The international journal of cardiovascular imaging最新文献

{"title":"Saw-tooth myocardium: An uncommon but characteristic left ventricular dysplasia.","authors":"Javier Urmeneta Ulloa, Vicente Martínez de Vega, Isabel Molina Borao, José Ángel Cabrera","doi":"10.1007/s10554-025-03379-w","DOIUrl":"10.1007/s10554-025-03379-w","url":null,"abstract":"","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":"1011-1012"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An unusual case of Raghib syndrome presenting as atrial fibrillation in an elderly male.","authors":"Mansi Verma, Ankit Jishtu, Ritesh Kumar, Sanjeev Kumar","doi":"10.1007/s10554-025-03412-y","DOIUrl":"https://doi.org/10.1007/s10554-025-03412-y","url":null,"abstract":"","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of left ventricular T1 mapping and left atrial strain for distinguishing myocardial amyloidosis and hypertrophic cardiomyopathy.","authors":"Honghu Shen, Lin Wang, Jianxiu Lian, Ying Shi, Pengfei Liu","doi":"10.1007/s10554-025-03410-0","DOIUrl":"https://doi.org/10.1007/s10554-025-03410-0","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to use T1 mapping and left atrial feature tracking techniques to distinguish myocardial amyloidosis (CA) from hypertrophic cardiomyopathy (HCM).</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 130 subjects who underwent cardiac magnetic resonance examinations from January 2021 to May 2024 at 1.5 and 3.0 T systems. Measurements of global left ventricular myocardial T1 values were performed, a standardized T1 z-score used as an assessment metric to overcome the effects of various manufacturers and field strengths. Left atrial strain was measured with feature tracking techniques.</p><p><strong>Results: </strong>42 CA patients、58 HCM patients and 30 healthy subjects were analyzed. For both 1.5T and 3.0T systems, the overall T1 values of LV myocardium was significantly higher in the CA group compared with the HCM and control groups (p < 0.001). T1z-scores in the CA and HCM groups were 4.8 ± 2.2 and 3.4 ± 1.9, respectively (p < 0.001). Myocardial strain analysis showed that atrial strain was significantly lower in the CA group when compared with the HCM and healthy control groups (p < 0.05). The correlation between left atrial strain and function parameters was assessed through Spearman correlation analysis. Multivariate logistic regression analysis showed that a combination model including T1z-score and left atrial reservoir function (Es) had an improved ability to discriminate CA and HCM with a higher AUC (0.937), with a sensitivity of 95.2% and a specificity of 83.3% (P < 0.05).</p><p><strong>Conclusion: </strong>T1 mapping combined with Εs could effectively distinguish CA from HCM, and provide new insights for the diagnosis of the etiology and treatment of cardiac hypertrophic diseases.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-assisted left ventricular global longitudinal strain assessment in patients with acute myocardial infarction: a RESUS-AMI trial sub-analysis.","authors":"Demeke Mekonnen, Ernest Spitzer, Eugene P McFadden, Noel M Caplice, Claire B Ren","doi":"10.1007/s10554-025-03409-7","DOIUrl":"https://doi.org/10.1007/s10554-025-03409-7","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this sub-analysis of the RESUS-AMI trial was to evaluate the correlation of artificial intelligence (AI)-assisted echocardiographic global longitudinal strain (GLS) assessments with infarct size, left ventricular ejection fraction (LVEF) and volumes from cardiac magnetic resonance (CMR) imaging, in patients undergoing primary percutaneous coronary intervention for ST-elevation myocardial infarction. The reproducibility of GLS and other echocardiographic parameters derived with the AI-assisted software were also assessed.</p><p><strong>Methods: </strong>This is a post-hoc imaging sub-analysis of the RESUS-AMI trial. Echocardiographic LVEF, volumes and GLS were measured with AI-assisted software (CAAS Qardia 2.0) using automated and semi-automated methods. The CMR LVEF, LV dimensions and infarct size were obtained from a CMR core lab with an off-line workstation (CAAS MRV 4.1).