Diagnostic and Interventional Imaging最新文献

{"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}

{"title":"Assessment of ischemia in mechanical small bowel obstruction: The time has come for dual-energy CT.","authors":"Marc Zins, Julien Frandon, Ingrid Millet","doi":"10.1016/j.diii.2025.01.001","DOIUrl":"https://doi.org/10.1016/j.diii.2025.01.001","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030149","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":"Does genicular artery embolization compromise future knee surgery in patients with knee osteoarthritis? A strategic call to the community.","authors":"Marc Sapoval, Olivier Pellerin, Axel Boyer, Carole Déan, Tom Boeken","doi":"10.1016/j.diii.2024.12.006","DOIUrl":"https://doi.org/10.1016/j.diii.2024.12.006","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025388","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":"https://doi.org/10.1016/j.diii.2025.01.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-18","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":"Myocardial scar detection in patients with implantable cardiac device: Wideband free-breathing motion-corrected black-blood late gadolinium enhancement could be the answer.","authors":"Farah Cadour, Benjamin Longère","doi":"10.1016/j.diii.2025.01.002","DOIUrl":"https://doi.org/10.1016/j.diii.2025.01.002","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014593","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":"First in-human gadolinium K-edge imaging with spectral photon counting CT.","authors":"Sara Boccalini, Salim Si-Mohamed, Klaus Erhard, Marine Bordet, Yoad Yagil, Philippe Douek","doi":"10.1016/j.diii.2024.12.004","DOIUrl":"https://doi.org/10.1016/j.diii.2024.12.004","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014592","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 in emergency neuroradiology: Opportunities and challenges ahead.","authors":"Loïc Duron, Augustin Lecler","doi":"10.1016/j.diii.2024.12.005","DOIUrl":"https://doi.org/10.1016/j.diii.2024.12.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014590","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":"Gadobenate dimeglumine-enhanced MRI: A surrogate marker of liver function recovery after auxiliary partial orthotopic liver transplantation.","authors":"Marco Dioguardi Burgio, Federica Dondero, Rachida Lebtahi, Maxime Ronot","doi":"10.1016/j.diii.2024.09.010","DOIUrl":"10.1016/j.diii.2024.09.010","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":"41-42"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356417","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":"Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study.","authors":"Kunhua Li, Yang Yang, Yongwei Yang, Qingrun Li, Lanqian Jiao, Ting Chen, Dajing Guo","doi":"10.1016/j.diii.2024.07.008","DOIUrl":"10.1016/j.diii.2024.07.008","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to investigate the added value of artificial intelligence (AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography (CTA).</p><p><strong>Materials and methods: </strong>Patients who underwent head and neck CTA examinations at two hospitals were retrospectively included. CTA examinations were randomized into group 1 (without AI-washout-with AI) and group 2 (with AI-washout-without AI), and six readers (two radiology residents, two non-neuroradiologists, and two neuroradiologists) independently interpreted each CTA examination without and with AI solutions. Additionally, reading time was recorded for each patient. Digital subtraction angiography was used as the standard of reference. The diagnostic performance for AS at lesion and patient levels with four AS thresholds (30 %, 50 %, 70 %, and 100 %) was assessed by calculating sensitivity, false-positive lesions index (FPLI), specificity, and accuracy.</p><p><strong>Results: </strong>A total of 268 patients (169 men, 63.1 %) with a median age of 65 years (first quartile, 57; third quartile, 72; age range: 28-88 years) were included. At the lesion level, AI improved the sensitivity of all readers by 5.2 % for detecting AS ≥ 30 % (P < 0.001). Concurrently, AI reduced the FPLI of all readers and specifically neuroradiologists for detecting non-occlusive AS (all P < 0.05). At the patient level, AI improved the accuracy of all readers by 4.1 % (73.9 % [1189/1608] without AI vs. 78.0 % [1254/1608] with AI) (P < 0.001). Sensitivity for AS ≥ 30 % and the specificity for AS ≥ 70 % increased for all readers with AI assistance (P = 0.01). The median reading time for all readers was reduced from 268 s without AI to 241 s with AI (P< 0.001).</p><p><strong>Conclusion: </strong>AI-assisted diagnosis improves the performance of radiologists in detecting head and neck AS, and shortens reading time.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":"11-21"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298955","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":"Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.","authors":"Felipe Lopez-Ramirez, Sahar Soleimani, Javad R Azadi, Sheila Sheth, Satomi Kawamoto, Ammar A Javed, Florent Tixier, Ralph H Hruban, Elliot K Fishman, Linda C Chu","doi":"10.1016/j.diii.2024.08.003","DOIUrl":"10.1016/j.diii.2024.08.003","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening.</p><p><strong>Materials and methods: </strong>Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses.</p><p><strong>Results: </strong>A total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 ± 11.6 (standard deviation) years (range: 20-85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5-1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90 % sensitivity (95 % confidence interval [CI]: 80-98), 76 % specificity (95 % CI: 62-88) and an AUC of 0.87 (95 % CI: 0.79-0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95 % CI: 0.79-0.93). In comparison, the two radiologists achieved a mean 50 % sensitivity and 100 % specificity using arterial phase CT images.</p><p><strong>Conclusion: </strong>Radiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":"28-40"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298961","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}