{"doi":"10.1016/j.ejrad.2023.110960","title":"Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction","abstract":null,"journal":"European Journal of Radiology","year":2023,"id":642696,"datarank":0.6336381642739579,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.26090216680575784,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.26090216680575784,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":10,"citers_with_citation_signal":8,"citers_with_endowment":8,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1671886,"name":"Yoshifumi Noda","orcid":null,"position":1,"is_corresponding":false},{"id":1671887,"name":"Masashi Asano","orcid":null,"position":2,"is_corresponding":false},{"id":1671888,"name":"Nobuyuki Kawai","orcid":null,"position":3,"is_corresponding":false},{"id":1671889,"name":"Tetsuro Kaga","orcid":null,"position":4,"is_corresponding":false},{"id":1671890,"name":"Yuki Tsuchida","orcid":null,"position":5,"is_corresponding":false},{"id":1671891,"name":"Toshiharu Miyoshi","orcid":null,"position":6,"is_corresponding":false},{"id":781249,"name":"Fuminori Hyodo","orcid":"0000-0002-9266-9937","position":7,"is_corresponding":false},{"id":20752,"name":"Hiroki Kato","orcid":"0000-0002-9571-4895","position":8,"is_corresponding":false},{"id":781258,"name":"Masayuki Matsuo","orcid":"0000-0001-7615-3247","position":9,"is_corresponding":false},{"id":1671885,"name":"Yukiko Takai","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction","abstract":"<h4>Purpose</h4>To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms.<h4>Method</h4>A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test.<h4>Results</h4>The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003).<h4>Conclusion</h4>In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.","is_dataset_classified":null,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37423016","pmcid":null,"openalex_id":"https://openalex.org/W4383102422","authors":[],"funders":[],"total_grants":0,"fwci":0.8786,"citation_percentile":0.67874513,"influential_citations":0,"citation_trend":[{"year":2024,"count":4},{"year":2025,"count":4},{"year":2026,"count":3}],"oa_status":"closed","license":"http://www.elsevier.com/open-access/userlicense/1.0/","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S0720048X23002747?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0720048X23002747?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.ejrad.2023.110960","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/37423016","host_type":"repository"},{"url":"https://gifu-u.repo.nii.ac.jp/records/2000438","host_type":"repository"}],"fields_of_study":["Advanced X-ray and CT Imaging","Pancreatic and Hepatic Oncology Research","Radiation Dose and Imaging","Humans","Retrospective Studies","Deep Learning","Tomography, X-Ray Computed","Pancreatic Neoplasms","Pancreas","Carcinoma, Pancreatic Ductal","Image Processing, Computer-Assisted","Radiographic Image Interpretation, Computer-Assisted","Radiation Dosage","Algorithms"],"mesh_terms":["Deep Learning","Algorithms","Humans","Image Processing, Computer-Assisted","Pancreas","Pancreatic Neoplasms","Radiation Dosage","Radiographic Image Interpretation, Computer-Assisted","Retrospective Studies","Tomography, X-Ray Computed","Carcinoma, Pancreatic Ductal"],"keywords":["Medicine","Visibility","Pancreatic ductal adenocarcinoma","Iterative reconstruction","Image quality","Radiology","Adenocarcinoma","Pancreas","Image (mathematics)","Artificial intelligence","Nuclear medicine","Pancreatic cancer","Internal medicine","Cancer","Optics","Multidetector Computed Tomography","Deep Learning"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-08T02:38:55.977242Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}