Gadolinium-enhanced Cardiac Magnetic Resonance Imaging Research Articles

Adaptive dynamic inference for few-shot left atrium segmentation

Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images is crucial for aiding the treatment of patients with atrial fibrillation. Few-shot learning holds significant potential for achieving accurate LA segmentation with low demand on high-cost labeled LGE CMR data and fast generalization across different centers. However, accurate LA segmentation with few-shot learning is a challenging task due to the low-intensity contrast between the LA and other neighboring organs in LGE CMR images. To address this issue, we propose an Adaptive Dynamic Inference Network (ADINet) that explicitly models the differences between the foreground and background. Specifically, ADINet leverages dynamic collaborative inference (DCI) and dynamic reverse inference (DRI) to adaptively allocate semantic-aware and spatial-specific convolution weights and indication information. These allocations are conditioned on the support foreground and background knowledge, utilizing pixel-wise correlations, for different spatial positions of query images. The convolution weights adapt to different visual patterns based on spatial positions, enabling effective encoding of differences between foreground and background regions. Meanwhile, the indication information adapts to the background visual pattern to reversely decode foreground LA regions, leveraging their spatial complementarity. To promote the learning of ADINet, we propose hierarchical supervision, which enforces spatial consistency and differences between the background and foreground regions through pixel-wise semantic supervision and pixel-pixel correlation supervision. We demonstrated the performance of ADINet on three LGE CMR datasets from different centers. Compared to state-of-the-art methods with ten available samples, ADINet yielded better segmentation performance in terms of four metrics.

Advancements in the Study of Dilated Cardiomyopathy Biomarkers

Dilated cardiomyopathy (DCM) is the leading cause of sudden cardiac death (SCD) and heart failure (HF) in children and adults worldwide, and it is also the main indication of heart transplantation. DCM remains a significant cause of heart transplantation despite recent improvements in treatment, which is related to the enormous financial strain placed on the world's healthcare system. Therefore, determining the prognosis of individuals with DCM is crucial for providing them with a customized course of treatment. Genetic testing, microvolt T-wave alternation, and late gadolinium-enhanced cardiac magnetic resonance imaging have developed into potent methods for predicting the occurrence of sudden cardiac death and optimizing patient selection. Despite the availability of numerous novel diagnostic techniques, further tests are still required to enhance or partially substitute current methods of risk categorization. As a result, biomarkers are robust and straightforward tools that can identify people at risk for unfavorable cardiovascular events. Thus, this article aims to review the state of the research on biomarkers for DCM.

DSBAV-Net: Depthwise Separable Bottleneck Attention V-Shaped Network with Hybrid Convolution for Left Atrium Segmentation

AbstractAccurate and precise segmentation of the left atrium (LA) is crucial in the early diagnosis and treatment of atrial fibrillation (AF), which is the most common heart rhythm disease in cases. The size of fibrotic tissue in patients with AF is based on manual examination of images obtained from the gadolinium-enhanced cardiac magnetic resonance imaging (MRI) technique. However, manual examination of the acquired images is time-consuming and has many difficulties, such as LA thickness between observers and resolution according to MR devices. To overcome the challenges of manual segmentation of images obtained from MRI devices, end-to-end, fully automated deep learning-based segmentation architectures have become extremely important today. In this study, an encoder–decoder-based V-shaped deep learning architecture is proposed for precise segmentation of LA. In the proposed architecture, standard convolution and depthwise separable convolution are used together. Thus, sparsely connected blocks with fewer parameters and deeply separable convolutions learn the feature representations better, increasing the robustness of the model. In addition, the bottleneck attention module has been added to each encoder layer, allowing the network to learn which features to focus on and which features to suppress in images by attention mapping channel and spatially. The proposed architecture obtained 0.915 dice and 0.844 Jaccard scores in the STACOM 2018 challenge dataset. The obtained results draw attention to the robustness of the model.

Correlation Between Corrected TIMI Frame Count with the Extent of Myocardial Fibrosis on ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention

Background: Microvascular injury after primary percutaneous coronary intervention (PPCI) reperfusion contributes to necrosis propagation. Corrected TIMI Frame Count (CTFC) is a surrogate marker of microvascular dysfunction and can stratify in-hospital mortality risk in patients with final TIMI flow 3. The extent of myocardial fibrosis after STEMI is associated with a higher incidence of major cardiovascular events. This study aimed to determine the relationship between CTFC in the infarct-related artery and myocardial fibrosis area based on cardiac magnetic resonance (CMR) in STEMI patients undergoing PPCI.Methods: This retrospective cohort study included 31 STEMI patients who had undergone PPCI and CMR examination between days 60 and 75 after STEMI as the sample. CTFC was measured in the infarct-related artery from post-PPCI angiogram recordings. Myocardial fibrosis area was measured from late gadolinium enhancement CMR (LGE-CMR) imaging results.Results: In this study, the mean age was 51.61±10.49 years, 90.3% were male, non-anterior infarction location was 58.1%, mean total ischemic time was 489.48±228.33 minutes, mean CTFC was 27.4±9.3 frames, and mean myocardial fibrosis was 18.33±7.87%. There was no significant correlation found between CTFC and myocardial fibrosis (p=0.530), however total ischemic time had a positive and significant correlation with myocardial fibrosis (p=0.025, r=0.403).Conclusion: CTFC in the infarct-related artery is not correlated with myocardial fibrosis area in STEMI patients undergoing PCI.

Prognostic role of atrial fibrillation in patients with non-compaction cardiomyopathy

Aim. To evaluate the prognostic role of atrial fibrillation (AF) as a predictor of adverse events and outcomes in a cohort of patients with non-compaction cardiomyopathy (NCCM).Material and methods. We examined 216 patients with NCCM (140 men and 76 women, median age, 39 (30; 50) years). In addition to traditional clinical methods, all patients underwent late gadolinium-enhanced cardiac magnetic resonance imaging (MRI). The endpoints of the study included progression of NYHA class III heart failure (HF) with the need for hospitalization, ventricular tachyarrhythmias, and thromboembolic events (TEEs).Results. There were 54 out of 216 (23,6%) patients with AF, of which 18 had paroxysmal AF, 16 — persistent AF, and 20 — permanent AF. During the follow-up period (median follow-up, 36 (6; 72) months), 98 out of 216 (45,4%) patients with NCCM had adverse events and outcomes as follows: 16 (7,4%) had ventricular tachyarrhythmias, of which 12 (5,6%) — sudden cardiac death with successful resuscitation and implantation of an implantable cardioverter-defibrillator; 62 (28,7%) patients had NYHA III-IV class HF progression; 20 (9,3%) patients had TEEs. The rate of adverse cardiac events was significantly higher in patients with AF (74,1% vs 35,8%, χ2=23,93, p<0,001) compared with patients without AF, including the incidence of TEEs (20,4% vs 5,6%, χ2=10,58, p=0,002) and HF progression to class III (46,3% vs 22,8%, χ2=10,9, p=0,002).Multivariate analysis showed that the following most significant predictors of HF progression risk: left ventricular ejection fraction (LVEF) <50% according to cardiac MRI (hazard ratio (HR), 95,8; 95% confidence interval (CI), 10,2 -898,6; p=0,0001), presence of AF (HR, 8,2; 95% CI, 2,2-31,3; p=0,0022) and left atrial volume index (LAVI) >43 ml/m2 (HR, 5,2; 95% CI, 2,1-12,8; p=0,0004); predictors of TEE risk were the presence of AF (HR, 6,5; 95% CI, 2,0-20,8; p=0,0020) and LAVI >43 ml/m2 (HR, 6,0; 95% CI, 1,8-19,7; p=0,036). No association of AF with ventricular tachyarrhythmias was found in the study cohort of patients with NCCM. Predictors of ventricular tachyarrhythmias were LVEF <50% (HR, 4,5; 95% CI, 2,950,4; p=0,0241) and the presence of non-sustained ventricular tachycardia (HR, 3,5; 95% CI, 1,3-9,3 p=0,0139).Conclusion. The present study shows that, along with the traditional predictor of adverse events in patients with NCCM (LVEF <50%), the identified additional predictors (AF and LAVI >43 ml/m2) can be used to identify patients at high risk of complicated NCCM for the timely prevention and treatment.

Electrocardiographic Associations of Cardiac Biomarkers and Cardiac Magnetic Resonance Measures of Fibrosis in the Multiethnic Study of Atherosclerosis (MESA)

Abnormalities in myocardial substrate, including diffuse and replacement fibrosis, increase the risk of cardiovascular disease (CVD).Data are sparse on whether electrocardiogram (ECG) measures, coupled with circulating biomarkers, may aid in identifying cardiac fibrosis. This study aimed to determine whether 12-lead ECG and biomarkers together augment the prediction of cardiac fibrosis in participants who are free of known CVD. This is a cross-sectional analysis in the MESA (Multiethnic Study of Atherosclerosis) study at visit 5 (2010 to 2012), with measurements of biomarkers (cardiac troponin T and growth differentiation factor-15), gadolinium-enhanced cardiac magnetic resonance imaging, and ECG.Logistic regression associations of ECG measures with cardiac magnetic resonance surrogates of fibrosis (highest quartile extracellular volume [interstitial fibrosis] and late gadolinium enhancement [replacement fibrosis]) were adjusted for demographics and risk factors.Using the C-statistic, we evaluated whether adding ECG measures and biomarkers to clinical characteristics improved the prediction of either type of fibrosis. There were 1,170 eligible participants (aged 67.1 ± 8.6years).Among the ECG measures, QRS duration (odds ratio [OR] 1.41 per 10ms, 95% confidence interval [CI] 1.10 to 1.81), major ST-T abnormalities (OR 3.03, 95%CI 1.20, 7.65),and abnormal QRS-T angle (OR 6.32, 95%CI 3.00, 13.33) were associated with replacement fibrosis, whereas only abnormal QRS-T angle (OR 3.05, 95%CI,1.69, 5.48) was associated with interstitial fibrosis.ECG markers, in addition to clinical characteristics, improved the prediction of replacement fibrosis (p=0.002) but not interstitial fibrosis. The addition of cardiac troponin T and growth differentiation factor-15 to the ECG findings did not significantly improve the model discrimination for either type of cardiac fibrosis.In CVD free participants, simple ECG measures are associated with replacement fibrosis and interstitial fibrosis. The addition of these measures improves identification of replacement but not interstitial fibrosis. These findings may help refine the identification of myocardial scar in the general population.

Cryoballoon-Induced Circumferential Pulmonary Vein Fibrosis, Assessed by Late Gadolinium-Enhancement Cardiac Magnetic Resonance Imaging, and Its Correlation with Clinical Atrial Fibrillation Recurrence.

Prior studies evaluating post-atrial fibrillation (AF) ablation pulmonary vein (PV) ostial gaps via magnetic resonance imaging (MRI) have shown circumferential PV fibrosis in a minority of patients, and their correlation with AF recurrence was weak. These studies were mostly based on radio-frequency AF ablations. We aimed to assess cryoballoon ablation-induced PV fibrosis via MRI and its correlation with AF recurrence. This was a prospective study of consecutive patients with symptomatic AF who underwent pre- and post-ablation MRI to assess baseline and ablation-induced fibrosis, respectively. Post-ablation PV gaps were assessed by new semi-quantitative visual analysis assisted by computerized ADAS analysis. AF recurrence monitored via multiple ECGs and event monitoring at 6 and 12 months post ablation. Nineteen patients with 80 PVs were included, age 56 ± 11, with paroxysmal and persistent AF in 17/19 and 2/19 patients, respectively. Baseline MRI showed minimal LA fibrosis. All patients underwent successful cryoballoon PV electrical isolation. Post-ablation MRI revealed circumferential PV fibrosis among 63/80 (78.8%) PVs and partial fibrosis with major gaps among 17/80 (21.2%) PVs. AF recurred within one year in 5/9 (55.5%) patients with partial PV fibrosis, while no AF recurred among the 10 patients in whom all PVs had circumferential fibrosis (p < 0.01). Similarly, there were significantly more PVs without circumferential fibrosis (due to major gaps) among patients with AF recurrence as compared with patients without AF recurrence (42.9% vs. 13.5%; p < 0.01). Cryoballoon AF ablation results in circumferential PV fibrosis in the majority of PVs, as assessed by a new clinically relevant MRI-LGE analysis. Significant correlation was found between major PV gaps on post-ablation MRI and AF recurrence, suggesting that MRI might have the ability to predict AF recurrence.

The extent of late gadolinium enhancement predicts mortality, sudden death and major adverse cardiovascular events in patients with nonischaemic cardiomyopathy: a systematic review and meta-analysis

To conduct a systematic review and meta-analysis with the objective of evaluating the prognostic value of extent of myocardial fibrosis by late gadolinium-enhanced cardiac magnetic resonance imaging (CMR) in non-ischaemic dilated cardiomyopathy (NICM). The databases PubMed, EMBASE, and Google Scholar were searched for studies that investigated the prognostic value of quantification of late gadolinium enhancement (LGE) in patients with NICM. Unadjusted and adjusted hazard ratios (HRs) of uniformly defined predictors were pooled for meta-analysis. Fourteen studies were retrieved from 884 publications for this systematic review and meta-analysis. In total, 4,336 patients (mean age 51.2 years; mean follow-up 35.1 months) were included in the analysis. Meta-analysis showed the extent of LGE was associated with an increased risk of all-cause mortality (HR: 1.07/1% LGE; 95% confidence interval [CI]: 1.03-1.11; p=0.0003), composite arrhythmic endpoint (HR: 1.09/1% LGE; 95% CI: 1.03-1.15; p=0.002) and major adverse cardiovascular events (MACE; HR: 1.06/1% LGE; 95% CI: 1.02-1.11; p=0.005). After adjusting for baseline characteristics, the higher extent of LGE remained associated with the risk of all-cause mortality (HRadjusted: 1.07/1% LGE; 95% CI: 1.00-1.14; p=0.04), also strongly associated with the risk of composite arrhythmic endpoint (HRadjusted: 1.07; 95% CI: 1.02-1.012; p=0.004) and MACE (HRadjusted: 1.04; 95% CI: 1.01-1.08; p=0.005). Extent of LGE in CMR predicts all-cause mortality, arrhythmic events, and MACE. Collectively, these findings emphasise that extent of LGE by CMR may have value for optimising current predictive models for clinical events or mortality in patients with NICM.

BMAnet: Boundary Mining With Adversarial Learning for Semi-Supervised 2D Myocardial Infarction Segmentation.

Automatic segmentation of myocardial infarction (MI) regions in late gadolinium-enhanced cardiac magnetic resonance images is an essential step in the computed diagnosis of myocardial infarction. Most of the current myocardial infarction region segmentation methods are based on fully supervised deep learning. However, cardiologists' annotation of myocardial infarction regions in cardiac magnetic resonance images during the diagnosis process is time-consuming and expensive. This paper proposes a semi-supervised myocardial infarction segmentation. It consists of two models: 1) a boundary mining model and 2) an adversarial learning model. The boundary mining model can solve the boundary ambiguity problem by enlarging the gap between the foreground and background features, thus segmenting the myocardial infarction region accurately. The adversarial learning model can make the boundary mining model learn from additional unlabeled data by evaluating the segmentation performance and providing pseudo supervision, which significantly increases the robustness of the boundary mining model. We conduct extensive experiments on an in-house myocardial magnetic resonance dataset. The experimental results on six evaluation metrics demonstrate that our method achieves excellent results in myocardial infarction segmentation and outperforms the state-of-the-art semi-supervised methods.

Comprehensive information integration network for left atrium segmentation on LGE CMR images

Automatic and accurate segmentation of the left atrium (LA) is a prerequisite for quantifying the LA, and effective LA segmentation is helpful for the clinical diagnosis of patients with atrial fibrillation. The existing LA segmentation methods are prone to a lack of structural prediction of the pulmonary vein and mitral valve, and the structure of the LA is prone to over- and under-segmentation. To solve these problems, we propose a comprehensive information integration network (CII-Net) to segment the LA from late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) images. The CII-Net integrates multiscale information from the input stage, high-level semantic information from the bottleneck stage, interaction information from the encoding and decoding stages, and information from the output stage, which can more completely capture the comprehensive image characteristics. We conducted experiments on 154 LGE CMR cases with atrial fibrillation proposed by the organizer of the 2018 atrial segmentation challenge. Compared with state-of-the-art methods, the proposed CII-Net obtains an average DICE score of 91.9%, average Jaccard score of 85.1%, average Hausdorff distance of 5.924 mm, and average symmetric surface distance of 0.993 mm without post-processing, demonstrating the potential for clinical application.

Detection of inflammation using cardiac positron emission tomography for evaluation of ventricular arrhythmias: An institutional experience

The use of cardiac positron emission tomography-computed tomography (PET-CT) is increasingly used for the detection of underlying inflammation in patients with ventricular arrhythmias (ventricular tachycardia/ventricular fibrillation [VT/VF]), but the role of PET-CT remains undefined, particularly for patients who do not meet Task Force criteria for sarcoidosis. The purpose of this study was to determine the utility of PET-CT for clinical evaluation of VT/VF in patients with nonischemic cardiomyopathy. Consecutive patients with nonischemic cardiomyopathy and VT/VF who underwent cardiac PET-CT to detect inflammation between 2012 and 2019 were analyzed for baseline demographic characteristics, imaging results, and outcomes. Patients with known sarcoidosis or other conditions requiring immunosuppressive therapy were excluded. PET-CT was performed in 133 patients with mean age 56.3 ± 13.5 years and left ventricular ejection fraction 43% ± 16.1%, with evidence of myocardial inflammation detected in 32 (23.5%). Patients with myocardial inflammation were managed conservatively with medical therapy including immunosuppressive agents. Ten patients with myocardial inflammation ultimately required catheter ablation for ongoing arrhythmias. There was no significant difference in arrhythmia recurrence between PET-positive and PET-negative groups (37.5% vs 32.4%; P = .43) or in time to recurrence (P = .26), in spite of the disparate management strategies. Gadolinium-enhanced cardiac magnetic resonance imaging was performed in 96 patients (72%); however, magnetic resonance imaging did not detect 31% of cases with active inflammation that were otherwise detected on PET-CT. The use of PET-CT significantly improves the detection of underlying myocardial inflammation contributing to ventricular arrhythmias. Management of these patients with immunosuppressive medical therapy is effective for arrhythmia control and may obviate the need for invasive ablation procedures in some patients.

Comparison of Diagnostic Value Between STE+LDDSE and CMR-FT for Evaluating Coronary Microvascular Obstruction in Post-PCI Patients for STEMI.

BackgroundCoronary microvascular obstruction (CMVO) is closely associated with poor prognosis of ST-segment elevation myocardial infarction (STEMI) patients. However, data showing the comparison between cardiac magnetic resonance feature tracking (CMR-FT) and speckle tracking echocardiography (STE) combined with low-dose dobutamine stress echocardiography (LDDSE) in evaluating CMVO was scarcely available. We aimed to explore and compare the predictive value between CMR-FT and STE+LDDSE in detecting CMVO.MethodsSixty-one STEMI patients were executed cardiac magnetic resonance and echocardiography within the first 5–7 days after primary percutaneous coronary intervention (PCI). The myocardial strain analysis was performed in STE, STE+LDDSE, and CMR-FT, and strain parameters included radial strain (RS), circumferential strain (CS), and longitudinal strain (LS). ROC curves were performed to predict infarcted myocardium segments with CMVO.ResultsFinally, 324 infarcted myocardium segments were analyzed, including 100 infarcted segments with CMVO and 224 segments without CMVO by the gold standard assessment of late gadolinium-enhancement cardiac magnetic resonance imaging (LGE-CMR). The results showed that CS was generally superior to RS and LS in identifying CMVO. CS in CMR-FT facilitated the detection of CMVO, with a sensitivity, specificity, and accuracy of 78.00%, 81.25%, and 80.25%, respectively. The sensitivity, specificity, and accuracy of CS in STE combined with LDDSE were better than STE alone (76.00% vs 60.00%, 79.91% vs 64.29%, and 78.70% vs 62.96%, P < 0.05). In addition, CMR-FT is not superior to STE+LDDSE for detection of CMVO (P > 0.05).ConclusionLow-dose dobutamine can improve the clinical value of STE for evaluating CMVO in STEMI patients. Compared with CMR-FT, STE+LDDSE might be a better choice for STEMI patients because of its safety, convenience, and low-cost.

Assessment of left atrial fibrosis progression in canines following rapid ventricular pacing using 3D late gadolinium enhanced CMR images.

BackgroundAtrial fibrillation (AF) is associated with extracellular matrix (ECM) remodelling and often coexists with myocardial fibrosis (MF); however, the causality of these conditions is not well established.ObjectiveWe aim to corroborate AF to MF causality by quantifying left atrial (LA) fibrosis in cardiac magnetic resonance (CMR) images after persistent rapid ventricular pacing and subsequent AF using a canine model and histopathological validation.MethodsTwelve canines (9 experimental, 3 control) underwent baseline 3D LGE-CMR imaging at 3T followed by insertion of a pacing device and 5 weeks of rapid ventricular pacing to induce AF (experimental) or no pacing (control). Following the 5 weeks, pacing devices were removed to permit CMR imaging followed by excision of the hearts and histopathological imaging. LA myocardial segmentation was performed manually at baseline and post-pacing to permit volumetric %MF quantification using the image intensity ratio (IIR) technique, wherein fibrosis was defined as pixels > mean LA myocardium intensity + 2SD.ResultsVolumetric %MF increased by an average of 2.11 ± 0.88% post-pacing in 7 of 9 experimental dogs. While there was a significant difference between paired %MF measurements from baseline to post-pacing in experimental dogs (P = 0.019), there was no significant change in control dogs (P = 0.019 and P = 0.5, Wilcoxon signed rank tests). The median %MF for paced animals was significantly greater than that of non-paced dogs at the 5-week post-insertion time point (P = 0.009, Mann Whitney U test). Histopathological imaging yielded an average %MF of 19.42 ± 4.80% (mean ± SD) for paced dogs compared to 1.85% in one control dog.ConclusionPersistent rapid ventricular pacing and subsequent AF leads to an increase in LA fibrosis volumes measured by the IIR technique; however, quantification is limited by inherent image acquisition parameters and observer variability.

Simple Predictors for Cardiac Fibrosis in Patients with Type 2 Diabetes Mellitus: The Role of Circulating Biomarkers and Pulse Wave Velocity.

Cardiac fibrosis is the basis of structural and functional disorders in patients with diabetes mellitus (T2DM). A wide range of laboratory and instrumental methods is used for its prediction. The study aimed to identify simple predictors of cardiac fibrosis in patients with T2DM based on the analysis of circulating fibrosis biomarkers and arterial stiffness. The study included patients with T2DM (n = 37) and cardiovascular risk factors (RF, n = 27) who underwent ECHO, cardiac magnetic resonance imaging (MRI), pulse wave analysis (PWV), reactive hyperemia (RH), peripheral arterial tonometry, carotid ultrasonography, and assessment of serum fibrosis biomarkers. As a control group, 15 healthy subjects were examined. Left ventricular concentric hypertrophy was accompanied by an increased serum galectin-3 level in T2DM patients. There was a relationship between the PICP and HbA1c levels in both main groups (R2 = 0.309; p = 0.014). A negative correlation between PICP level and the global longitudinal strain (GLS) was found (r = −0.467; p = 0.004). The RH index had a negative correlation with the duration of diabetes (r = −0.356; p = 0.03), the carotid-femoral PWV (r = −0.371; p = 0.024), and the carotid intima-media thickness (r = −0.622; p < 0.001). The late gadolinium-enhanced (LGE) cardiac MRI was detected in 22 (59.5%) T2DM and in 4 (14.85%) RF patients. Diabetes, its baseline treatment with metformin, HbA1c and serum TIMP-1 levels, and left ventricle hypertrophy had moderate positive correlations with LGE findings (p < 0.05). Using the multivariate regression analysis, increased TIMP-1 level was identified as an independent factor associated with cardiac fibrosis.

Dark-blood late gadolinium-enhancement cardiac magnetic resonance imaging for myocardial scar detection based on simplified timing scheme: single-center experience in patients with suspected coronary artery disease.

This study aims to examine scar detectability using dark-blood late gadolinium enhancement (LGE) with simplified timing scheme and fixed parameters comparing to two conventional bright-blood approaches in patients with known or suspected coronary artery disease. Three LGE techniques were performed in all patients with known or suspected coronary artery disease at 3 T: dark blood two-dimensional (2D) phase-sensitive inversion recovery (PSIR) preceded with a T2-preparation pulse (DB-LGE), conventional three-dimensional (3D) gradient-echo inversion recovery (3D-IR) and conventional 2D PSIR. Timing parameters in DB-LGE were tested in five clinically confirmed coronary artery disease patients with scars and fixed for the rest of the study. Two independent readers evaluated images at both patient and segment levels. Image quality and contrast ratio between scar and adjacent tissues were assessed. Concordance between the three techniques and detection rate based on expert consensus were reported. Forty-six patients were recruited in the study (average age 66.8 years, 69.6% male). DB-LGE demonstrated superior image quality (P=0.001 vs. 3D-IR) and scar-to-blood contrast ratio (P<0.001 vs. 3D-IR and PSIR). Among 41 patients with suspected coronary artery disease, myocardial scar was present in 30 patients (73.2%), all detected by DB-LGE, yielding a detection rate of 100% compared to 93.3% and 96.7% for bright-blood 3D-IR and PSIR. For subendocardial scar detection among 656 segments, DB-LGE had a detection rate of 99.4% compared to 57.8% for 3D-IR and 61.0% for PSIR (both P<0.001). DB-LGE improves detection of myocardial scar compared with conventional bright-blood LGE techniques, particularly of subendocardial scar.

JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets.

Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive attention cascade to automatically correlate the segmentation tasks of the unbalanced atrial targets. The adaptive attention cascade mainly models the inclusion relationship of the two unbalanced atrial targets, where the estimated LA acts as the attention map to adaptively focus on the small atrial scars roughly. Then, an adversarial regularization is applied to the segmentation tasks of the unbalanced atrial targets for making a consistent optimization. It mainly forces the estimated joint distribution of LA and atrial scars to match the real ones. We evaluated the performance of our JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the state-of-the-art methods, our proposed approach yielded better segmentation performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and 0.821 for LA and atrial scars, respectively), which indicated the effectiveness of our proposed approach for segmenting unbalanced atrial targets.

Quantification of left atrial fibrosis by 3D late gadolinium-enhanced cardiac magnetic resonance imaging in patients with atrial fibrillation: impact of different analysis methods.

AimsVarious methods and post-processing software packages have been developed to quantify left atrial (LA) fibrosis using 3D late gadolinium-enhancement cardiac magnetic resonance (LGE-CMR) images. Currently, it remains unclear how the results of these methods and software packages interrelate.Methods and resultsForty-seven atrial fibrillation (AF) patients underwent 3D-LGE-CMR imaging prior to their AF ablation. LA fibrotic burden was derived from the images using open-source CEMRG software and commercially available ADAS 3D-LA software. Both packages were used to calculate fibrosis based on the image intensity ratio (IIR)-method. Additionally, CEMRG was used to quantify LA fibrosis using three standard deviations (3SD) above the mean blood pool signal intensity. Intraclass correlation coefficients were calculated to compare LA fibrosis quantification methods and different post-processing software outputs. The percentage of LA fibrosis assessed using IIR threshold 1.2 was significantly different from the 3SD-method (29.80 ± 14.15% vs. 8.43 ± 5.42%; P < 0.001). Correlation between the IIR-and SD-method was good (r = 0.85, P < 0.001) although agreement was poor [intraclass correlation coefficient (ICC) = 0.19; P < 0.001]. One-third of the patients were allocated to a different fibrosis category dependent on the used quantification method. Fibrosis assessment using CEMRG and ADAS 3D-LA showed good agreement for the IIR-method (ICC = 0.93; P < 0.001).ConclusionsBoth, the IIR1.2 and 3SD-method quantify atrial fibrotic burden based on atrial wall signal intensity differences. The discrepancy in the amount of LA fibrosis between these methods may have clinical implications when patients are classified according to their fibrotic burden. There was no difference in results between post-processing software packages to quantify LA fibrosis if an identical quantification method including the threshold was used.

Analyzing the Role of Repolarization Gradients in Post-infarct Ventricular Tachycardia Dynamics Using Patient-Specific Computational Heart Models.

Aims: Disease-induced repolarization heterogeneity in infarcted myocardium contributes to VT arrhythmogenesis but how apicobasal and transmural (AB-TM) repolarization gradients additionally affect post-infarct VT dynamics is unknown. The goal of this study is to assess how AB-TM repolarization gradients impact post-infarct VT dynamics using patient-specific heart models.Method: 3D late gadolinium-enhanced cardiac magnetic resonance images were acquired from seven post-infarct patients. Models representing the patient-specific scar and infarct border zone distributions were reconstructed without (baseline) and with repolarization gradients along both the AB-TM axes. AB only and TM only models were created to assess the effects of each ventricular gradient on VT dynamics. VTs were induced in all models via rapid pacing.Results: Ten baseline VTs were induced. VT inducibility in AB-TM models was not significantly different from baseline (p>0.05). Reentry pathways in AB-TM models were different than baseline pathways due to alterations in the location of conduction block (p<0.05). VT exit sites in AB-TM models were different than baseline VT exit sites (p<0.05). VT inducibility of AB only and TM only models were not significantly different than that of baseline (p>0.05) or AB-TM models (p>0.05). Reentry pathways and VT exit sites in AB only and TM only models were different than in baseline (p<0.05). Lastly, repolarization gradients uncovered multiple VT morphologies with different reentrant pathways and exit sites within the same structural, conducting channels.Conclusion: VT inducibility was not impacted by the addition of AB-TM repolarization gradients, but the VT reentrant pathway and exit sites were greatly affected due to modulation of conduction block. Thus, during ablation procedures, physiological and pharmacological factors that impact the ventricular repolarization gradient might need to be considered when targeting the VTs.

Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy

Delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging requires novel and time-efficient approaches to characterize the myocardial substrate associated with ventricular arrhythmia in patients with ischemic cardiomyopathy. Using a translational approach in pigs and patients with established myocardial infarction, we tested and validated a novel 3D methodology to assess ventricular scar using custom transmural criteria and a semiautomatic approach to obtain transmural scar maps in ventricular models reconstructed from both 3D-acquired and 3D-upsampled-2D-acquired LGE-CMR images. The results showed that 3D-upsampled models from 2D LGE-CMR images provided a time-efficient alternative to 3D-acquired sequences to assess the myocardial substrate associated with ischemic cardiomyopathy. Scar assessment from 2D-LGE-CMR sequences using 3D-upsampled models was superior to conventional 2D assessment to identify scar sizes associated with the cycle length of spontaneous ventricular tachycardia episodes and long-term ventricular tachycardia recurrences after catheter ablation. This novel methodology may represent an efficient approach in clinical practice after manual or automatic segmentation of myocardial borders in a small number of conventional 2D LGE-CMR slices and automatic scar detection.

ST-segment resolution as a marker for severe myocardial fibrosis in ST-segment elevation myocardial infarction

ObjectiveTo investigate the relationship between ST-segment resolution (STR) and myocardial scar thickness after percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI).MethodsForty-two STEMI patients with single-branch coronary artery stenosis or occlusion were enrolled. ST-segment elevations were measured at emergency admission and at 24 h after PCI. Late gadolinium-enhanced cardiac magnetic resonance imaging (CMR-LGE) was performed 7 days after PCI to evaluate myocardial scars. Statistical analyses were performed to assess the utility of STR to predict the development of transmural (> 75%) or non-transmural (< 75%) myocardial scars, according to previous study.ResultsThe sensitivity and specificity of STR for predicting transmural scars were 96% and 88%, respectively, at an STR cut-off value of 40.15%. The area under the curve was 0.925. Multivariate logistic proportional hazards regression analysis disclosed that patients with STR < 40.15% had a 170.90-fold higher probability of developing transmural scars compared with patients with STR ≥ 40.15%. Pearson correlation and linear regression analyses showed STR percentage was significantly associated with myocardial scar thickness and size.ConclusionSTR < 40.15% at 24 h after PCI may provide meaningful diagnostic information regarding the extent of myocardial scarification in STEMI patients.