Infarct quantification with cardiovascular magnetic resonance using "standard deviation from remote" is unreliable: validation in multi-centre multi-vendor data

Automated fibrosis segmentation from wideband post-contrast T 1 ∗ $$ {T}_1^{\ast } $$ mapping in an animal model of ischemic heart disease with implantable cardioverter-defibrillators

PURPOSE

Post-contrastT 1 ∗ $$ {T}_1^{\ast } $$ mapping has proven promising for automated scar segmentation in subjects without ICDs, but this has not been implemented in patients with ICDs. We introduce an automated cluster-based thresholding method forT 1 ∗ $$ {T}_1^{\ast } $$ maps with an ICD present and compare it to manually tuned thresholding of synthetic LGE images with an ICD present and standard LGE without an ICD present.

METHODS

Seven swine received an ischemia-reperfusion myocardial infarction and were imaged at 3 T 4-5 weeks post-infarct with and without an ICD. Mapping-based thresholding was performed using synthetic LGE and artifact-corrected cluster-thresholding methods, both employing connected component filtering. Standard pixel signal intensity thresholding was performed on the conventional LGE without an ICD. Volumetric accuracy is relative to conventional LGE and Dice similarity between SynLGE and cluster-based segmentations were evaluated.

RESULTS

No statistical significance was observed between LGE volumes without an ICD and both SynLGE and artifact-corrected cluster-threshold volumes with an ICD, when using connected component filtering. Additionally, Dice alignment between SynLGE and cluster-thresholding was high for healthy myocardium (0.96), dense scar (0.83), and dense scar union gray zone (0.91) when artifact correction and connected component filtering were implemented.

CONCLUSION

Clustering ofT 1 ∗ $$ {T}_1^{\ast } $$ maps holds promise for a reproducible approach to scar segmentation in the presence of ICDs.

Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. However, the exact quantification of those markers of myocardial infarct severity remains challenging and very time-consuming. As LGE distribution patterns can be quite complex and hard to delineate from the blood pool or epicardial fat, automatic segmentation of LGE CMR images is challenging. In this work, we propose a cascaded framework of two-dimensional and three-dimensional convolutional neural networks (CNNs) which enables to calculate the extent of myocardial infarction in a fully automated way. By artificially generating segmentation errors which are characteristic for 2D CNNs during training of the cascaded framework we are enforcing the detection and correction of 2D segmentation errors and hence improve the segmentation accuracy of the entire method. The proposed method was trained and evaluated on two publicly available datasets. We perform comparative experiments where we show that our framework outperforms state-of-the-art reference methods in segmentation of myocardial infarction. Furthermore, in extensive ablation studies we show the advantages that come with the proposed error correcting cascaded method. The code of this project is publicly available at https://github.com/matthi99/EcorC.git.

Lumos: Software for Multi-level Multi-reader Comparison of Cardiovascular Magnetic Resonance Late Gadolinium Enhancement Scar Quantification

Cardiovascular magnetic resonance imaging (CMR) offers state-of-the-art myocardial tissue differentiation. The CMR technique late gadolinium enhancement (LGE) currently provides the noninvasive gold standard for the detection of myocardial fibrosis. Typically, thresholding methods are used for fibrotic scar tissue quantification. A major challenge for standardized CMR assessment is large variations in the estimated scar for different methods. The aim was to improve quality assurance for LGE scar quantification, a multi-reader comparison tool "Lumos" was developed to support quality control for scar quantification methods. The thresholding methods and an exact rasterization approach were implemented, as well as a graphical user interface (GUI) with statistical and case-specific tabs. Twenty LGE cases were considered with half of them including artifacts and clinical results for eight scar quantification methods computed. Lumos was successfully implemented as a multi-level multi-reader comparison software, and differences between methods can be seen in the statistical results. Histograms visualize confounding effects of different methods. Connecting the statistical level with the case level allows for backtracking statistical differences to sources of differences in the threshold calculation. Being able to visualize the underlying groundwork for the different methods in the myocardial histogram gives the opportunity to identify causes for different thresholds. Lumos showed the differences in the clinical results between cases with artifacts and cases without artifacts. A video demonstration of Lumos is offered as supplementary material 1. Lumos allows for a multi-reader comparison for LGE scar quantification that offers insights into the origin of reader differences.

Late gadolinium enhancement on cardiac MRI: A systematic review and meta-analysis of prognosis across cardiomyopathies

BACKGROUND

Late gadolinium enhancement (LGE) on cardiac MRI has been shown to predict adverse outcomes in a range of cardiac diseases. However, no study has systematically reviewed and analyzed the literature across all cardiac pathologies including rare diseases.

METHODS

PubMed, EMBASE and Web of Science were searched for studies evaluating the relationship between LGE burden and cardiovascular outcomes. Outcomes included all-cause mortality, MACE, sudden cardiac death, sustained VT or VF, appropriate ICD shock, heart transplant, and heart failure hospitalization. Only studies reporting hazards ratios with LGE as a continuous variable were included.

RESULTS

Of the initial 8928 studies, 95 studies (23,313 patients) were included across 19 clinical entities. The studies included ischemic cardiomyopathy (7182 patients, 33 studies), hypertrophic cardiomyopathy (5080 patients, 17 studies), non-ischemic cardiomyopathy not otherwise specified (2627 patients, 11 studies), and dilated cardiomyopathy (2345 patients, 14 studies). Among 42 studies that quantified LGE by percent myocardium, a 1 % increase in LGE burden was associated with life-threatening ventricular arrhythmias (LTVA) with a pooled hazard ratio of 1.04 (CI 1.02-1.05), and MACE with a pooled hazard ratio of 1.06 (CI 1.04-1.07). The risk of these events was similar across disease types, with minimal heterogeneity.

CONCLUSIONS

Despite mechanistic differences in myocardial injury, LGE appears to have a fairly consistent, dose-dependent effect on risk of LTVA, MACE, and mortality. These data can be applied to derive a patient's absolute risk of LTVA, and therefore can be clinically useful in informing decisions on primary prevention ICD implantation irrespective of the disease etiology.

Non-invasive pressure-volume loops provide incremental value to age, sex, and infarct size for predicting adverse cardiac remodelling after ST-elevation myocardial infarction

Aims

This study aimed to assess the predictive value of non-invasive pressure-volume (PV) loop variables by cardiovascular magnetic resonance (CMR) for determining development of adverse remodelling 3 months after primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI).

Methods and results

In total, 181 STEMI patients examined with CMR during the index admission (baseline) after primary PCI and at 3-month follow-up in The Third DANish Study of Optimal Acute Treatment of Patients with STEMI (DANAMI-3) study were retrospectively analysed. A time-varying elastance model for generating PV loops from CMR volumetry and brachial blood pressure was used to calculate contractility, arterial elastance, stroke work, potential energy, efficiency, external power, ventriculoarterial coupling, and energy per ejected volume. Adverse remodelling was seen in 28 patients (15%), defined as a concomitant increase in end-diastolic and end-systolic volume of ≥12% from baseline to follow-up. PV loop variables measured at baseline showed predictive value for adverse remodelling, independent of age, sex, and infarct size (IS) by a logistic regression analysis: contractility [odds ratio (OR) 4.6, 95% confidence interval (CI) 1.8-12.4] and efficiency (OR 1.05, 95% CI 1.00-1.11). Furthermore, females showed a higher increase in contractility between the timepoints (ΔContractility = 0.4 ± 0.4 mmHg/mL vs. 0.1 ± 0.4 mmHg/mL, P < 0.0001). A higher energy expenditure was seen at baseline in left arterial descending artery infarctions compared to left circumflex artery and right coronary artery infarctions.

Conclusion

Non-invasive PV loop variables by CMR have incremental predictive value to age, sex, and IS for determining development of adverse cardiac remodelling in STEMI patients treated with primary PCI. Furthermore, the PV loop variables show significant differences in post-infarct cardiovascular adaptation between sexes and culprit vessels.

