Scar Quantification Research Articles

Predicting arrhythmia recurrence following catheter ablation for ventricular tachycardia using late gadolinium enhancement magnetic resonance imaging: Implications of varying scar ranges.

Thresholding-based analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) can create scar maps and identify corridors that might provide a reentrant substrate for ventricular tachycardia (VT). Current recommendations use a full-width-at-half-maximum approach, effectively classifying areas with a pixel signal intensity (PSI) >40% as border zone (BZ) and >60% as core. The purpose of this study was to investigate the impact of 4 different threshold settings on scar and corridor quantification and to correlate this with postablation VT recurrence. Twenty-seven patients with ischemic cardiomyopathy who had undergone catheter ablation for VT were included for retrospective analysis. LGE-CMR images were analyzed using ADAS3D LV. Scar maps were created for 4 PSI thresholds (40-60, 35-65, 30-70, and 45-55), and the extent of variation in BZ and core, as well as the number and weight of conduction corridors, were quantified. Three-dimensional representations were reconstructed from exported segmentations and used to quantify the surface area between healthy myocardium and scar (BZ + core), and between BZ and core. A wider PSI threshold was associated with an increase in BZ mass and decrease in scar (P <.001). No significant differences were observed for the total number of corridors and their mass with increasing PSI threshold. The best correlation in predicting arrhythmia recurrence was observed for PSI 45-55 (area under the curve 0.807; P = .001). Varying PSI has a significant impact on quantification of LGE-CMR parameters and may have incremental clinical value in predicting arrhythmia recurrence. Further prospective investigation is warranted to clarify the functional implications of these findings for LGE-CMR-guided ventricular ablation.

Optimized automated cardiac MR scar quantification with GAN‐based data augmentation

BackgroundThe clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tested the hypothesis that a cascaded deep learning pipeline trained with augmentation by synthetically generated data would improve model accuracy and robustness for automated scar quantification. MethodsA cascaded pipeline consisting of three consecutive neural networks is proposed, starting with a bounding box regression network to identify a region of interest around the left ventricular (LV) myocardium. Two further nnU-Net models are then used to segment the myocardium and, if present, scar. The models were trained on the data from the EMIDEC challenge, supplemented with an extensive synthetic dataset generated with a conditional GAN. ResultsThe cascaded pipeline significantly outperformed a single nnU-Net directly segmenting both the myocardium (mean Dice similarity coefficient (DSC) (standard deviation (SD)): 0.84 (0.09) vs 0.63 (0.20), p < 0.01) and scar (DSC: 0.72 (0.34) vs 0.46 (0.39), p < 0.01) on a per-slice level. The inclusion of the synthetic data as data augmentation during training improved the scar segmentation DSC by 0.06 (p < 0.01). The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0.86 (0.03) and 0.67 (0.29) for myocardium and scar, respectively. ConclusionA cascaded deep learning-based pipeline trained with augmentation by synthetically generated data leads to myocardium and scar segmentations that are similar to the manual operator, and outperforms direct segmentation without the synthetic images.

Improved myocardial scar visualization with fast free-breathing motion-compensated black-blood T1-rho-prepared late gadolinium enhancement MRI

