Diagnostic and Interventional Imaging最新文献

{"title":"Meandering pulmonary vein mimicking pulmonary arteriovenous malformation.","authors":"Shinji Wada, Masahiro Nitta, Yusuke Kobayashi","doi":"10.1016/j.diii.2026.07.006","DOIUrl":"https://doi.org/10.1016/j.diii.2026.07.006","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148609040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning and multimodal imaging: Toward a more reproducible characterization of lacrimal gland lesions.","authors":"Blanche Bapst, Anne-Laure Gaultier","doi":"10.1016/j.diii.2026.07.005","DOIUrl":"https://doi.org/10.1016/j.diii.2026.07.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing occupational radiation exposure beyond compliance.","authors":"Philippe Lucien Pereira, Dimitrios Filippiadis","doi":"10.1016/j.diii.2026.07.003","DOIUrl":"https://doi.org/10.1016/j.diii.2026.07.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148498290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shedding light on hepatobiliary MRI contrast agents: Key insights for radiologists.","authors":"Roberto Cannella","doi":"10.1016/j.diii.2026.06.009","DOIUrl":"https://doi.org/10.1016/j.diii.2026.06.009","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photon-counting CT: New horizons for functional lung imaging.","authors":"Guillaume Chassagnon","doi":"10.1016/j.diii.2026.07.002","DOIUrl":"https://doi.org/10.1016/j.diii.2026.07.002","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The position of the head influences the degree of subclavian artery stenosis during dynamic CT angiography in patients with thoracic outlet syndrome: Implications for imaging protocols.","authors":"Raja Noureddine, Mohammed Yassine, Fatma Boubaker, Nicolas Douis, Pedro Augusto Gondim Teixeira, Maxime Clara, Alain Blum, Romain Gillet","doi":"10.1016/j.diii.2026.07.001","DOIUrl":"https://doi.org/10.1016/j.diii.2026.07.001","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of artificial intelligence on emergency musculoskeletal imaging","authors":"Mickael Tordjman, Ali Guermazi","doi":"10.1016/j.diii.2026.04.005","DOIUrl":"10.1016/j.diii.2026.04.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"107 7","pages":"Pages 243-244"},"PeriodicalIF":11.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-learning image reconstruction algorithms for CT: A task-based image quality assessment of four CT systems using a phantom","authors":"Joël Greffier,&nbsp;Alexa Liogier,&nbsp;Maxime Pastor,&nbsp;Fabien de Oliveira,&nbsp;Quentin Chaine,&nbsp;Skander Sammoud,&nbsp;Jean Paul Beregi,&nbsp;Djamel Dabli","doi":"10.1016/j.diii.2026.01.003","DOIUrl":"10.1016/j.diii.2026.01.003","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to assess the performance of iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms developed by four CT vendors in terms of image quality.</div></div><div><h3>Materials and methods</h3><div>Acquisitions were performed on an image quality phantom at three dose levels (1.8, 6 and 11 mGy) using four CT systems (further referred to as G-CT, P-CT, U-CT, and C-CT). For each CT, raw data were reconstructed using the commonly used soft tissue kernel and level for IR and DLR algorithms. Noise power spectrum and task-based transfer function were computed to assess noise magnitude, noise texture (f_av) and spatial resolution, respectively. Detectability indexes (<em>d</em>’) were computed to model the detection of two abdominal lesions.</div></div><div><h3>Results</h3><div>Compared to IR, noise magnitude reduction with DLR was similar for all dose levels for G-CT (-21.1 ± 1.5 [standard deviation (SD)] %) and P-CT (-48.4 ± 0.1 [SD] %) but more pronounced at 1.8 mGy and decreased as the dose level increased for U-CT and C-CT. Noise texture was greater with DLR than IR at all dose levels for all CT systems, except for U-CT, which gave similar f_av values. For both inserts, spatial resolution was better with DLR than with IR for all CT systems, except for the low-contrast insert with C-CT at 1.8 and 6 mGy and P-CT at 1.8 mGy. For both simulated lesions and all dose levels, <em>d</em>’ values were greater with DLR than with IR by 77.5 ± 8.7 (SD) % for C-CT, 33.7 ± 5.6 (SD) % for G-CT, 112.7 ± 4.7 (SD) % for P-CT and from 158.3 % to 546.6 % on average for U-CT.</div></div><div><h3>Conclusion</h3><div>Compared to IR, DLR algorithms reduce the image noise and improve detectability whilst providing similar or better noise texture and spatial resolution.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"107 7","pages":"Pages 256-264"},"PeriodicalIF":11.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145991328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-learning reconstruction in computed tomography: Cosmetic improvements should be backed by clinical evidence","authors":"Augustin Lecler,&nbsp;Philippe Soyer","doi":"10.1016/j.diii.2026.01.009","DOIUrl":"10.1016/j.diii.2026.01.009","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"107 7","pages":"Pages 241-242"},"PeriodicalIF":11.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146107548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography examinations: A multicenter retrospective case-control study","authors":"Andre Yanchen Yeh,&nbsp;Dawei Chang,&nbsp;Pochuan Wang,&nbsp;Yenjia Chen,&nbsp;Kao-Lang Liu,&nbsp;Holger Roth,&nbsp;Hsu-Heng Yen,&nbsp;David Yen-Ting Chen,&nbsp;Po-Ting Chen,&nbsp;Wei-Chih Liao,&nbsp;Weichung Wang","doi":"10.1016/j.diii.2026.01.007","DOIUrl":"10.1016/j.diii.2026.01.007","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to develop and validate a computer-aided detection (CAD) tool for the detection of pancreatic cancer (PC) on diagnostic and prediagnostic computed tomography (CT) examinations.</div></div><div><h3>Materials and methods</h3><div>A CAD tool was developed using 2496 contrast-enhanced CT images (596 PCs, 1335 normal pancreas, 565 other pancreatic diseases) from a referral center (October 2004–December 2019) and underwent external validation at two independent institutions (January 2018–December 2020) in a retrospective case-control design. Prediagnostic CT images obtained one to 12 months before the clinical diagnosis of PC, representing clinically challenging or missed images, were collected (November 2004–August 2022) from three referral centers to further evaluate the performance of the CAD tool. Classification performance of the CAD tool was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC),</div></div><div><h3>Results</h3><div>From internal and external datasets, the diagnostic test sets included 200 PCs and 4998 controls of 4744 patients with normal pancreas and 254 patients with other pancreatic diseases (2448 women and 2750 men; median age, 63 years; age range: 18–101). The CAD tool achievedan AUC of 0.950 (95 % confidence interval [CI]: 0.932–0.968), 90.0 % sensitivity (180 out of 200; 95 % CI: 85.0–93.8), and 87.8 % specificity (4389 out of 4998; 95 % CI: 86.9–88.7) in the diagnosis of PC. For prediagnostic test sets, which included 54 PCs and 118 controls of 89 patients with normal pancreas and 19 patients with other pancreatic diseases (63 women and 99 men; median age, 61 years; age range: 18–99), the sensitivity was 66.7 % (36 out of 54; 95 % CI: 52.5–78.9). Sensitivities for PCs ≤ 2 cm were 77.1 % (27 out of 35; 95 % CI: 59.9–89.6) and 66.7 % (14 out of 21; 95 % CI: 43.0–85.4) in diagnostic and prediagnostic test sets, respectively.</div></div><div><h3>Conclusion</h3><div>This CAD tool demonstrates high diagnostic performance for the detection of PC, including for small PC or clinically unrecognized patients.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"107 7","pages":"Pages 265-275"},"PeriodicalIF":11.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146100974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}