Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13244-024-01888-1
Objectives To investigate the image quality and diagnostic performance with ultra-low dose dual-layer detector spectral CT (DLSCT) by various reconstruction techniques for evaluation of pulmonary nodules. Materials and methods Between April 2023 and December 2023, patients with suspected pulmonary nodules were prospectively enrolled and underwent regular-dose chest CT (RDCT; 120 kVp/automatic tube current) and ultra-low dose CT (ULDCT; 100 kVp/10 mAs) on a DLSCT scanner. ULDCT was reconstructed with hybrid iterative reconstruction (HIR), electron density map (EDM), and virtual monoenergetic images at 40 keV and 70 keV. Quantitative and qualitative image analysis, nodule detectability, and Lung-RADS evaluation were compared using repeated one-way analysis of variance, Friedman test, and weighted kappa coefficient. Results A total of 249 participants (mean age ± standard deviation, 50.0 years ± 12.9; 126 male) with 637 lung nodules were included. ULDCT resulted in a significantly lower mean radiation dose than RDCT (0.3 mSv ± 0.0 vs. 3.6 mSv ± 0.8; p < 0.001). Compared with RDCT, ULDCT EDM showed significantly higher signal-noise-ratio (44.0 ± 77.2 vs. 4.6 ± 6.6; p < 0.001) and contrast-noise-ratio (26.7 ± 17.7 vs. 5.0 ± 4.4; p < 0.001) with qualitative scores ranked higher or equal to the average. Using the regular-dose images as a reference, ULDCT EDM images had a satisfactory nodule detection rate (84.6%) and good inter-observer agreements compared with RDCT (κw > 0.60). Conclusion Ultra-low dose dual-layer detector CT with 91.2% radiation dose reduction achieves sufficient image quality and diagnostic performance of pulmonary nodules. Critical relevance statement Dual-layer detector spectral CT enables substantial radiation dose reduction without impairing image quality for the follow-up of pulmonary nodules or lung cancer screening. Key Points Radiation dose is a major concern for patients requiring pulmonary nodules CT screening. Ultra-low dose dual-layer detector spectral CT with 91.2% dose reduction demonstrated satisfactory performance. Dual-layer detector spectral CT has the potential for lung cancer screening and management. Graphical Abstract
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13244-024-01888-1
- https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1
- OA Status
- gold
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406233548
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406233548Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s13244-024-01888-1Digital Object Identifier
- Title
-
Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-10Full publication date if available
- Authors
-
Li Ding, Mingwang Chen, Xiaomei Li, Yuting Wu, Jingxu Li, Shuting Deng, Yikai Xu, Chen Zhao, Chenggong YanList of authors in order
- Landing page
-
https://doi.org/10.1186/s13244-024-01888-1Publisher landing page
- PDF URL
-
https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1Direct OA link when available
- Concepts
-
Medicine, Nuclear medicine, Image quality, Image noise, Nodule (geology), Neuroradiology, Effective dose (radiation), Radiation dose, Interventional radiology, Dual layer, Iterative reconstruction, Radiology, Image (mathematics), Organic chemistry, Paleontology, Computer science, Chemistry, Neurology, Artificial intelligence, Biology, Psychiatry, Layer (electronics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406233548 |
|---|---|
| doi | https://doi.org/10.1186/s13244-024-01888-1 |
| ids.doi | https://doi.org/10.1186/s13244-024-01888-1 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39792229 |
| ids.openalex | https://openalex.org/W4406233548 |
| fwci | 3.98572362 |
| type | article |
| title | Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance |
| biblio.issue | 1 |
| biblio.volume | 16 |
| biblio.last_page | 11 |
| biblio.first_page | 11 |
| grants[0].funder | https://openalex.org/F4320321001 |
| grants[0].award_id | 82271987 |
| grants[0].funder_display_name | National Natural Science Foundation of China |
| topics[0].id | https://openalex.org/T12386 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Advanced X-ray and CT Imaging |
| topics[1].id | https://openalex.org/T10844 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9994000196456909 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Radiation Dose and Imaging |
| topics[2].id | https://openalex.org/T10522 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9965000152587891 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | Medical Imaging Techniques and Applications |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 1690 |
| apc_list.currency | GBP |
| apc_list.value_usd | 2072 |
| apc_paid.value | 1690 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 2072 |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7839354872703552 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C2989005 |
| concepts[1].level | 1 |
| concepts[1].score | 0.678831934928894 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q214963 |
| concepts[1].display_name | Nuclear medicine |
| concepts[2].id | https://openalex.org/C55020928 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5974627137184143 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3813865 |
| concepts[2].display_name | Image quality |
| concepts[3].id | https://openalex.org/C35772409 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5903657078742981 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1323086 |
| concepts[3].display_name | Image noise |
| concepts[4].id | https://openalex.org/C2776731575 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5423792600631714 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2916245 |
| concepts[4].display_name | Nodule (geology) |
| concepts[5].id | https://openalex.org/C2779889316 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5134369134902954 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q642836 |
| concepts[5].display_name | Neuroradiology |
| concepts[6].id | https://openalex.org/C149857219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4792911410331726 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5239054 |
| concepts[6].display_name | Effective dose (radiation) |
| concepts[7].id | https://openalex.org/C2987700449 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46141278743743896 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q186161 |
| concepts[7].display_name | Radiation dose |
| concepts[8].id | https://openalex.org/C513090587 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4339950680732727 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q426449 |
| concepts[8].display_name | Interventional radiology |
| concepts[9].id | https://openalex.org/C2993148961 |
| concepts[9].level | 3 |
| concepts[9].score | 0.42767611145973206 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q5294 |
| concepts[9].display_name | Dual layer |
| concepts[10].id | https://openalex.org/C141379421 |
| concepts[10].level | 2 |
| concepts[10].score | 0.42424705624580383 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q6094427 |
| concepts[10].display_name | Iterative reconstruction |
| concepts[11].id | https://openalex.org/C126838900 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3977741599082947 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[11].display_name | Radiology |
| concepts[12].id | https://openalex.org/C115961682 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0788104236125946 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[12].display_name | Image (mathematics) |
| concepts[13].id | https://openalex.org/C178790620 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11351 |
| concepts[13].display_name | Organic chemistry |
| concepts[14].id | https://openalex.org/C151730666 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[14].display_name | Paleontology |
| concepts[15].id | https://openalex.org/C41008148 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[15].display_name | Computer science |
| concepts[16].id | https://openalex.org/C185592680 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[16].display_name | Chemistry |
| concepts[17].id | https://openalex.org/C16568411 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q83042 |
| concepts[17].display_name | Neurology |
| concepts[18].id | https://openalex.org/C154945302 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[18].display_name | Artificial intelligence |
| concepts[19].id | https://openalex.org/C86803240 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[19].display_name | Biology |
| concepts[20].id | https://openalex.org/C118552586 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q7867 |
