Diagnostic and Interventional Imaging最新文献

{"title":"Generative AI smartphones: From entertainment to potentially serious risks in radiology","authors":"Loïc Duron , Philippe Soyer , Augustin Lecler","doi":"10.1016/j.diii.2024.10.002","DOIUrl":"10.1016/j.diii.2024.10.002","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 76-78"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382083","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":"Ultra-high resolution spectral photon-counting CT outperforms dual layer CT for lung imaging: Results of a phantom study","authors":"Hugo Lacombe ,&nbsp;Joey Labour ,&nbsp;Fabien de Oliveira ,&nbsp;Antoine Robert ,&nbsp;Angèle Houmeau ,&nbsp;Marjorie Villien ,&nbsp;Sara Boccalini ,&nbsp;Jean-Paul Beregi ,&nbsp;Philippe C. Douek ,&nbsp;Joël Greffier ,&nbsp;Salim A. Si-Mohamed","doi":"10.1016/j.diii.2024.09.011","DOIUrl":"10.1016/j.diii.2024.09.011","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to compare lung image quality obtained with ultra-high resolution (UHR) spectral photon-counting CT (SPCCT) with that of dual-layer CT (DLCT), at standard and low dose levels using an image quality phantom and an anthropomorphic lung phantom.</div></div><div><h3>Methods</h3><div>An image quality phantom was scanned using a clinical SPCCT prototype and an 8 cm collimation DLCT from the same manufacturer at 10 mGy. Additional acquisitions at 6 mGy were performed with SPCCT only. Images were reconstructed with dedicated high-frequency reconstruction kernels, slice thickness between 0.58 and 0.67 mm, and matrix between 512² and 1024² mm, using a hybrid iterative algorithm at level 6. Noise power spectrum (NPS), task-based transfer function (TTF) for iodine and air inserts, and detectability index (<em>d’</em>) were assessed for ground-glass and solid nodules of 2 mm to simulate highly detailed lung lesions. Subjective analysis of an anthropomorphic lung phantom was performed by two radiologists using a five-point quality score.</div></div><div><h3>Results</h3><div>At 10 mGy, noise magnitude was reduced by 29.1 % with SPCCT images compared to DLCT images for all parameters (27.1 ± 11.0 [standard deviation (SD)] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 6 mGy with SPCCT images, noise magnitude was reduced by 8.9 % compared to DLCT images at 10 mGy (34.8 ± 14.1 [SD] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 10 mGy and 6 mGy, average NPS spatial frequency (f_av) was greater for SPCCT images (0.75 ± 0.17 [SD] mm^-1) compared to DLCT images at 10 mGy (0.55 ± 0.04 [SD] mm^-1) while remaining constant from 10 to 6 mGy. At 10 mGy, TTF at 50 % (f₅₀) was greater for SPCCT images (0.92 ± 0.08 [SD] mm^-1) compared to DLCT images (0.67 ± 0.06 [SD] mm^-1) for both inserts. At 6 mGy, f₅₀ decreased by 1.1 % for SPCCT images, while remaining greater compared to DLCT images at 10 mGy (0.91 ± 0.06 [SD] mm^-1 vs. 0.67 ± 0.06 [SD] mm^-1, respectively). At both dose levels, <em>d’</em> were greater for SPCCT images compared to DLCT for all clinical tasks. Subjective analysis performed by two radiologists revealed a greater median image quality for SPCCT (5; Q1, 4; Q3, 5) compared to DLCT images (3; Q1, 3; Q3, 3).</div></div><div><h3>Conclusion</h3><div>UHR SPCCT outperforms DLCT in terms of image quality for lung imaging. In addition, UHR SPCCT contributes to a 40 % reduction in radiation dose compared to DLCT.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 60-67"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367077","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":"CT, MRI and contrast-enhanced ultrasound features of mucinous cystic neoplasm of the liver","authors":"Emily Rius ,&nbsp;Raphael Dautry ,&nbsp;Stylianos Tzedakis","doi":"10.1016/j.diii.2024.10.003","DOIUrl":"10.1016/j.diii.2024.10.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 79-80"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407067","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":"Combining photon-counting coronary CT and artificial intelligence to detect coronary artery stenosis","authors":"David A. Bluemke,&nbsp;Prashant Nagpal","doi":"10.1016/j.diii.2024.11.005","DOIUrl":"10.1016/j.diii.2024.11.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 45-46"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787407","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":"Editor's note: 2024-the year in review for Diagnostic & Interventional Imaging","authors":"Philippe Soyer","doi":"10.1016/j.diii.2025.01.003","DOIUrl":"10.1016/j.diii.2025.01.