Feasibility of relaxation along a fictitious field in the 2nd rotating frame (TRAFF2) mapping in the human myocardium at 3 T

Purpose

Evaluate the feasibility of quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame (RAFF2) relaxation times in the human myocardium at 3 T.

Methods

T RAFF 2 mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. Pixel-wiseT RAFF 2 maps were obtained after three-parameter exponential fitting. The repeatability ofT RAFF 2 ,T 1 , andT 2 was assessed in phantom via the coefficient of variation (CV) across three repetitions. In seven healthy subjects,T RAFF 2 was tested for precision, reproducibility, inter-subject variability, and image quality (IQ) on a Likert scale (1 = Nondiagnostic, 5 = Excellent). Additionally,T RAFF 2 mapping was performed in three patients with suspected cardiovascular disease, comparing it to late gadolinium enhancement (LGE), nativeT 1 ,T 2 , and ECV mapping.

Results

In phantom,T RAFF 2 showed good repeatability (CV < 1.5%) while showing no ( R 2 = 0.09 ) and high ( R 2 = 0.99 ) correlation withT 1 andT 2 , respectively. MyocardialT RAFF 2 maps exhibited overall acceptable image quality (IQ = 3.0 ± 1.0) with moderate artifact levels, stemming from off-resonances near the coronary sinus. AverageT RAFF 2 time across subjects and repetitions was 79.1 ± 7.3 ms. Good precision (7.6 ± 1.4%), reproducibility (1.0 ± 0.6%), and low inter-subject variability (10.0 ± 1.8%) were obtained. In patients, visual agreement of the infarcted area was observed in theT RAFF 2 map and LGE.

Conclusion

MyocardialT RAFF 2 quantification at 3 T was successfully achieved in a single breath-hold with acceptable image quality, albeit with residual off-resonance artifacts. Nonetheless, preliminary clinical data indicate potential sensitivity ofT RAFF 2 mapping to myocardial infarction detection without the need for contrast agents, but off-resonance artifacts mitigation warrants further investigation.

Noninvasive Pressure-Volume Loops Predict Major Adverse Cardiac Events in Heart Failure With Reduced Ejection Fraction

Background

Heart failure with reduced ejection fraction (HFrEF) is characterized by ventricular remodeling and impaired myocardial energetics. Left ventricular pressure-volume (PV) loop analysis can be performed noninvasively using cardiovascular magnetic resonance (CMR) imaging to assess cardiac thermodynamic efficiency.

Objectives

The aim of the study was to investigate whether noninvasive PV loop parameters, derived from CMR, could predict major adverse cardiac events (MACE) in HFrEF patients.

Methods

PV loop parameters (stroke work, ventricular efficiency, external power, contractility, and energy per ejected volume) were computed from CMR cine images and brachial blood pressure. The primary end point was MACE (cardiovascular death, heart failure (HF) hospitalization, myocardial infarction, revascularization, ventricular tachycardia/fibrillation, heart transplantation, or left ventricular assist device implantation within 5 years). Associations between PV loop parameters and MACE were evaluated using multivariable Cox regression.

Results

One hundred and sixty-four HFrEF patients (left ventricular ejection fraction ≤40%, age 63 [IQR: 55-70] years, 79% male) who underwent clinical CMR examination between 2004 and 2014 were included. Eighty-eight patients (54%) experienced at least one MACE after an average of 2.8 years. Unadjusted models demonstrated a significant association between MACE and all PV loop parameters (P < 0.05 for all), HF etiology (P < 0.001), left ventricular ejection fraction (P = 0.003), global longitudinal strain (P < 0.001), and N-terminal prohormone of brain natriuretic peptide level (P = 0.001). In the multivariable Cox regression analysis adjusted for age, sex, hypertension, diabetes, and HF etiology, ventricular efficiency was associated with MACE (HR: 1.04 (95% CI: 1.01-1.08) per-% decrease, P = 0.01).

Conclusions

Ventricular efficiency, derived from noninvasive PV loop analysis from standard CMR scans, is associated with MACE in patients with HFrEF.

Impact of late gadolinium enhancement image acquisition resolution on neural network based automatic scar segmentation

BACKGROUND

Automatic 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.

METHODS

Thirty-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.

RESULTS

The 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.

CONCLUSION

A 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.

Current use of cardiac MRI in animals

Cardiovascular magnetic resonance (CMR) imaging has evolved to become an indispensable tool in human cardiology. It is a non-invasive technique that enables objective assessment of myocardial function, size, and tissue composition. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of parametric mapping to explore tissue characteristics, and the emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. As veterinary cardiology continues to utilize CMR beyond the reference standard, clinical application of CMR will further expand our capabilities. This article describes the current use of CMR and adoption of more recent advances such as T1/T2 mapping in veterinary cardiology.

Late-gadolinium enhancement is common in older pediatric heart transplant recipients and is associated with lower ejection fraction

BACKGROUND

Chronic graft failure and cumulative rejection history in pediatric heart transplant recipients (PHTR) are associated with myocardial fibrosis on endomyocardial biopsy (EMB). Cardiovascular magnetic resonance imaging (CMR) is a validated, non-invasive method to detect myocardial fibrosis via the presence of late gadolinium enhancement (LGE). In adult heart transplant recipients, LGE is associated with increased risk of future adverse clinical events including hospitalization and death. We describe the prevalence, pattern, and extent of LGE on CMR in a cohort of PHTR and its associations with recipient and graft characteristics.

METHODS

This was a retrospective study of consecutive PHTR who underwent CMR over a 6-year period at a single center. Two independent reviewers assessed the presence and distribution of left ventricular (LV) LGE using the American Heart Association (AHA) 17-segment model. LGE quantification was performed on studies with visible fibrosis (LGE+). Patient demographics, clinical history, and CMR-derived volumetry and ejection fractions were obtained.

RESULTS

Eighty-one CMR studies were performed on 59 unique PHTR. Mean age at CMR was 14.8 ± 6.2 years; mean time since transplant was 7.3 ± 5.0 years. The CMR indication was routine surveillance (without a clinical concern based on laboratory parameters, echocardiography, or cardiac catheterization) in 63% (51/81) of studies. LGE was present in 36% (29/81) of PHTR. In these LGE + studies, patterns included inferoseptal in 76% of LGE + studies (22/29), lateral wall in 41% (12/29), and diffuse, involving > 4 AHA segments, in 21% (6/29). The mean LV LGE burden as a percentage of myocardial mass was 18.0 ± 9.0%. When reviewing only the initial CMR per PHTR (n = 59), LGE + patients were older (16.7 ± 2.9 vs. 12.8 ± 4.6 years, p = 0.001), with greater time since transplant (8.3 ± 5.4 vs. 5.7 ± 3.9 years, p = 0.041). These patients demonstrated higher LV end-systolic volume index (LVESVI) (34.7 ± 11.7 vs. 28.7 ± 6.1 ml/m2, p = 0.011) and decreased LV ejection fraction (LVEF) (56.2 ± 8.1 vs. 60.6 ± 5.3%, p = 0.015). There were no significant differences in history of moderate/severe rejection (p = 0.196) or cardiac allograft vasculopathy (CAV) (p = 0.709).

CONCLUSIONS

LV LGE was present in approximately one third of PHTR, more commonly in older patients with longer time since transplantation. Grafts with LGE have lower LVEF. CMR-derived LGE may aid in surveillance of chronic graft failure in PHTR.

Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images

Background

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability.

Methods

A dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy).

Results

The QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845 ± 0.075 ; VNE: 0.845 ± 0.071 ; p = n s ). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance (MAE = 0.043 , accuracy = 0.951 ) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference (p = n s ) was found when comparing the LGE and VNE test sets across all experiments.

Conclusions

The QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use.