Journal of Thoracic Imaging最新文献

{"title":"The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels.","authors":"Yenpo Lin, Gigin Lin, Meng-Ting Peng, Chi-Tai Kuo, Yung-Liang Wan, Wen-Jin Cherng","doi":"10.1097/RTI.0000000000000765","DOIUrl":"10.1097/RTI.0000000000000765","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels.</p><p><strong>Patients and methods: </strong>Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ).</p><p><strong>Results: </strong>The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively).</p><p><strong>Conclusions: </strong>There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"111-118"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approach to Imaging of Patients Presenting With Acute Coronary Syndrome With No Culprit Lesion Identified at Angiography.","authors":"Tiffany T Ni, Wendy Tsang, Elsie T Nguyen","doi":"10.1097/RTI.0000000000000773","DOIUrl":"10.1097/RTI.0000000000000773","url":null,"abstract":"<p><p>Chest pain is a common chief complaint among patients presenting to the emergency department. However, in the scenario where the clinical presentation is consistent with acute coronary syndrome and no culprit lesions are identified on angiography, clinicians and cardiac imagers should be informed of the differential diagnosis and appropriate imaging modalities used to investigate the potential causes. This review describes an imaging-based algorithm that highlights the diagnostic possibilities, their differentiating imaging features, and the important role of cardiovascular magnetic resonance imaging for narrowing the differential diagnosis.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"69-78"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139547547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional Impairment in Small Airways Associated With the Breathlessness Symptoms in Long-Coronavirus Disease.","authors":"Minsuok Kim, Jeongeun Hwang, James T Grist, Gabriele Abueid, Soon Ho Yoon, Vicente Grau, Emily Fraser, Fergus V Gleeson","doi":"10.1097/RTI.0000000000000748","DOIUrl":"10.1097/RTI.0000000000000748","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to determine the association between functional impairment in small airways and symptoms of dyspnea in patients with Long-coronavirus disease (COVID), using imaging and computational modeling analysis.</p><p><strong>Patients and methods: </strong>Thirty-four patients with Long-COVID underwent thoracic computed tomography and hyperpolarized Xenon-129 magnetic resonance imaging (HP Xe MRI) scans. Twenty-two answered dyspnea-12 questionnaires. We used a computed tomography-based full-scale airway network (FAN) flow model to simulate pulmonary ventilation. The ventilation distribution projected on a coronal plane and the percentage lobar ventilation modeled in the FAN model were compared with the HP Xe MRI data. To assess the ventilation heterogeneity in small airways, we calculated the fractal dimensions of the impaired ventilation regions in the HP Xe MRI and FAN models.</p><p><strong>Results: </strong>The ventilation distribution projected on a coronal plane showed an excellent resemblance between HP Xe MRI scans and FAN models (structure similarity index: 0.87 ± 0.04). In both the image and the model, the existence of large clustered ventilation defects was not identifiable regardless of dyspnea severity. The percentage lobar ventilation of the HP Xe MRI and FAN model showed a strong correlation (ρ = 0.63, P < 0.001). The difference in the fractal dimension of impaired ventilation zones between the low and high dyspnea-12 score groups was significant (HP Xe MRI: 1.97 [1.89 to 2.04] and 2.08 [2.06 to 2.14], P = 0.005; FAN: 2.60 [2.59 to 2.64] and 2.64 [2.63 to 2.65], P = 0.056).</p><p><strong>Conclusions: </strong>This study has identified a potential association of small airway functional impairment with breathlessness in Long-COVID, using fractal analysis of HP Xe MRI scans and FAN models.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"79-85"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiparametric Cardiovascular Magnetic Resonance in Nonhospitalized COVID-19 Infection Subjects: An Intraindividual Comparison Study.","authors":"Jun Zhang, Song Luo, Jun Cai, Xiang Kong, Lingyan Zhang, Li Qi, Long Jiang Zhang","doi":"10.1097/RTI.0000000000000774","DOIUrl":"10.1097/RTI.0000000000000774","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate intraindividual cardiac structural and functional changes before and after COVID-19 infection in a previously healthy population with a 3T cardiac magnetic resonance (CMR).</p><p><strong>Materials and methods: </strong>A total of 39 unhospitalized patients with COVID-19 were recruited. They participated in our previous study as non-COVID-19 healthy volunteers undergoing baseline CMR examination and were recruited to perform a repeated CMR examination after confirmed COVID-19 infection in December 2022. The CMR parameters were measured and compared between before and after COVID-19 infection with paired t tests. The laboratory measures including myocardial enzymes and inflammatory indicators were also collected when performing repeated CMR.</p><p><strong>Results: </strong>The median duration was 393 days from the first to second CMR and 26 days from clinical symptoms onset to the second CMR. Four patients (10.3%, 4/39) had the same late gadolinium enhancement pattern at baseline and repeated CMR and 5 female patients (12.8%, 5/39) had myocardial T2 ratio >2 (2.07 to 2.27) but with normal T2 value in post-COVID-19 CMR. All other CMR parameters were in normal ranges before and after COVID-19 infection. Between before and after the COVID-19 infection, there were no significant differences in cardiac structure, function, and tissue characterization, no matter with or without symptoms (fatigue, chest discomfort, palpitations, shortness of breath, and insomnia/sleep disorders) (all P >0.05). The laboratory measures at repeated CMR were in normal ranges in all participants.</p><p><strong>Conclusions: </strong>These intraindividual CMR studies showed unhospitalized patients with COVID-19 with normal myocardial enzymes had no measurable CMR abnormalities, which can help alleviate wide social concerns about COVID-19-related myocarditis.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"86-92"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139547551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Mitral Valve Prolapse on Non-electrocardiography-gated Enhanced Chest Computed Tomography.","authors":"Min Ji Son, Eun Ju Chun, Seung Min Yoo, Soo Jeong Lee, Charles S White","doi":"10.1097/RTI.0000000000000766","DOIUrl":"10.1097/RTI.0000000000000766","url":null,"abstract":"<p><strong>Purpose: </strong>The primary imaging modality for the diagnosis of mitral valve prolapse (MVP) is echocardiography supplemented by electrocardiography (ECG)-gated cardiac computed tomography (CT) angiography. However, we have recently encountered patients with MVP who were initially identified on non-ECG-gated enhanced chest CT. The purpose of this study is to evaluate the diagnostic accuracy of non-ECG-gated enhanced chest CT to predict the presence of MVP.</p><p><strong>Patients and methods: </strong>Of 92 patients (surgically confirmed MVP who underwent non-ECG-gated chest CT), 27 patients were excluded for motion artifact or insufficient surgical correlation, and 65 patients were ultimately included. As a control, 65 patients with dyspnea and without MVP (non-ECG-gated chest CT and echocardiography were performed within 1 month) were randomly selected. We retrospectively analyzed an asymmetric double line sign on axial CT images for the presence of MVP. The asymmetric double line sign was defined as the presence of a linear structure, not located in the plane traversing the mitral annulus.</p><p><strong>Results: </strong>Use of the asymmetric double line sign to predict MVP on non-ECG-gated CT showed modest sensitivity, high specificity, modest negative predictive value, and high positive predictive value of 59% (38/65), 99% (64/65), 70% (64/91), and 97% (38/39), respectively.</p><p><strong>Conclusion: </strong>The asymmetric double line sign on non-ECG-gated enhanced chest CT may be a valuable finding to predict the presence of MVP. Familiarity with this CT finding may lead to prompt diagnosis and proper management of MVP.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"W19-W23"},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.","authors":"Verena Brandt, Andreas Fischer, Uwe Joseph Schoepf, Raffi Bekeredjian, Christian Tesche, Gilberto J Aquino, Jim O'Doherty, Puneet Sharma, Mehmet A Gülsün, Paul Klein, Asik Ali, William Evans Few, Tilman Emrich, Akos Varga-Szemes, Josua A Decker","doi":"10.1097/RTI.0000000000000753","DOIUrl":"10.1097/RTI.0000000000000753","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA).</p><p><strong>Patients and methods: </strong>A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers.</p><p><strong>Results: </strong>A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%.</p><p><strong>Conclusions: </strong>The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"93-100"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FFR CT and Static Computed Tomography Myocardial Perfusion Imaging for Therapeutic Decision-making and Prognosis in Patients With Coronary Artery Disease.","authors":"Su Yu Li, Jian Zhong, Hong Yan Qiao, U Joseph Schoepf, Tilman Emrich, W Nicholas Butler, Rui Zuo, Yi Xue, Ya Liu, Li Yan Dai, Chang Sheng Zhou, Guang Ming Lu, Chun Xiang Tang, Long Jiang Zhang","doi":"10.1097/RTI.0000000000000718","DOIUrl":"10.1097/RTI.0000000000000718","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to investigate the effect of integrated evaluation of resting static computed tomography perfusion (CTP) and coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR CT ) on therapeutic decision-making and predicting major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease.</p><p><strong>Materials and methods: </strong>In this post hoc analysis of a prospective trial of CCTA in patients assigned to either CCTA or CCTA plus FFR CT arms, 500 patients in the CCTA plus FFR CT arm were analyzed. Both resting static CTP and FFR CT were evaluated by using the conventional CCTA. Perfusion defects in the myocardial segments with ≥50% degree of stenosis in the supplying vessels were defined as resting static CTP positive, and any vessel with an FFR CT value of ≤0.80 was considered positive. Patients were divided into 3 groups: (1) negative CTP-FFR CT match group (resting static CTP-negative and FFR CT -negative group); (2) mismatch CTP-FFR CT group (resting static CTP-positive and FFR CT -negative or resting static CTP-negative and FFR CT -positive group); and (3) positive CTP-FFR CT match group (resting static CTP-positive and FFR CT -positive group). We compared the revascularization-to-invasive coronary angiography ratio and the MACE rate among 3 subgroups at 1- and 3-year follow-ups. The adjusted Cox hazard proportional model was used to assess the prognostic value of FFR CT and resting static CTP to determine patients at risk of MACE.</p><p><strong>Results: </strong>Patients in the positive CTP-FFR CT match group were more likely to undergo revascularization at the time of invasive coronary angiography compared with those in the mismatch CTP-FFR CT group (81.4% vs 57.7%, P =0.033) and the negative CTP-FFR CT match group (81.4% vs 33.3%, P= 0.001). At 1- and 3-year follow-ups, patients in the positive CTP-FFR CT match group were more likely to have MACE than those in the mismatch CTP-FFR CT group (10.5% vs 4.2%, P= 0.046; 35.6% vs 9.4%, P <0.001) and the negative CTP-FFR CT match group (10.5% vs 0.9%, P <0.001; 35.6% vs 5.4%, P <0.001). A positive CTP-FFR CT match was strongly related to MACE at 1-year (hazard ratio=8.06, P= 0.003) and 3-year (hazard ratio=6.23, P <0.001) follow-ups.</p><p><strong>Conclusion: </strong>In patients with suspected coronary artery disease, the combination of FFR CT with resting static CTP could guide therapeutic decisions and have a better prognosis with fewer MACE in a real-world scenario.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"101-110"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10878454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9566106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Memoriam: Robert D. Pugatch, MD (1945-2023).","authors":"","doi":"10.1097/RTI.0000000000000775","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000775","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":"39 2","pages":"67-68"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Memoriam: Robert D. Pugatch, MD (1945-2023).","authors":"","doi":"10.1097/RTI.0000000000000775","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000775","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":"39 2","pages":"67-68"},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Coronary Artery Calcification Using Chest X-ray Radiographs and Machine Learning: The Role of the Radiomics Score.","authors":"Hyunseok Jeong, Hyung-Bok Park, Jongsoo Hong, Jina Lee, Seongmin Ha, Ran Heo, Juyeong Jung, Youngtaek Hong, Hyuk-Jae Chang","doi":"10.1097/RTI.0000000000000757","DOIUrl":"10.1097/RTI.0000000000000757","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the ability of radiomics score (RS)-based machine learning to identify moderate to severe coronary artery calcium (CAC) on chest x-ray radiographs (CXR).</p><p><strong>Materials and methods: </strong>We included 559 patients who underwent a CAC scan with CXR obtained within 6 months and divided them into training (n = 391) and validation (n = 168) cohorts. We extracted radiomic features from annotated cardiac contours in the CXR images and developed an RS through feature selection with the least absolute shrinkage and selection operator regression in the training cohort. We evaluated the incremental value of the RS in predicting CAC scores when combined with basic clinical factor in the validation cohort. To predict a CAC score ≥100, we built an RS-based machine learning model using random forest; the input variables were age, sex, body mass index, and RS.</p><p><strong>Results: </strong>The RS was the most prominent factor for the CAC score ≥100 predictions (odds ratio = 2.33; 95% confidence interval: 1.62-3.44; P < 0.001) compared with basic clinical factor. The machine learning model was tested in the validation cohort and showed an area under the receiver operating characteristic curve of 0.808 (95% confidence interval: 0.75-0.87) for a CAC score ≥100 predictions.</p><p><strong>Conclusions: </strong>The use of an RS-based machine learning model may have the potential as an imaging marker to screen patients with moderate to severe CAC scores before diagnostic imaging tests, and it may improve the pretest probability of detecting coronary artery disease in clinical practice.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"119-126"},"PeriodicalIF":3.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10878443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}