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 47-48"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014591","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":"Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography","authors":"Jan M. Brendel ,&nbsp;Jonathan Walterspiel ,&nbsp;Florian Hagen ,&nbsp;Jens Kübler ,&nbsp;Andreas S. Brendlin ,&nbsp;Saif Afat ,&nbsp;Jean-François Paul ,&nbsp;Thomas Küstner ,&nbsp;Konstantin Nikolaou ,&nbsp;Meinrad Gawaz ,&nbsp;Simon Greulich ,&nbsp;Patrick Krumm ,&nbsp;Moritz T. Winkelmann","doi":"10.1016/j.diii.2024.09.012","DOIUrl":"10.1016/j.diii.2024.09.012","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).</div></div><div><h3>Materials and methods</h3><div>Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels.</div></div><div><h3>Results</h3><div>A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83–0.94) at the patient level and 0.92 (95 % CI: 0.89–0.94) at the vessel level.</div></div><div><h3>Conclusion</h3><div>Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 68-75"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376173","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":"Photon-counting CT systems: A technical review of current clinical possibilities","authors":"Joël Greffier ,&nbsp;Anaïs Viry ,&nbsp;Antoine Robert ,&nbsp;Mouad Khorsi ,&nbsp;Salim Si-Mohamed","doi":"10.1016/j.diii.2024.09.002","DOIUrl":"10.1016/j.diii.2024.09.002","url":null,"abstract":"<div><div>In recent years, computed tomography (CT) has undergone a number of developments to improve radiological care. The most recent major innovation has been the development of photon-counting detectors. By comparison with the energy-integrating detectors traditionally used in CT, these detectors offer better dose efficiency, eliminate electronic noise, improve spatial resolution and have intrinsic spectral sensitivity. These detectors also allow the energy of each photon to be counted, thus improving the sampling of the X-ray spectrum in multiple energy bins, to better distinguish between photoelectric and Compton attenuation coefficients, resulting in better spectral images and specific color K-edge images. The purpose of this article was to make the reader more familiar with the basic principles and techniques of new photon-counting CT systems equipped with photon-counting detectors and also to describe the currently available devices that could be used in clinical practice.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 53-59"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298960","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":"An introductory guide to statistics for the radiologist","authors":"Emma O'Shaughnessy ,&nbsp;Emmanuel Detrinidad ,&nbsp;Philippe Soyer ,&nbsp;Augustin Lecler","doi":"10.1016/j.diii.2024.11.003","DOIUrl":"10.1016/j.diii.2024.11.003","url":null,"abstract":"<div><div>Radiology generates both qualitative and quantitative data. As a consequence, statistical analysis is essential to validate data interpretation, and support reliable conclusions. Statistics serves as a cornerstone of radiology research, objectively verifying observations and establishing relationships between variables. This article provides a practical guide to basic statistical methods for radiology researchers, enabling them to structure their analyses more effectively and highlight their findings in a meaningful way. Although not exhaustive, this article covers basic statistical principles.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 2","pages":"Pages 49-52"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796547","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":"Evaluation of navigation and robotic systems for percutaneous image-guided interventions: A novel metric for advanced imaging and artificial intelligence integration.","authors":"Francois H Cornelis, Dimitrios K Filippiadis, Philipp Wiggermann, Stephen B Solomon, David C Madoff, Laurent Milot, Sylvain Bodard","doi":"10.1016/j.diii.2025.01.004","DOIUrl":"https://doi.org/10.1016/j.diii.2025.01.004","url":null,"abstract":"<p><strong>Purpose: </strong>Navigation and robotic systems aim to improve the accuracy and efficiency of percutaneous image-guided interventions, but the evaluation of their autonomy and integration of advanced imaging and artificial intelligence (AI) is lacking. The purpose of this study was to evaluate the level of automation and integration of advanced imaging and artificial intelligence in navigation and robotic systems for percutaneous image-guided interventions, using established and novel metrics to categorize and compare their capabilities.</p><p><strong>Materials and methods: </strong>Following PRISMA guidelines, a systematic review was conducted to identify studies on clinically validated navigation and robotic systems published between 2000 and May 2024. The PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Data on navigation devices were extracted and analyzed. The levels of autonomy in surgical robotics (LASR) classification system (from 1 to 5) was used to analyze automation. A novel taxonomy, the Levels of Integration of Advanced Imaging and AI (LIAI2) classification system, was created to categorize the integration of imaging technologies and AI (from 1 to 5). These two scores were combined into an aggregate score (from 1 to 10) to reflect the autonomy in percutaneous image-guided intervention.</p><p><strong>Results: </strong>The review included 20 studies assessing two navigation systems and eight robotic devices. The median LASR score was 1 (Q1, Q3: 1, 1), the median LIAI2 score was 2 (Q1, Q3: 2, 3), and the median aggregate score was 3 (Q1, Q3: 3, 4). Only one robotic system (10 % of those reviewed) achieved the highest LASR qualification in the literature, a level 2/5. Four systems (40 %) shared the highest rating for LIAI2, which was a score of 3/5. Four systems (40 %) achieved the highest aggregate scores of 4/10.</p><p><strong>Conclusion: </strong>None of the navigation and robotic systems achieved full autonomy for percutaneous image-guided intervention. The LASR and LIAI2 scales can guide innovation by identifying areas for further development and integration.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068969","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":"Normal variations of myocardial T1, T2 and T2* values at 1.5 T cardiac MRI in sex-matched healthy volunteers.","authors":"Farah Cadour, Jérôme Caudron, André Gillibert, Sébastien Normant, Jean-Nicolas Dacher","doi":"10.1016/j.diii.2025.01.005","DOIUrl":"https://doi.org/10.1016/j.diii.2025.01.005","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to determine the normal variations of myocardial T1, T2, and T2* relaxation times on cardiac MRI obtained at 1.5 T in healthy, sex-balanced volunteers aged between 18 and 69 years.</p><p><strong>Material and methods: </strong>A total of 172 healthy volunteers were recruited prospectively. They were further divided into seven sex-balanced age groups (18-19 years, 20-24 years, 25-29 years, 30-39 years, 40-49 years, 50-59 years, and 60-69 years). T1, T2, and T2* mapping were acquired in a single short-axis slice at the mid-level of the left ventricle. Global T1, T2, and T2* values were the mean of all segments. Comparisons between females and males were performed in each age group using independent samples t-test or Wilcoxon rank sum test, as appropriate. Multivariable linear effects models were used to analyze the effect of heart rate, body mass index, left ventricular mass, age, and sex on T1, T2, and T2* values. Inter- and intra-observer correlation (ICC) was evaluated.</p><p><strong>Results: </strong>A total of 172 healthy participants were included. There were 83 males and 89 females, with a mean age of 37.3 ± 15.6 (standard deviation [SD]) years. Females had greater T1 values (980.9 ± 26.2 [SD] ms) compared to males (949.7 ± 18.3 [SD] ms) (P < 0.001). T1 values decreased with age (974.3 ± 26.97 [SD] ms when ≤ 39 years vs. 954.4 ± 24.12 [SD] ms when > 39 years; P < 0.001), with smaller sex-related differences in older participants. Male sex and age were independently associated with lower values of T1 mapping. Age in females was independently associated with lower T1, T2, and T2* values. Moderate to good inter- and intra-observer agreement was found for T1, T2, and T2* (ICC ranging from 0.72 to 0.89).</p><p><strong>Conclusion: </strong>T1, T2, and T2* values are influenced by age and sex, emphasizing the need to read and calibrate MRI values with respect to patient characteristics to avoid misdiagnosis.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048091","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}