| concepts[20].display_name | Psychiatry |
| concepts[21].id | https://openalex.org/C2779227376 |
| concepts[21].level | 2 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q6505497 |
| concepts[21].display_name | Layer (electronics) |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.7839354872703552 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/nuclear-medicine |
| keywords[1].score | 0.678831934928894 |
| keywords[1].display_name | Nuclear medicine |
| keywords[2].id | https://openalex.org/keywords/image-quality |
| keywords[2].score | 0.5974627137184143 |
| keywords[2].display_name | Image quality |
| keywords[3].id | https://openalex.org/keywords/image-noise |
| keywords[3].score | 0.5903657078742981 |
| keywords[3].display_name | Image noise |
| keywords[4].id | https://openalex.org/keywords/nodule |
| keywords[4].score | 0.5423792600631714 |
| keywords[4].display_name | Nodule (geology) |
| keywords[5].id | https://openalex.org/keywords/neuroradiology |
| keywords[5].score | 0.5134369134902954 |
| keywords[5].display_name | Neuroradiology |
| keywords[6].id | https://openalex.org/keywords/effective-dose |
| keywords[6].score | 0.4792911410331726 |
| keywords[6].display_name | Effective dose (radiation) |
| keywords[7].id | https://openalex.org/keywords/radiation-dose |
| keywords[7].score | 0.46141278743743896 |
| keywords[7].display_name | Radiation dose |
| keywords[8].id | https://openalex.org/keywords/interventional-radiology |
| keywords[8].score | 0.4339950680732727 |
| keywords[8].display_name | Interventional radiology |
| keywords[9].id | https://openalex.org/keywords/dual-layer |
| keywords[9].score | 0.42767611145973206 |
| keywords[9].display_name | Dual layer |
| keywords[10].id | https://openalex.org/keywords/iterative-reconstruction |
| keywords[10].score | 0.42424705624580383 |
| keywords[10].display_name | Iterative reconstruction |
| keywords[11].id | https://openalex.org/keywords/radiology |
| keywords[11].score | 0.3977741599082947 |
| keywords[11].display_name | Radiology |
| keywords[12].id | https://openalex.org/keywords/image |
| keywords[12].score | 0.0788104236125946 |
| keywords[12].display_name | Image (mathematics) |
| language | en |
| locations[0].id | doi:10.1186/s13244-024-01888-1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S44632665 |
| locations[0].source.issn | 1869-4101 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1869-4101 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Insights into Imaging |
| locations[0].source.host_organization | https://openalex.org/P4310319965 |
| locations[0].source.host_organization_name | Springer Nature |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319965 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Insights into Imaging |
| locations[0].landing_page_url | https://doi.org/10.1186/s13244-024-01888-1 |
| locations[1].id | pmid:39792229 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Insights into imaging |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39792229 |
| locations[2].id | pmh:oai:doaj.org/article:31f9f1c3f5df4022a4660a25c32e3463 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].source.host_organization_lineage | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Insights into Imaging, Vol 16, Iss 1, Pp 1-12 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/31f9f1c3f5df4022a4660a25c32e3463 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11723867 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Insights Imaging |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11723867 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5001527120 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Li Ding |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[0].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Nanfang Hospital |
| authorships[0].institutions[1].id | https://openalex.org/I58200834 |
| authorships[0].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Southern Medical University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Li Ding |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[1].author.id | https://openalex.org/A5007412602 |
| authorships[1].author.orcid | https://orcid.org/0009-0009-4043-5379 |
| authorships[1].author.display_name | Mingwang Chen |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[1].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Nanfang Hospital |
| authorships[1].institutions[1].id | https://openalex.org/I58200834 |
| authorships[1].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Southern Medical University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Mingwang Chen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[2].author.id | https://openalex.org/A5100413467 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3221-5888 |
| authorships[2].author.display_name | Xiaomei Li |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[2].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Nanfang Hospital |
| authorships[2].institutions[1].id | https://openalex.org/I58200834 |
| authorships[2].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Southern Medical University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Xiaomei Li |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[3].author.id | https://openalex.org/A5100651958 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6278-5366 |
| authorships[3].author.display_name | Yuting Wu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[3].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Nanfang Hospital |
| authorships[3].institutions[1].id | https://openalex.org/I58200834 |
| authorships[3].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Southern Medical University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yuting Wu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[4].author.id | https://openalex.org/A5103147498 |
| authorships[4].author.orcid | https://orcid.org/0009-0006-1707-4091 |
| authorships[4].author.display_name | Jingxu Li |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[4].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Nanfang Hospital |
| authorships[4].institutions[1].id | https://openalex.org/I58200834 |
| authorships[4].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Southern Medical University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Jingxu Li |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[5].author.id | https://openalex.org/A5111007892 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Shuting Deng |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[5].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[5].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[5].institutions[0].type | healthcare |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Nanfang Hospital |
| authorships[5].institutions[1].id | https://openalex.org/I58200834 |
| authorships[5].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | Southern Medical University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Shuting Deng |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[6].author.id | https://openalex.org/A5101891724 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-1607-5077 |
| authorships[6].author.display_name | Yikai Xu |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[6].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[6].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Nanfang Hospital |
| authorships[6].institutions[1].id | https://openalex.org/I58200834 |
| authorships[6].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[6].institutions[1].type | education |
| authorships[6].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[6].institutions[1].country_code | CN |
| authorships[6].institutions[1].display_name | Southern Medical University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Yikai Xu |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[7].author.id | https://openalex.org/A5102958055 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0840-8022 |
| authorships[7].author.display_name | Chen Zhao |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[7].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[7].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Nanfang Hospital |
| authorships[7].institutions[1].id | https://openalex.org/I58200834 |
| authorships[7].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[7].institutions[1].type | education |
| authorships[7].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[7].institutions[1].country_code | CN |