</p><p><strong>Results: </strong>In total 169 echocardiograms were analysed and the GLS showed moderate correlation with the CMR infarct size (r = 0.58 automated and 0.64 semi-automated, both p < 0.001) and LVEF (r=-0.63 automated and - 0.65 semi-automated, both p < 0.001) from 81 CMR recordings. GLS also showed moderate correlation with the LVEF (r= -0.51 automated and - 0.67 semi-automated, both p < 0.001) from echocardiography. The inter-observer reproducibility was excellent in GLS from both the automated (intraclass correlation (ICC) = 0.94, bias = 0.08, limit of agreement (LOA) = 1.75) and semi-automated analysis (ICC = 0.93, bias=-0.68, LOA = 1.44). The intra-observer reproducibility was excellent in all echocardiographic measurements.</p><p><strong>Conclusion: </strong>GLS derived from the AI-assisted software (automated or semi-automated) could be used as a marker of LV systolic function as it correlates well the infarct size and LVEF assessed with CMR and LVEF with echocardiography.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.","authors":"Justin Baraboo, Amanda DiCarlo, Haben Berhane, Daming Shen, Rod Passman, Daniel C Lee, Patrick M McCarthy, Rishi Arora, Dan Kim, Michael Markl","doi":"10.1007/s10554-025-03407-9","DOIUrl":"https://doi.org/10.1007/s10554-025-03407-9","url":null,"abstract":"<p><p>Real time 2D phase contrast (RTPC) MRI is useful for flow quantification in atrial fibrillation (AF) patients, but data analysis requires time-consuming anatomical contouring for many cardiac time frames. Our goal was to develop a convolutional neural network (CNN) for fully automated left atrial (LA) flow quantification. Forty-four AF patients underwent cardiac MRI including LA RTPC, collecting a median of 358 timeframes per scan. 15,307 semi-manual derived RTPC LA contours comprised ground truth for CNN training, validation, and testing. CNN vs. human performance was assessed using Dice scores (DSC), Hausdorff distance (HD), and flow measures (stasis, velocities, flow). LA contour DSC across all patients were similar to human inter-observer DSC (0.90 vs. 0.93) and a median 4.6 mm [3.5-5.9 mm] HD. There was no impact of heart rate variability on contouring quality (low vs. high variability DSC: 0.92 ± 0.05 vs. 0.91 ± 0.03, p = 0.95). CNN based LA flow quantification showed good to excellent agreement with semi-manual analysis (r > 0.90) and small bias in Bland-Altman analysis for mean velocity (-0.10 cm/s), stasis (1%), and net flow (-2.4 ml/s). This study demonstrated the feasibility of CNN based LA flow analysis with good agreements in LA contours and flow measures and resilience to heartbeat variability in AF.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast and automatic coronary artery segmentation using nnU-Net for non-contrast enhanced magnetic resonance coronary angiography.","authors":"Huiming Zhu, Huizhong Wu, Shike Zhang, Kuaifa Fang, Guoxi Xie, Yekun Zheng, Jinxing Qiu, Feng Liu, Zhenmin Miao, Xinchen Yuan, Weibo Chen, Lincheng He","doi":"10.1007/s10554-025-03408-8","DOIUrl":"https://doi.org/10.1007/s10554-025-03408-8","url":null,"abstract":"<p><p>Non-contrast enhanced magnetic resonance coronary angiography (MRCA) is a promising coronary heart disease screening modality. However, its clinical application is hindered by inherent limitations, including low spatial resolution and insufficient contrast between coronary arteries and surrounding tissues. These technical challenges impede fast and automatic coronary artery segmentation. To tackle these issues, we propose a self-configuring deep learning-based approach for automating the segmentation of coronary arteries in MRCA images. The nnU-Net model was trained on MRCA data from 134 subjects and tested on data from 114 subjects. Two radiologists qualitatively evaluated all segmented arteries as good to excellent. Using coronary computed tomography angiography (CCTA) data from the 114 tested subjects as the gold standard. Specifically, we compared the number of branches, the total branch length, and the distance from the base of the coronary sinus to the origin of the corresponding main coronary artery obtained from manual and artificial intelligence measurements in MRCA images with those obtained from CCTA. Experiment results demonstrated that in validation nnU-Net can accurately segment from MRCA images with the Dice score of 0.903 and 0.962 for major coronary arteries and aorta, respectively.In Testing, nnU-Net achieved the Dice score of 0.726 and 0.890 for major coronary arteries and aorta, respectively. Integrating MRCA with nnU-Net to extract coronary arteries offers a non-invasive screening tool for the detection of coronary heart disease, potentially enhancing early detection and reducing reliance from CCTA.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship between left atrium, epicardial fat and severity of atrial fibrillation.","authors":"Mengyuan Jing, Qing Liu, Huaze Xi, Hao Zhu, Qiu Sun, Xuehui Liu, Yuting Zhang, Wei Ren, Liangna Deng, Tao Han, Bin Zhang, Junlin Zhou","doi":"10.1007/s10554-025-03405-x","DOIUrl":"https://doi.org/10.1007/s10554-025-03405-x","url":null,"abstract":"<p><p>To investigate the relationship between left atrium (LA) and epicardial adipose tissue (EAT) parameters and different disease severities (paroxysmal and persistent) in patients with atrial fibrillation (AF). A total of 115 patients with AF (58 paroxysmal and 57 persistent) who underwent cardiac computed tomography angiography (CTA) at our institution between October 2021 and May 2022 were included. The left atrium volume index (LAVI) and left atrium fractal dimension (LAFD) were measured for each patient. EAT volumes and attenuation values for total heart and LA in early and delayed enhancement phases were calculated using semi-automated software. LA and EAT parameters were compared with patients with paroxysmal and persistent AF. Compared with paroxysmal AF, persistent AF had significantly greater LAVI (33.60 ml/m² vs. 26.65 ml/m², P < 0.001) and LAFD (1.31 vs. 1.22, P = 0.001). At both early and late enhancement, the total EAT volume (136.29 cm³ vs. 88.68 cm³, 152.30 cm³ vs. 88.96 cm³; all P < 0.001) and attenuation values (-84.00 HU vs. -87.50 HU, -83.00 HU vs. -86.00 HU; all P < 0.05) were significantly higher in persistent AF than in paroxysmal AF. Additionally, LA EAT volumes (15.53 cm³ vs. 8.19 cm³, 18.57 cm³ vs. 9.26 cm³; all P < 0.001) and attenuation values (-74.00 HU vs. -77.00 HU, -75.00 HU vs. -77.00 HU; all P < 0.05) were significantly larger in persistent AF compared with paroxysmal AF, in both early and late enhancement phases. Correlation analysis showed that both LA (r = 0.381, 0.310; P < 0.05) and EAT parameters (r = 0.524, 0.334, 0.665, 0.208, 0.537, 0.223, 0.606, 0.276; P < 0.05) were positively associated with AF severity. Both EAT (volume and attenuation values) and parameters for assessing LA size and morphology, including LAVI and LAFD, were related to the severity of AF.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of multimodality imaging pre-access for planning of surgical creation of arteriovenous fistulas and arteriovenous grafts in the chronic kidney disease and end-stage renal disease population.","authors":"Abdullah Khan, Daniel Raskin, Sasan Partovi, Lee Kirksey","doi":"10.1007/s10554-025-03356-3","DOIUrl":"https://doi.org/10.1007/s10554-025-03356-3","url":null,"abstract":"<p><p>This review explores a range of imaging techniques used in the pre-surgical planning of vascular access, including duplex ultrasound (DUS), digital subtraction angiography (DSA), digital subtraction venography (DSV), CO2 Venography, magnetic resonance angiography (MRA), computed tomography angiography (CTA), and Intravascular ultrasound (IVUS). For each modality, we analyze its technical background, applications, advantages and disadvantages, and comparisons with alternative imaging options. DUS is the most widely used imaging modality in pre-surgical planning due to its low cost, non-invasiveness, absence of ionizing radiation and nephrotoxic contrast agents, and comparable accuracy in pre-access mapping with other methods. DSA and DSV have high sensitivity and specificity to visualize the arterial and venous system and are recommended when central vascular stenosis is suspected, or a simultaneous intervention is anticipated. However, their use is limited due to exposure to contrast agents and ionizing radiation. CO2-based contrast agents provide an alternative for end-stage renal disease (ESRD) patients to preserve residual renal function. MRA provides a noninvasive option with no radiation exposure and superior image resolution, yet the high cost and limited availability restrict their widespread clinical use. CTA, with its short acquisition time and high-resolution imaging, is a vital modality in intricate cases. However, radiation and contrast exposure can pose challenges in this patient population. The newer IVUS modality has a superior ability to central venous outflow obstruction compared to DSA and provides more information regarding vascular geometry and anatomy. Each imaging modality has its unique advantages and disadvantages in this patient cohort. The decision to use a particular imaging must be made on a case-to-case basis. However, following KDOQI guidelines, a combination of a patient's medical history, physical examination, and DUS is a widely accepted standard practice in pre-surgical vascular access planning, with other imaging modalities reserved for selected patients.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.","authors":"Tatsuya Nishii, Tomoro Morikawa, Hiroki Nakajima, Yasutoshi Ohta, Takuma Kobayashi, Kensuke Umehara, Junko Ota, Takashi Kakuta, Satsuki Fukushima, Tetsuya Fukuda","doi":"10.1007/s10554-025-03403-z","DOIUrl":"https://doi.org/10.1007/s10554-025-03403-z","url":null,"abstract":"<p><p>We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV prolapse and assess their diagnostic performance and efficiency. We retrospectively reviewed the cardiac CTs of consecutive patients who underwent MV repair in 2023. The original images were iteratively reconstructed and denoised with a residual dense network. 3DVR images of the \"surgeon's view\" were created with blood chamber transparency to display the MV leaflets. We compared the 3DVR image quality between the original and denoised images with a 100-point scoring system. Diagnostic confidence for prolapse was evaluated across eight MV segments: A1-3, P1-3, and the anterior and posterior commissures. Surgical findings were used as the reference to assess diagnostic ability with the area under curve (AUC). The interpretation time for the denoised 3DVR images was compared with that for multiplanar reformat images. For fifty patients (median age 64 years, 30 males), denoising the 3DVR images significantly improved their image quality scores from 50 to 76 (P <.001). The AUC in identifying MV prolapse improved from 0.91 (95% CI 0.87-0.95) to 0.94 (95% CI 0.91-0.98) (P =.009). The denoised 3DVR images were interpreted five-times faster than the multiplanar reformat images (P <.001). Deep learning-based denoising enhanced the quality of 3DVR imaging of the MV, improving the performance and efficiency in detecting MV prolapse on cardiac CT.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of parametric feature maps on the reproducibility of radiomics from different fields of view in cardiac magnetic resonance cine images- a clinical and experimental study setting.","authors":"Laura Jacqueline Jensen, Damon Kim, Thomas Elgeti, Ingo Günter Steffen, Lars-Arne Schaafs, Anja Cretnik, Bernd Hamm, Sebastian Niko Nagel","doi":"10.1007/s10554-025-03404-y","DOIUrl":"https://doi.org/10.1007/s10554-025-03404-y","url":null,"abstract":"<p><p>In cardiac MRI, the field of view (FOV) is adapted to the individual patient's size, influencing spatial resolution and myocardial radiomics. This study aimed to investigate the effects of parametric feature maps on radiomics derived from cine images acquired with different FOV sizes on individuals without myocardial pathologies. In the clinical setting, cardiac MRI scans from clinical care were screened retrospectively for patients without pathological findings, neither in the MRI nor the medical history or follow-up, resulting in 61 included patients. In the experimental setting, 12 healthy volunteers were prospectively examined on a 1.5 Tesla MRI scanner with cine images acquired with three different FOVs (256 × 329 mm, 279 × 359 mm, 302 × 390 mm). One midventricular end-diastolic short-axis slice of the non-enhanced cine images was extracted for healthy volunteers and patients. The left ventricular myocardium was encompassed with regions of interest (ROIs). Ninety-three features were extracted using PyRadiomics. Images were converted to parametric radiomic feature maps using pretested software. ROIs were copied to the maps to retrieve the feature quantity. The variability of features across the different FOVs from the original images and feature maps was assessed with coefficients of variation (COVs) and rated stable at up to 10%. When derived from the original images, out of the 93 extracted features, only 24 (patients) and 29 (volunteers) revealed COVs < 10%. When extracted from the parametric maps, the number of stable features increased by 63% and 66%, with 39 (patients) and 48 (volunteers) features showing COVs < 10%, respectively. Software-computed parametric feature maps improve the reproducibility of radiomics across different FOVs in cardiac cine images of individuals without myocardial pathologies. Prospective investigations with different FOVs of a patient collective with myocardial pathologies could enhance the generalizability of the findings.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144034161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}