+of+lung+CT+number+and+airway+dimension+evaluation+capabilities+of+ultra-high-resolution+CT%2C+using+different+scan+modes+and+reconstruction+methods+including+deep+learning+reconstruction%2C+with+those+of+multi-detector+CT+in+a+QIBA+phantom+study&rft.jtitle=European+radiology&rft.au=Ohno%2C+Yoshiharu&rft.au=Akino%2C+Naruomi&rft.au=Fujisawa%2C+Yasuko&rft.au=Kimata%2C+Hirona&rft.date=2023-01-01&rft.eissn=1432-1084&rft.volume=33&rft.issue=1&rft.spage=368&rft_id=info:doi/10.1007%2Fs00330-022-08983-1&rft_id=info%3Apmid%2F35841417&rft.externalDocID=35841417 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1432-1084&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1432-1084&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1432-1084&client=summon |