| authorships[7].institutions[1].display_name | Southern Medical University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Zhao Chen |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[8].author.id | https://openalex.org/A5023469305 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-2968-4595 |
| authorships[8].author.display_name | Chenggong Yan |
| authorships[8].countries | CN |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210103346, https://openalex.org/I58200834 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| authorships[8].institutions[0].id | https://openalex.org/I4210103346 |
| authorships[8].institutions[0].ror | https://ror.org/01eq10738 |
| authorships[8].institutions[0].type | healthcare |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210103346 |
| authorships[8].institutions[0].country_code | CN |
| authorships[8].institutions[0].display_name | Nanfang Hospital |
| authorships[8].institutions[1].id | https://openalex.org/I58200834 |
| authorships[8].institutions[1].ror | https://ror.org/01vjw4z39 |
| authorships[8].institutions[1].type | education |
| authorships[8].institutions[1].lineage | https://openalex.org/I58200834 |
| authorships[8].institutions[1].country_code | CN |
| authorships[8].institutions[1].display_name | Southern Medical University |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Chenggong Yan |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12386 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Advanced X-ray and CT Imaging |
| related_works | https://openalex.org/W2060534205, https://openalex.org/W2375071291, https://openalex.org/W2393944063, https://openalex.org/W4309900034, https://openalex.org/W2013245909, https://openalex.org/W2255980157, https://openalex.org/W2330000517, https://openalex.org/W4283689329, https://openalex.org/W2571873071, https://openalex.org/W3032084249 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1186/s13244-024-01888-1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S44632665 |
| best_oa_location.source.issn | 1869-4101 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1869-4101 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Insights into Imaging |
| best_oa_location.source.host_organization | https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_name | Springer Nature |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Insights into Imaging |
| best_oa_location.landing_page_url | https://doi.org/10.1186/s13244-024-01888-1 |
| primary_location.id | doi:10.1186/s13244-024-01888-1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S44632665 |
| primary_location.source.issn | 1869-4101 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1869-4101 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Insights into Imaging |
| primary_location.source.host_organization | https://openalex.org/P4310319965 |
| primary_location.source.host_organization_name | Springer Nature |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-024-01888-1 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Insights into Imaging |
| primary_location.landing_page_url | https://doi.org/10.1186/s13244-024-01888-1 |
| publication_date | 2025-01-10 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3125542419, https://openalex.org/W130099911, https://openalex.org/W3003415550, https://openalex.org/W2948691893, https://openalex.org/W1213336605, https://openalex.org/W4388523332, https://openalex.org/W2945958333, https://openalex.org/W2041966681, https://openalex.org/W3100996698, https://openalex.org/W2802881871, https://openalex.org/W2982444987, https://openalex.org/W2898404836, https://openalex.org/W2510844683, https://openalex.org/W2587216188, https://openalex.org/W2604225475, https://openalex.org/W2796805542, https://openalex.org/W2935069779, https://openalex.org/W2763781088, https://openalex.org/W2809482765, https://openalex.org/W2946480236, https://openalex.org/W2972419341, https://openalex.org/W2903196570, https://openalex.org/W2767819642, https://openalex.org/W2934480076, https://openalex.org/W3080513822, https://openalex.org/W4220804759, https://openalex.org/W2141834520, https://openalex.org/W2314771911, https://openalex.org/W2034903235, https://openalex.org/W4318928758, https://openalex.org/W2972256219, https://openalex.org/W4385827600, https://openalex.org/W4401305329, https://openalex.org/W4401153800, https://openalex.org/W4399710277, https://openalex.org/W1566739424, https://openalex.org/W2765880305, https://openalex.org/W2898907020, https://openalex.org/W4385715956, https://openalex.org/W2594318146 |
