Dataset Information

Verification of threshold for image intensity ratio analyses of late gadolinium enhancement magnetic resonance imaging of left atrial fibrosis in 1.5T scans.

ABSTRACT: The use of cardiovascular magnetic resonance imaging left atrial late gadolinium enhancement (LA LGE) is increasing for fibrosis evaluation though the use is still limited to specialized centres due to complex image acquisition and lack of consensus on image analyses. Analysis of LA LGE with image intensity ratio (IIR) (pixel intensity of atrial wall normalized by blood pool intensity) provides an objective method to obtain quantitative data on atrial fibrosis. A threshold between healthy myocardium and fibrosis of 1.2 has previously been established in 3T scans. The aim of the study was to reaffirm this threshold in 1.5T scans. LA LGE was performed using a 1.5T magnetic resonance scanner on: 11 lone-AF patients, 11 age-matched healthy volunteers (aged 27-44) and 11 elderly patients without known history of AF but varying degrees of comorbidities. Mean values of IIR for all healthy volunteers +2SD were set as upper limit of normality and was reproduced to 1.21 and the original IIR-threshold of 1.20 was maintained. The degree of fibrosis in lone-AF patients [median 9.0% (IQR 3.9-12.0)] was higher than in healthy volunteers [2.8% (1.3-8.3)] and even higher in elderly non-AF [20.1% (10.2-35.8), p?=?0.001]. The previously established IIR-threshold of 1.2 was reaffirmed in 1.5T LA LGE scans. Patients with lone AF presented with increased degrees of atrial fibrosis compared to healthy volunteers in the same age-range. Elderly patients with no history of AF showed significantly higher degrees of fibrosis compared to both groups with younger individuals.

SUBMITTER: Bertelsen L

PROVIDER: S-EPMC7080681 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveArrhythmogenic mitral valve prolapse (MVP) is an important cause of sudden cardiac death characterized by fibrosis of the papillary muscles or left ventricle (LV) wall, and an association between late gadolinium enhancement (LGE) of the LV papillary muscles and ventricular arrhythmia in MVP has been reported. However, LGE of the papillary muscles may be observed in other causes of mitral regurgitation, and it is not limited to patients with MVP. This study was to evaluate the association of LGE of the LV papillary muscles or ventricular wall on cardiac magnetic resonance imaging (CMR) and ventricular arrhythmia in patients with mitral regurgitation.Materials and methodsThis study included 88 patients (mean age ± standard deviation, 58.3 ± 12.0 years; male, 42%) with mitral regurgitation who underwent CMR. They were allocated to the MVP (n = 43) and non-MVP (n = 45) groups, and their LGE images on CMR, clinical characteristics, echocardiographic findings, and presence of arrhythmia were compared.ResultsLV myocardial wall enhancement was more frequent in the MVP group than in the non-MVP group (28% vs. 11%, p = 0.046). Papillary muscle enhancement was observed in 7 (7.9%) patients. Of the 43 patients with MVP, 15 (34.8%) showed LGE in the papillary muscles or LV myocardium, including 12 (27.9%) with LV myocardial wall enhancement and 4 (9.3%) with papillary muscle enhancement. One patient with bilateral diffuse papillary muscle enhancement experienced sudden cardiac arrest due to ventricular fibrillation. Univariable logistic regression analysis showed that high systolic blood pressure (BP; odds ratio [OR], 1.05; 95% confidence interval [CI], 1.01-1.09; p = 0.027) and ventricular arrhythmia (OR, 6.84; 95% CI, 1.29-36.19; p = 0.024) were significantly associated with LGE of the papillary muscles.ConclusionLGE of the papillary muscles was present not only in patients with MVP, but also in patients with other etiologies of mitral regurgitation, and it was associated with high systolic BP and ventricular arrhythmia. Papillary muscle enhancement on CMR should not be overlooked.

Project description:BackgroundAutomatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data.MethodsThirty-six high-resolution (0.7×0.7×2.0 mm3) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δxtrain = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δxtest = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δxtrain = 0.7 to 1.7 mm) was tested.ResultsThe prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and - 0.5 - 0.0 p.p. (2.00 - 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 - 1.69) for all investigated test resolutions.ConclusionA mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.

Project description:AimsThis study aimed to evaluate the clinical parameters including late gadolinium enhancement (LGE) of cardiovascular magnetic resonance to predict re-worsening of left ventricular ejection fraction (LVEF) in patients with dilated cardiomyopathy (DCM).Methods and resultsWe included 138 patients with recent-onset DCM who had an LVEF <45% and underwent LGE of cardiovascular magnetic resonance imaging at diagnosis and echocardiography at the yearly follow-up [median 6 (4-8.3) years]. Initial LVEF recovery was defined as LVEF increase >10% from baseline, resulting in LVEF ?45% after treatment. The patients were divided into three groups: (i) improved (n = 83, 60%), defined as those with sustained LVEF ?45%; (ii) re-worsening (n = 39, 28%), those with >5% decrease and LVEF <45% after the initial LVEF recovery; and (iii) not-improved (n = 16, 12%), those without initial LVEF recovery. The primary endpoint was a composite of hospitalization for heart failure or sudden cardiac death. In baseline, LGE was observed in 70 patients. The LGE area was significantly larger in the re-worsening and not-improved groups than that in the improved group (P < 0.001). Loess curves of long-term LVEF trajectories showed that LVEF in the re-worsening group increased in the first 2 years and slowly declined thereafter. Multivariate logistic regression analysis demonstrated that LGE area [odds ratio (OR) 1.09, 95% confidence interval (CI) 1.03-1.16, P = 0.004], B-type natriuretic peptide (OR 1.49, 95% CI 1.05-2.21, P = 0.030) level at the initial recovery, and LVEF (OR 0.91, 95% CI 0.86-0.97, P = 0.004) at the initial LVEF recovery were independent predictors of re-worsening of LVEF. During a median follow-up of 2273 (interquartile range: 1634-3191) days, the primary endpoint was observed in 31 (22%) patients. Univariate Cox proportional hazards analysis demonstrated that the risk of experiencing the primary event in the re-worsening group was significantly higher (hazard ratio: 4.30, 95% CI 1.63-11.31, P = 0.003) than that in the improved group and was lower than that in the not-improved group (hazard ratio: 0.33, 95% CI 0.15-0.72, P = 0.006).ConclusionsRe-worsening of LVEF was observed in 28% of patients with recent-onset DCM who showed an initial improvement in LVEF. High LGE burden, higher B-type natriuretic peptide level, and lower LVEF at the initial LVEF recovery were independent predictors of re-worsening of LVEF in patients with DCM. Careful observation is recommended for patients with a high risk for re-worsening of LVEF, even in those with an initial LVEF recovery.

Project description:BackgroundQuantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced readers often use conventional cine sequences to accurately identify the myocardium borders.PurposeTo develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification.Study typeRetrospective.PopulationA total of 191 hypertrophic cardiomyopathy patients: 1) 162 patients from two sites randomly split into training (50%; 81 patients), validation (25%, 40 patients), and testing (25%; 41 patients); and 2) an external testing dataset (29 patients) from a third site.Field strength/sequence1.5T, inversion-recovery segmented gradient-echo LGE and balanced steady-state free-precession cine sequences ASSESSMENT: Two convolutional neural networks (CNN) were trained for myocardium and scar segmentation, one with and one without LGE-Cine fusion. For CNN with fusion, the input was two aligned LGE and cine images at matched cardiac phase and anatomical location. For CNN without fusion, only LGE images were used as input. Manual segmentation of the datasets was used as reference standard.Statistical testsManual and CNN-based quantifications of LGE scar burden and of myocardial volume were assessed using Pearson linear correlation coefficients (r) and Bland-Altman analysis.ResultsBoth CNN models showed strong agreement with manual quantification of LGE scar burden and myocardium volume. CNN with LGE-Cine fusion was more robust than CNN without LGE-Cine fusion, allowing for successful segmentation of significantly more slices (603 [95%] vs. 562 (89%) of 635 slices; P < 0.001). Also, CNN with LGE-Cine fusion showed better agreement with manual quantification of LGE scar burden than CNN without LGE-Cine fusion (%ScarLGE-cine = 0.82 × %Scarmanual , r = 0.84 vs. %ScarLGE = 0.47 × %Scarmanual , r = 0.81) and myocardium volume (VolumeLGE-cine = 1.03 × Volumemanual , r = 0.96 vs. VolumeLGE = 0.91 × Volumemanual , r = 0.91).Data conclusionCNN based LGE-Cine fusion can improve the robustness and accuracy of automated scar quantification.Level of evidence3 TECHNICAL EFFICACY: 1.

Project description:BACKGROUND:Myocardial fibrosis occurs in aortic stenosis (AS) as part of the hypertrophic response. It can be detected by LGE, which is associated with an adverse prognosis in the form of increased mortality and morbidity. OBJECTIVES:To assess the prevalence of LGE patterns using cardiac magnetic resonance (CMR) in severe AS patients and to study its prognostic significance. METHODS:Patients enrolled into the study from June 2012 to November 2014. All the patients underwent CMR and various patterns of LGE studied. These patients if symptomatic were advised AVR and others were managed conservatively. All patients were followed up and watched for outcomes like mortality, heart failure/hospitalization for cardiovascular cause, fall in left ventricular ejection fraction (LVEF) ?20% and arrhythmia. RESULTS:A total of 109 patients (mean age-57.7±12.5yrs) underwent CMR with 63 males. These patients were followed up for a mean of 13 months. Among 38 patients who underwent AVR, 6 died (5-cardiovascular cause, 1-non cardiovascular). 71 patients were managed conservatively out of which 18 died (17-cardiovascular cause, 1-non cardiovascular cause). LGE patterns were seen in 46 patients (43%); mid myocardial enhancement was seen in 31.1% of cases (33 patients). No LGE pattern was seen in 57%(63 patients). Basal and mid regions were maximally involved with mid myocardial enhancement in 66% & 68.3% respectively. LV ejection fraction (p=0.002), peak aortic systolic velocity (p=0.01) and peak aortic systolic gradient (p=0.02) were the main predictors of LGE. Main predictors of primary outcome were NYHA class [OR- 13.4(2.8-26.1), p?0.001], age- 62±?9.6yrs(p=0.001), EF simpson-50.9±13%(p??0.001), LGE[OR 2.8 (1.27-6.47),p=0.01], number of segments involved [2.37±2.1,P?0.001] & CMR LV mass (151.73±32gms, p=0.007). LGE predicted heart failure/hospitalization for cardiovascular cause [OR- 3.8(1.2-11.9), p=0.01] and fall in LVEF [OR- 5.8(1.5-22.5), p=0.005]. Patients with LGE had 2.87 times risk of adverse outcomes and patients with more than 3 segment LGE involvement had again increased chances for adverse outcomes. CONCLUSIONS:LGE was detected by CMR in 43% of patients with severe AS. It predicted recurrent heart failure, hospitalization for cardiovascular cause and fall in LV ejection fraction. Our study has laid a path to larger prospective studies with long term follow up to assess the prognostic impact of CMR in patients with severe AS.

Dataset Information

Verification of threshold for image intensity ratio analyses of late gadolinium enhancement magnetic resonance imaging of left atrial fibrosis in 1.5T scans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets