Diagnostic and Interventional Imaging最新文献

{"title":"Artificial intelligence in radiation therapy: An emerging revolution that will be driven by generative methodologies.","authors":"Steven P Rowe, N Ari Wijetunga","doi":"10.1016/j.diii.2024.09.006","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.006","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298956","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 systems: A technical review of current clinical possibilities.","authors":"Joël Greffier, Anaïs Viry, Antoine Robert, Mouad Khorsi, Salim Si-Mohamed","doi":"10.1016/j.diii.2024.09.002","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.002","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-20","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":"","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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","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":"Myocardial strain imaging: Advancing the diagnosis of cardiac amyloidosis with MRI.","authors":"Patrick Krumm","doi":"10.1016/j.diii.2024.09.007","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.007","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298959","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, Djamel Dabli, Raphaël Lonjon, Chris Serrand, Fehmi Snene, Fayssal Trad, Fabien de Oliveira, Jean-Paul Beregi, Joël Greffier","doi":"10.1016/j.diii.2024.09.004","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.004","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Result: </strong>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 (P = 0.045), specificity (P = 0.016), and accuracy (P < 0.001).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","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":"Artificial intelligence solutions for head and neck CT angiography: Ready for prime time?","authors":"Alexandre Bani-Sadr,Augustin Lecler","doi":"10.1016/j.diii.2024.09.005","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256137","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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-14","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":"Dural arteriovenous fistula masquerading as neuromyelitis optica spectrum disorder.","authors":"Sylvain Bourdoncle, Caroline Papeix, Augustin Lecler","doi":"10.1016/j.diii.2024.09.003","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-14","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}

{"title":"Flow quantification within the aortic ejection tract using 4D flow cardiac MRI in patients with bicuspid aortic valve: Implications for the assessment of aortic regurgitation.","authors":"Lan-Anh Nguyen,Umit Gencer,Guillaume Goudot,Damian Craiem,Mariano E Casciaro,Charles Cheng,Emmanuel Messas,Elie Mousseaux,Gilles Soulat","doi":"10.1016/j.diii.2024.09.001","DOIUrl":"https://doi.org/10.1016/j.diii.2024.09.001","url":null,"abstract":"PURPOSEThe purpose of this study was to evaluate the performance of four-dimensional (4D) flow cardiac MRI in quantifying aortic flow in patients with bicuspid aortic valve (BAV).MATERIALS AND METHODSPatients with BAV who underwent transthoracic echocardiography (TTE) and 4D flow cardiac MRI were prospectively included. Aortic flow was quantified using two-dimensional phase contrast velocimetry at the sinotubular junction and in the ascending aorta and using 4D flow in the regurgitant jet, in the left ventricular outflow tract, at the aortic annulus, the sinotubular junction, and the ascending aorta, with or without anatomical tracking. Flow quantification was compared with ventricular volumes, pulmonary flow using Pearson correlation test, bias and limits of agreement (LOA) using Bland Altman method, and with multiparametric transthoracic echocardiography quantification using weighted kappa test.RESULTSEighty-eight patients (63 men, 25 women) with a mean age of 50.5 ± 14.8 (standard deviation) years (age range: 20.8-78.3) were included. Changes in flow with or without tracking were modest (< 5 mL). The best correlation was obtained at the aortic annulus for forward volume (r = 0.84; LOA [-28.4; 25.3] mL) and at the regurgitant jet and sinotubular junction for regurgitant volume (r = 0.68; LOA [-27.8; 33.8] and r = 0.69; LOA [-28.6; 24.2] mL). A combined approach for regurgitant fraction and net volume calculations using forward volume measured at ANN and regurgitant volume at sinotubular junction performed better than each level taken separately (r = 0.90; LOA [-20.7; 10.0] mL and r = 0.48, LOA [-33.8; 33.4] %). The agreement between transthoracic echocardiography and 4D flow cardiac MRI for aortic regurgitation grading was poor (kappa, 0.13 to 0.42).CONCLUSIONIn patients with BAV, aortic flow quantification by 4D flow cardiac MRI is the most accurate at the annulus for the forward volume, and at the sinotubular junction or directly in the jet for the regurgitant volume.","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269808","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":"Breaking barriers in inner ear MRI: The changing role of deep learning reconstruction","authors":"","doi":"10.1016/j.diii.2024.07.010","DOIUrl":"10.1016/j.diii.2024.07.010","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256138","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}