| referenced_works_count | 40 |
| abstract_inverted_index.A | 113 |
| abstract_inverted_index.a | 63, 138, 204, 210, 278 |
| abstract_inverted_index.p | 155, 174, 186 |
| abstract_inverted_index.40 | 83 |
| abstract_inverted_index.70 | 86 |
| abstract_inverted_index.CT | 16, 48, 57, 231, 253, 286, 293, 304 |
| abstract_inverted_index.To | 2 |
| abstract_inverted_index.as | 203 |
| abstract_inverted_index.at | 82 |
| abstract_inverted_index.by | 18 |
| abstract_inverted_index.in | 137 |
| abstract_inverted_index.is | 277 |
| abstract_inverted_index.of | 24, 104, 115, 244, 266 |
| abstract_inverted_index.on | 62 |
| abstract_inverted_index.or | 194, 269 |
| abstract_inverted_index.to | 196 |
| abstract_inverted_index.± | 120, 125, 148, 153, 168, 172, 180, 184 |
| abstract_inverted_index.0.0 | 149 |
| abstract_inverted_index.100 | 59 |
| abstract_inverted_index.120 | 50 |
| abstract_inverted_index.126 | 127 |
| abstract_inverted_index.249 | 116 |
| abstract_inverted_index.3.6 | 151 |
| abstract_inverted_index.4.6 | 171 |
| abstract_inverted_index.5.0 | 183 |
| abstract_inverted_index.637 | 130 |
| abstract_inverted_index.EDM | 162, 207 |
| abstract_inverted_index.Key | 273 |
| abstract_inverted_index.age | 119 |
| abstract_inverted_index.and | 7, 28, 33, 44, 54, 78, 85, 89, 95, 108, 177, 216, 241, 312 |
| abstract_inverted_index.for | 22, 263, 281, 308 |
| abstract_inverted_index.had | 209 |
| abstract_inverted_index.has | 305 |
| abstract_inverted_index.keV | 84 |
| abstract_inverted_index.mSv | 147, 152 |
| abstract_inverted_index.map | 76 |
| abstract_inverted_index.the | 4, 197, 200, 264, 306 |
| abstract_inverted_index.vs. | 150, 170, 182 |
| abstract_inverted_index.was | 67 |
| abstract_inverted_index.> | 224 |
| abstract_inverted_index.< | 156, 175, 187 |
| abstract_inverted_index.(0.3 | 146 |
| abstract_inverted_index.(κw | 223 |
| abstract_inverted_index.0.8; | 154 |
| abstract_inverted_index.17.7 | 181 |
| abstract_inverted_index.2023 | 32 |
| abstract_inverted_index.4.4; | 185 |
| abstract_inverted_index.50.0 | 123 |
| abstract_inverted_index.6.6; | 173 |
| abstract_inverted_index.77.2 | 169 |
| abstract_inverted_index.RDCT | 145, 222 |
| abstract_inverted_index.dose | 12, 56, 143, 228, 235, 257, 276, 289, 296 |
| abstract_inverted_index.good | 217 |
| abstract_inverted_index.keV. | 87 |
| abstract_inverted_index.lung | 131, 270, 309 |
| abstract_inverted_index.mAs) | 61 |
| abstract_inverted_index.mean | 141 |
| abstract_inverted_index.rate | 214 |
| abstract_inverted_index.than | 144 |
| abstract_inverted_index.tube | 52 |
| abstract_inverted_index.were | 41, 98, 133 |
| abstract_inverted_index.with | 10, 37, 69, 129, 159, 189, 221, 232, 294 |
| abstract_inverted_index.(26.7 | 179 |
| abstract_inverted_index.(44.0 | 167 |
| abstract_inverted_index.(mean | 118 |
| abstract_inverted_index.12.9; | 126 |
| abstract_inverted_index.2023, | 35 |
| abstract_inverted_index.91.2% | 233, 295 |
| abstract_inverted_index.April | 31 |
| abstract_inverted_index.DLSCT | 64 |
| abstract_inverted_index.RDCT, | 160 |
| abstract_inverted_index.ULDCT | 66, 135, 161, 206 |
| abstract_inverted_index.Using | 199 |
| abstract_inverted_index.chest | 47 |
| abstract_inverted_index.equal | 195 |
| abstract_inverted_index.image | 5, 91, 239, 261 |
| abstract_inverted_index.kappa | 110 |
| abstract_inverted_index.lower | 140 |
| abstract_inverted_index.major | 279 |
| abstract_inverted_index.male) | 128 |
| abstract_inverted_index.test, | 107 |
| abstract_inverted_index.total | 114 |
| abstract_inverted_index.using | 100 |
| abstract_inverted_index.years | 124 |
| abstract_inverted_index.(EDM), | 77 |
| abstract_inverted_index.(HIR), | 73 |
| abstract_inverted_index.(RDCT; | 49 |
| abstract_inverted_index.0.001) | 176, 188 |
| abstract_inverted_index.0.60). | 225 |
| abstract_inverted_index.Points | 274 |
| abstract_inverted_index.cancer | 271, 310 |
| abstract_inverted_index.higher | 165, 193 |
| abstract_inverted_index.hybrid | 70 |
| abstract_inverted_index.images | 81, 202, 208 |
| abstract_inverted_index.kVp/10 | 60 |
| abstract_inverted_index.nodule | 93, 212 |
| abstract_inverted_index.ranked | 192 |
| abstract_inverted_index.scores | 191 |
| abstract_inverted_index.showed | 163 |
| abstract_inverted_index.(84.6%) | 215 |
| abstract_inverted_index.(DLSCT) | 17 |
| abstract_inverted_index.(ULDCT; | 58 |
| abstract_inverted_index.0.001). | 157 |
| abstract_inverted_index.Between | 30 |
| abstract_inverted_index.Results | 112 |
| abstract_inverted_index.concern | 280 |
| abstract_inverted_index.density | 75 |
| abstract_inverted_index.enables | 254 |
| abstract_inverted_index.methods | 29 |
| abstract_inverted_index.nodules | 40, 132, 268, 285 |
| abstract_inverted_index.one-way | 102 |
| abstract_inverted_index.quality | 6, 240, 262 |
| abstract_inverted_index.various | 19 |
| abstract_inverted_index.virtual | 79 |
| abstract_inverted_index.without | 259 |
| abstract_inverted_index.Abstract | 0, 315 |
| abstract_inverted_index.Compared | 158 |
| abstract_inverted_index.Critical | 247 |
| abstract_inverted_index.December | 34 |
| abstract_inverted_index.Friedman | 106 |
| abstract_inverted_index.achieves | 237 |
| abstract_inverted_index.analysis | 103 |
| abstract_inverted_index.average. | 198 |
| abstract_inverted_index.compared | 99, 220 |
| abstract_inverted_index.current) | 53 |
| abstract_inverted_index.detector | 14, 230, 251, 291, 302 |
| abstract_inverted_index.electron | 74 |
| abstract_inverted_index.enrolled | 43 |
| abstract_inverted_index.nodules. | 26, 246 |
| abstract_inverted_index.patients | 36, 282 |
| abstract_inverted_index.repeated | 101 |
| abstract_inverted_index.resulted | 136 |
| abstract_inverted_index.scanner. | 65 |
| abstract_inverted_index.spectral | 15, 252, 292, 303 |
| abstract_inverted_index.standard | 121 |
| abstract_inverted_index.weighted | 109 |
| abstract_inverted_index.Graphical | 314 |
| abstract_inverted_index.Lung-RADS | 96 |
| abstract_inverted_index.Materials | 27 |
| abstract_inverted_index.Radiation | 275 |
| abstract_inverted_index.Ultra-low | 227, 288 |
| abstract_inverted_index.analysis, | 92 |
| abstract_inverted_index.detection | 213 |
| abstract_inverted_index.follow-up | 265 |
| abstract_inverted_index.impairing | 260 |
| abstract_inverted_index.included. | 134 |
| abstract_inverted_index.iterative | 71 |
| abstract_inverted_index.potential | 307 |
| abstract_inverted_index.pulmonary | 25, 39, 245, 267, 284 |
| abstract_inverted_index.radiation | 142, 234, 256 |
| abstract_inverted_index.reduction | 236, 258, 297 |
| abstract_inverted_index.relevance | 248 |
| abstract_inverted_index.requiring | 283 |
| abstract_inverted_index.screening | 311 |
| abstract_inverted_index.statement | 249 |
| abstract_inverted_index.suspected | 38 |
| abstract_inverted_index.ultra-low | 11, 55 |
| abstract_inverted_index.underwent | 45 |
| abstract_inverted_index.variance, | 105 |
| abstract_inverted_index.Conclusion | 226 |
| abstract_inverted_index.Dual-layer | 250, 301 |
| abstract_inverted_index.Objectives | 1 |
| abstract_inverted_index.agreements | 219 |
| abstract_inverted_index.deviation, | 122 |
| abstract_inverted_index.diagnostic | 8, 242 |
| abstract_inverted_index.dual-layer | 13, 229, 290 |
| abstract_inverted_index.evaluation | 23, 97 |
| abstract_inverted_index.reference, | 205 |
| abstract_inverted_index.screening. | 272, 287 |
| abstract_inverted_index.sufficient | 238 |
| abstract_inverted_index.techniques | 21 |
| abstract_inverted_index.investigate | 3 |
| abstract_inverted_index.management. | 313 |
| abstract_inverted_index.performance | 9, 243 |
| abstract_inverted_index.qualitative | 90, 190 |
| abstract_inverted_index.substantial | 255 |
| abstract_inverted_index.Quantitative | 88 |
| abstract_inverted_index.coefficient. | 111 |
| abstract_inverted_index.demonstrated | 298 |
| abstract_inverted_index.participants | 117 |
| abstract_inverted_index.performance. | 300 |
| abstract_inverted_index.regular-dose | 46, 201 |
| abstract_inverted_index.satisfactory | 211, 299 |
| abstract_inverted_index.kVp/automatic | 51 |
| abstract_inverted_index.monoenergetic | 80 |
| abstract_inverted_index.prospectively | 42 |
| abstract_inverted_index.reconstructed | 68 |
| abstract_inverted_index.significantly | 139, 164 |
| abstract_inverted_index.detectability, | 94 |
| abstract_inverted_index.inter-observer | 218 |
| abstract_inverted_index.reconstruction | 20, 72 |
| abstract_inverted_index.signal-noise-ratio | 166 |
| abstract_inverted_index.contrast-noise-ratio | 178 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.85270029 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |