Diagnostic and Interventional Imaging最新文献

{"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":"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 ,&nbsp;Julien Frandon ,&nbsp;Ingrid Millet","doi":"10.1016/j.diii.2025.01.001","DOIUrl":"10.1016/j.diii.2025.01.001","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 4","pages":"Pages 113-114"},"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":"Artificial intelligence in emergency neuroradiology: Opportunities and challenges ahead","authors":"Loïc Duron ,&nbsp;Augustin Lecler","doi":"10.1016/j.diii.2024.12.005","DOIUrl":"10.1016/j.diii.2024.12.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 4","pages":"Pages 111-112"},"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 ,&nbsp;Federica Dondero ,&nbsp;Rachida Lebtahi ,&nbsp;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":"106 1","pages":"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 ,&nbsp;Yang Yang ,&nbsp;Yongwei Yang ,&nbsp;Qingrun Li ,&nbsp;Lanqian Jiao ,&nbsp;Ting Chen ,&nbsp;Dajing Guo","doi":"10.1016/j.diii.2024.07.008","DOIUrl":"10.1016/j.diii.2024.07.008","url":null,"abstract":"<div><h3>Purpose</h3><div>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).</div></div><div><h3>Materials and methods</h3><div>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.</div></div><div><h3>Results</h3><div>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 % (<em>P</em> &lt; 0.001). Concurrently, AI reduced the FPLI of all readers and specifically neuroradiologists for detecting non-occlusive AS (all <em>P</em> &lt; 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) (<em>P</em> &lt; 0.001). Sensitivity for AS ≥ 30 % and the specificity for AS ≥ 70 % increased for all readers with AI assistance (<em>P</em> = 0.01). The median reading time for all readers was reduced from 268 s without AI to 241 s with AI (<em>P</em> <em>&lt;</em> 0.001).</div></div><div><h3>Conclusion</h3><div>AI-assisted diagnosis improves the performance of radiologists in detecting head and neck AS, and shortens reading time.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"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 ,&nbsp;Sahar Soleimani ,&nbsp;Javad R. Azadi ,&nbsp;Sheila Sheth ,&nbsp;Satomi Kawamoto ,&nbsp;Ammar A. Javed ,&nbsp;Florent Tixier ,&nbsp;Ralph H. Hruban ,&nbsp;Elliot K. Fishman ,&nbsp;Linda C. Chu","doi":"10.1016/j.diii.2024.08.003","DOIUrl":"10.1016/j.diii.2024.08.003","url":null,"abstract":"<div><h3>Purpose</h3><div>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.</div></div><div><h3>Materials and methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"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}

{"title":"Comparison between artificial intelligence solution and radiologist for the detection of pelvic, hip and extremity fractures on radiographs in adult using CT as standard of reference","authors":"Maxime Pastor ,&nbsp;Djamel Dabli ,&nbsp;Raphaël Lonjon ,&nbsp;Chris Serrand ,&nbsp;Fehmi Snene ,&nbsp;Fayssal Trad ,&nbsp;Fabien de Oliveira ,&nbsp;Jean-Paul Beregi ,&nbsp;Joël Greffier","doi":"10.1016/j.diii.2024.09.004","DOIUrl":"10.1016/j.diii.2024.09.004","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to compare the diagnostic performance of an artificial intelligence (AI) solution for the detection of fractures of pelvic, proximal femur or extremity fractures in adults with radiologist interpretation of radiographs, using standard dose CT examination as the standard of reference.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included 94 adult patients with suspected bone fractures who underwent a standard dose CT examination and radiographs of the pelvis and/or hip and extremities at our institution between January 2022 and August 2023. For all patients, an AI solution was used retrospectively on the radiographs to detect and localize bone fractures of the pelvis and/or hip and extremities. Results of the AI solution were compared to the reading of each radiograph by a radiologist using McNemar test. The results of standard dose CT examination as interpreted by a senior radiologist were used as the standard of reference.</div></div><div><h3>Result</h3><div>A total of 94 patients (63 women; mean age, 56.4 ± 22.5 [standard deviation] years) were included. Forty-seven patients had at least one fracture, and a total of 71 fractures were deemed present using the standard of reference (25 hand/wrist, 16 pelvis, 30 foot/ankle). Using the standard of reference, the analysis of radiographs by the AI solution resulted in 58 true positive, 13 false negative, 33 true negative and 15 false positive findings, yielding 82 % sensitivity (58/71; 95 % confidence interval [CI]: 71–89 %), 69 % specificity (33/48; 95 % CI: 55–80 %), and 76 % accuracy (91/119; 95 % CI: 69–84 %). Using the standard of reference, the reading of the radiologist resulted in 65 true positive, 6 false negative, 42 true negative and 6 false positive findings, yielding 92 % sensitivity (65/71; 95 % CI: 82–96 %), 88 % specificity (42/48; 95 % CI: 75–94 %), and 90 % accuracy (107/119; 95 % CI: 85–95 %). The radiologist outperformed the AI solution in terms of sensitivity (<em>P</em> = 0.045), specificity (<em>P</em> = 0.016), and accuracy (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>In this study, the radiologist outperformed the AI solution for the diagnosis of pelvic, hip and extremity fractures of the using radiographs. This raises the question of whether a strong standard of reference for evaluating AI solutions should be used in future studies comparing AI and human reading in fracture detection using radiographs.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 22-27"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298957","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":"Dural arteriovenous fistula masquerading as neuromyelitis optica spectrum disorder","authors":"Sylvain Bourdoncle ,&nbsp;Caroline Papeix ,&nbsp;Augustin Lecler","doi":"10.1016/j.diii.2024.09.003","DOIUrl":"10.1016/j.diii.2024.09.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 43-44"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298958","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}