PurposeClinical guidelines recommend the use of bright-blood late gadolinium enhancement (BR-LGE) for the detection and quantification of regional myocardial fibrosis and scar. This technique, however, may suffer from poor contrast at the blood-scar interface, particularly in patients with subendocardial myocardial infarction. The purpose of this study was to assess the clinical performance of a two-dimensional black-blood LGE (BL-LGE) sequence, which combines free-breathing T1-rho-prepared single-shot acquisitions with an advanced non-rigid motion-compensated patch-based reconstruction. Materials and methodsExtended phase graph simulations and phantom experiments were performed to investigate the performance of the motion-correction algorithm and to assess the black-blood properties of the proposed sequence. Fifty-one patients (37 men, 14 women; mean age, 55 ± 15 [SD] years; age range: 19–81 years) with known or suspected cardiac disease prospectively underwent free-breathing T1-rho-prepared BL-LGE imaging with inline non-rigid motion-compensated patch-based reconstruction at 1.5T. Conventional breath-held BR-LGE images were acquired for comparison purposes. Acquisition times were recorded. Two readers graded the image quality and relative contrasts were calculated. Presence, location, and extent of LGE were evaluated. ResultsBL-LGE images were acquired with full ventricular coverage in 115 ± 25 (SD) sec (range: 64–160 sec). Image quality was significantly higher on free-breathing BL-LGE imaging than on its breath-held BR-LGE counterpart (3.6 ± 0.7 [SD] [range: 2–4] vs. 3.9 ± 0.2 [SD] [range: 3–4]) (P <0.01) and was graded as diagnostic for 44/51 (86%) patients. The mean scar-to-myocardium and scar-to-blood relative contrasts were significantly higher on BL-LGE images (P < 0.01 for both). The extent of LGE was larger on BL-LGE (median, 5 segments [IQR: 2, 7 segments] vs. median, 4 segments [IQR: 1, 6 segments]) (P < 0.01), the method being particularly sensitive in segments with LGE involving the subendocardium or papillary muscles. In eight patients (16%), BL-LGE could ascertain or rule out a diagnosis otherwise inconclusive on BR-LGE. ConclusionFree-breathing T1-rho-prepared BL-LGE imaging with inline motion compensated reconstruction offers a promising diagnostic technology for the non-invasive assessment of myocardial injuries.

Power Modulation Echocardiography to Detect and Quantify Myocardial Scar

Myocardial scar correlates with clinical outcomes. Traditionally, late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is used to detect and quantify scar. In this prospective study using LGE CMR as reference, the authors hypothesized that nonlinear ultrasound imaging, namely, power modulation, can detect and quantify myocardial scar in selected patients with previous myocardial infarction. In addition, given the different histopathology between ischemic and nonischemic scar, a further aim was to test the diagnostic performance of this echocardiographic technique in unselected consecutive individuals with ischemic and nonischemic LGE or no LGE on CMR. Seventy-one patients with previous myocardial infarction underwent power modulation echocardiography following CMR imaging (group A). Subsequently, 101 consecutive patients with or without LGE on CMR, including individuals with nonischemic LGE, were scanned using power modulation echocardiography (group B). In group A, echocardiography detected myocardial scar in all 71 patients, with good scar volume agreement with CMR (bias = -1.9 cm3; limits of agreement [LOA], -8.0 to 4.2 cm3). On a per-segment basis, sensitivity was 82%, specificity 97%, and accuracy 92%. Sensitivity was higher in the inferior and posterior segments and lower in the anterior and lateral walls. In group B, on a per-subject basis, the sensitivity of echocardiography was 62% (91% for ischemic and 30% for nonischemic LGE), with specificity and accuracy of 89% and 72%, respectively. The bias for scar volume between modalities was 5.9 cm3, with LOA of 34.6 to 22.9 cm3 (bias = -1.9 cm3 [LOA, -11.4 to 7.6 cm3] for ischemic LGE, and bias = 18.9 cm3 [LOA, -67.4 to 29.7.6 cm3] for nonischemic LGE). Power modulation echocardiography can detect myocardial scar in both selected and unselected individuals with previous myocardial infarction and has good agreement for scar volume quantification with CMR. In an unselected cohort with nonischemic LGE, sensitivity is low.

Histopathological validation of semi-automated myocardial scar quantification techniques for dark-blood late gadolinium enhancement magnetic resonance imaging.

To evaluate the performance of various semi-automated techniques for quantification of myocardial infarct size on both conventional bright-blood and novel dark-blood late gadolinium enhancement (LGE) images using histopathology as reference standard. In 13 Yorkshire pigs, reperfused myocardial infarction was experimentally induced. At 7 weeks post-infarction, both bright-blood and dark-blood LGE imaging were performed on a 1.5 T magnetic resonance scanner. Following magnetic resonance imaging (MRI), the animals were sacrificed, and histopathology was obtained. The percentage of infarcted myocardium was assessed per slice using various semi-automated scar quantification techniques, including the signal threshold vs. reference mean (STRM, using 3 to 8 SDs as threshold) and full-width at half-maximum (FWHM) methods, as well as manual contouring, for both LGE methods. Infarct size obtained by histopathology was used as reference. In total, 24 paired LGE MRI slices and histopathology samples were available for analysis. For both bright-blood and dark-blood LGE, the STRM method with a threshold of 5 SDs led to the best agreement to histopathology without significant bias (-0.23%, 95% CI [-2.99, 2.52%], P = 0.862 and -0.20%, 95% CI [-2.12, 1.72%], P = 0.831, respectively). Manual contouring significantly underestimated infarct size on bright-blood LGE (-1.57%, 95% CI [-2.96, -0.18%], P = 0.029), while manual contouring on dark-blood LGE outperformed semi-automated quantification and demonstrated the most accurate quantification in this study (-0.03%, 95% CI [-0.22, 0.16%], P = 0.760). The signal threshold vs. reference mean method with a threshold of 5 SDs demonstrated the most accurate semi-automated quantification of infarcted myocardium, without significant bias compared to histopathology, for both conventional bright-blood and novel dark-blood LGE.

What Aspects of Phenotype Determine Risk for Sudden Cardiac Death in Pediatric Hypertrophic Cardiomyopathy?

Sudden cardiac death due to hypertrophic cardiomyopathy (HCM), is the most common autopsy-proven cause of unexpected medical death in children after infancy. This mode of death is preventable by implantation of an internal cardiac defibrillator (ICD), a procedure that has considerable morbidity in childhood patients, and even mortality. Since HCM is an inheritable disease (usually autosomal dominant, occasionally recessive), family screening may identify subjects at risk. This review summarizes published studies carried out to identify which phenotypic markers are important risk factors in childhood patients with HCM and reviews the performance of existing risk-stratification algorithms (HCM Risk-Kids, PRIMaCY) against those of single phenotypic markers. A significant proportion of HCM-patients diagnosed in childhood are associated with RASopathies such as Noonan syndrome, but a knowledge gap exists over risk stratification in this patient group. In conclusion, pediatric risk-stratification algorithms for sudden cardiac death perform better in children than adult HCM risk-stratification strategies. However, current multivariable algorithms overestimate risk substantially without having high sensitivity, and remain ‘a work in progress’. To include additional phenotypic parameters that can be reproducibly measured such as ECG-markers, e.g., ECG risk score (which has high sensitivity and negative predictive value), tissue Doppler diastolic function measurements, and quantification of myocardial scarring on cardiac magnetic resonance imaging, has the potential to improve risk-stratification algorithms. Until that work has been achieved, these are three factors that the clinician can combine with the current algorithm-calculated per cent risk, in order better to assess risk.

Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.

536 Objective Quantification of Hypertrophic Scar and Donor Scar between 2 to 7 months Post-burn Injury

IntroductionVery few objective scar evaluations have been conducted with the burn survivor population, which limits our knowledge of the clinical recovery profile of hypertrophic scars (HSc) and donor site scars (D), thus having an impact on rehabilitation intervention and treatment prioritization. The purpose of this study was to prospectively quantify the thickness, pliability, erythema and pigmentation of post-burn HSc, donor sites (normal scar) and normal skin in different anatomical locations between 2 and 7 months post-burn using objective instrumentation. A secondary objective was to compare this data with anatomic-specific normative data using the same objective instrumentation.MethodsSkin characteristics of HSc, D and N in 44 burn survivors were measured at 2, 3, 4, 5, 6 and 7 months post-burn using validated instrumentation: high-frequency ultrasound for thickness, Cutometer® to measure pliability and Mexameter® to measure erythema and pigmentation. Up to five sites were assessed on the same participant if their scar was located on the upper extremity (UE), lower extremity (LE) and trunk. A mixed model two-way analysis of variance was used to investigate the differences in means between sites at each time point and between time points at each site.ResultsThe results revealed that HSc sites were thicker than D and N at all time points and UE and trunk HSc were thicker than LE HSc at 7 months post-burn, pliability of trunk HSc did not improve over time, and UE HSc was more erythematous at 7 months compared to other anatomical sites whereas D erythema decreases from 2 to 7 months.ConclusionsScar management treatments should prioritize the UE and trunk sites which developed HSc during the first two months post-burn and continues to vary significantly from normal scar and normal skin at 7 months. Furthermore, these results provide preliminary evidence that the recovery profile of HSc varies at different anatomical sites and that thickness is the characteristic that distinguishes HSc from normal scar and normal skin.

Isotropic 3D compressed sensing (CS) based sequence is comparable to 2D-LGE in left ventricular scar quantification in different disease entities

The goal of this study was to evaluate a three-dimensional compressed sensing (3D-CS) LGE prototype sequence for the detection and quantification of myocardial fibrosis in patients with chronic myocardial infarction (CMI) and myocarditis (MYC) compared with a 2D-LGE standard. Patients with left-ventricular LGE due to CMI (n = 33) or MYC (n = 20) were prospectively recruited. 2D-LGE and 3D-CS images were acquired in random order at 1.5 Tesla. 3D-CS short axis (SAX) images were reconstructed corresponding to 2D SAX images. LGE was quantitatively assessed on patient and segment level using semi-automated threshold methods. Image quality (4-point scoring system), Contrast-ratio (CR) and acquisition times were compared. There was no significant difference between 2D and 3D sequences regarding global LGE (%) (CMI [2D-LGE: 11.4 ± 7.5; 3D-LGE: 11.5 ± 8.5; p = 0.99]; MYC [2D-LGE: 27.0 ± 15.7; 3D-LGE: 26.2 ± 13.1; p = 0.70]) and segmental LGE-extent (p = 0.63). 3D-CS identified papillary infarction in 5 cases which was not present in 2D images. 2D-LGE acquisition time was shorter (2D: median: 06:59 min [IQR: 05:51–08:18]; 3D: 14:48 min [12:45–16:57]). 3D-CS obtained better quality scores (2D: 2.06 ± 0.56 vs. 3D: 2.29 ± 0.61). CR did not differ (p = 0.63) between basal and apical regions in 3D-CS images but decreased significantly in 2D apical images (CR basal: 2D: 0.77 ± 0.11, 3D: 0.59 ± 0.10; CR apical: 2D: 0.64 ± 0.17, 3D: 0.53 ± 0.11). 3D-LGE shows high congruency with standard LGE and allows better identification of small lesions. However, the current 3D-CS LGE sequence did not provide PSIR reconstruction and acquisition time was longer.

484 The Role of Plasma Derived Purified Exosome Product (PEP) for Improved Locomotor Recovery in Spinal Cord Injured Rats

INTRODUCTION: Preclinical studies indicate that mesenchymal stem cell-derived secretomes exert immunomodulatory and neuroprotective effects in various spinal cord injury models. Exosomes as one of the secretomes, have been shown to successfully aid wound healing for the skin and following myocardial infarction. However, the effects of human plasma-derived purified exosomal products (PEP) on spinal cord injury (SCI) have not been explored to this date. METHODS: Twenty-four female rats were randomly divided into five groups. These included 2 control groups for the I.V or I.T injected PEP groups and a sham group. Groups were subject to a T9 laminectomy, followed by a moderate contusion SCI. After 24 hours, lactated ringer solution or PEP was injected through I.V or IT routes. Rats were assessed weekly for a total period of 10 weeks by an open field Basso, Beattie, and Bresnahan (BBB) motor score and a postmortem quantification of external scaring, neuronal regeneration, cavity volume, and glial scarring was carried out at the end of the follow-up period. RESULTS: Animals treated with I.V PEP or IT PEP had significant improvement within the first-week post-injury (1.05±1.2 vs. 2.16±3.15) and (1.08±3.5 vs. 4.5±0.083) respectively, with sustained improvement in BBB score at 10 weeks (11±1.16 vs. 13.8±1.90), (11.3±1.4 vs. 14±1.16). CONCLUSION: Our preliminary results suggest that I.V or IT PEP injection without immunosuppression might provide beneficial effects initially following traumatic SCI.

Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging: From Basic Concepts to Emerging Methods.

Late gadolinium enhancement (LGE) is a widely used cardiac magnetic resonance imaging (MRI) technique to diagnose a broad range of ischemic and non-ischemic cardiomyopathies. Since its development and validation against histology already more than two decades ago, the clinical utility of LGE and its span of applications have increased considerably. In this review we will present the basic concepts of LGE imaging and its diagnostic and prognostic value, elaborate on recent developments and emerging methods, and finally discuss future prospects. Continuous developments in 3 D imaging methods, motion correction techniques, water/fat-separated imaging, dark-blood methods, and scar quantification improved the performance and further expanded the clinical utility of LGE imaging. LGE imaging is the current noninvasive reference standard for the assessment of myocardial viability. Improvements in spatial resolution, scar-to-blood contrast, and water/fat-separated imaging further strengthened its position. · LGE MRI is the reference standard for the noninvasive assessment of myocardial viability. · LGE MRI is used to diagnose a broad range of non-ischemic cardiomyopathies in everyday clinical practice.. · Improvements in spatial resolution and scar-to-blood contrast further strengthened its position. · Continuous developments improve its performance and further expand its clinical utility. · Holtackers RJ, Emrich T, Botnar RM et al. Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging: From Basic Concepts to Emerging Methods. Fortschr Röntgenstr 2022; 194: 491 - 504.

AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information.

Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. The automatic segmentation is however still challenging due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an implicit surface projection to utilize the inherent correlation between LA cavity and scars. In specific, the SA scheme is embedded into a multi-task architecture to perform joint LA segmentation and scar quantification. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target in order to reduce noisy patches in the predicted segmentation. We evaluated the proposed framework on 60 post-ablation LGE MRIs from the MICCAI2018 Atrial Segmentation Challenge. Moreover, we explored the domain generalization ability of the proposed AtrialJSQnet on 40 pre-ablation LGE MRIs from this challenge and 30 post-ablation multi-center LGE MRIs from another challenge (ISBI2012 Left Atrium Fibrosis and Scar Segmentation Challenge). Extensive experiments on public datasets demonstrated the effect of the proposed AtrialJSQnet, which achieved competitive performance over the state-of-the-art. The relatedness between LA segmentation and scar quantification was explicitly explored and has shown significant performance improvements for both tasks. The code has been released via https://zmiclab.github.io/projects.html.

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 biomechanical properties of human corneal scar using acoustic radiation force optical coherence elastography.

Biomechanical properties of corneal scar are strongly correlated with many corneal diseases and some types of corneal surgery, however, there is no elasticity information available about corneal scar to date. Here, we proposed an acoustic radiation force optical coherence elastography system to evaluate corneal scar elasticity. Elasticity quantification was first conducted on ex vivo rabbit corneas, and the results validate the efficacy of our system. Then, experiments were performed on an ex vivo human scarred cornea, where the structural features, the elastic wave propagations, and the corresponding Young's modulus of both the scarred region and the normal region were achieved and based on this, 2D spatial distribution of Young's modulus of the scarred cornea was depicted. Up to our knowledge, we realized the first elasticity quantification of corneal scar, which may provide a potent tool to promote clinical research on the disorders and surgery of the cornea.

Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging—A systematic review

BackgroundAccurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success.ObjectiveWe aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation.MethodsFollowing PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods.ResultsThirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633, P = .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38, P = .09, I2 = 98% favoring learning methods.ConclusionFeasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.

Abstract 13853: A Fully Automated Deep Phenotyping of Late Gadolinium Enhancement Cardiac MRI for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Introduction: Myocardium scar volume in late gadolinium enhancement (LGE) MRI is associated with cardiac adverse outcomes in hypertrophic cardiomyopathy (HCM). However, scar quantification is time-consuming and challenging. Also, LGE contains information about scar heterogeneity, location, and shape not readily quantified by current LGE analyses. Hypothesis: Automated deep phenotyping of LGE images can extract novel imaging markers of heart failure (HF) progression in HCM. Methods: We developed a deep convolutional neural network (CNN) (Fig 1) to extract LGE image features associated with progressive HF in HCM. We developed the CNN using data for 572 HCM patients (183 F; 51±16 years) evaluated at Tufts medical center. The CNN input was 5 LGE images and the output was the probability of developing a HF outcome (defined as a change of New York Heart Association class I/II at baseline to class III/IV during follow-up). The dataset was split into training, 50%, validation, 25%, and independent testing, 25%. We trained the CNN for 50 epochs (learn-rate = 0.001, batch size = 12, class-weighted cross-entropy loss). To avoid overfitting, the model that best predicted HF outcomes in the validation cohort was used as the final model. We compared the CNN model to a single-variable logistic regression (LR) model (L2 regularization, class-weighted loss) that predicts HF outcomes using scar volume (manually quantified by an expert reader). Results: Progressive HF occurred in 96 (17%) patients during follow-up (4±2 years). In the testing cohort (n = 138), CNN correctly predicted HF in 60% of patients with positive HF outcomes (n = 22; specificity 77%, accuracy 73%, AUC = 0.73). Using the same dataset, the LR classifier showed much lower HF prediction power with AUC = 0.54 (accuracy 38%, specificity 30%). Conclusions: Deep phenotyping of LGE allows accurate prediction of progressive HF in HCM patients and outperforms classical LR models that use scar volume as a sole predictor of HF.

Effect of DrOnedarone on atrial fibrosis progression and atrial fibrillation recurrence postablation: Design of the EDORA randomized clinical trial.

BackgroundAtrial fibrillation (AF) recurrence after catheter ablation is associated with worse outcomes and quality of life. Left atrial (LA) structural remodeling provides the essential substrate for AF perpetuation. Baseline extent and the progression of LA fibrosis after ablation are strong predictors of postprocedural AF recurrence. Dronedarone is an antiarrhythmic drug proven to efficiently maintain sinus rhythm.ObjectiveWe sought to investigate the effect of the antiarrhythmic drug Dronedarone in decreasing LA fibrosis progression and AF recurrence after ablation of AF patients.MethodsEDORA (NCT04704050) is a multicenter, prospective, randomized controlled clinical trial. Patients with persistent or paroxysmal AF undergoing AF ablation will be randomized into Dronedarone versus placebo/standard of care. The co‐primary outcomes are the recurrence of atrial arrhythmias (AA) within 13 months of follow‐up after ablation and the progression of left atrial fibrosis postablation. All patients will receive a late‐gadolinium enhancement magnetic resonance imaging at baseline, 3‐ and 12‐month follow‐up for the quantification of LA fibrosis and ablation‐related scarring. AA recurrence and burden will be assessed using a 30‐day ECG patch every 3 months with daily ECG recordings in between. Quality of life improvement is assessed using the AFEQT and AFSS questionnaires.ConclusionEDORA will be the first trial to assess the progression of LA structural remodeling after ablation and its association with Dronedarone treatment and ablation success in a randomized controlled fashion. The trial will provide insight into the pathophysiology of AF recurrence after ablation and may provide potential therapeutic targets to optimize procedural outcomes.

Reproducibility of non-invasive VT substrate characterization: finding the same paths

Abstract Background Delayed enhancement gadolinium MRI is a useful technique to identify myocardial scar. The objective of this study is compare the reproducibility of the scar quantification and characterization based on cardiac MRI. Methods 10 patients with ischemic ethology underwent to 1,5T DE-MRI acquisition for myocardial scar analysis. Images were processed using a commercial software (ADAS3D-Galgo Medical) and different parameters from scar tissue (mass of the scar, core of scar and border zone expressed in grams) were analysed. Conducting channels evaluation was obtained by the number of corridors and the mass of the border zone of those corridors. To perform this analysis, 2 experienced and 1 non experienced users segmented DE-MRI acquisition in order to evaluate the inter observer variability. Bland-Altman analysis was employed to evaluate the comparison between the measurements. Results Inter observer agreement between experienced users was high (table). The mean and the standard deviation of the differences between two measurements for the scar mass was −3,9±14,66 gr. Analysing the scar tissue divided in core and border zone, the mass of these volume tissues were very similar (−3,51±4,56gr and −0,4±12,87gr respectively. Regarding conducting channels characteristics, the mean of the differences was 0±2 for the number of channels and 1,71±7,76 gr for the mass on the border zone of the corridors. Comparing the measurements between one of the experienced users and the beginner user, results were similar but significant differences were found on the mass of the core and the number of channels, with a variability of ±2 channels (table). Conclusions Left ventricular scar size and characteristics derived from late gadolinium enhanced post-processed images are highly reproducible between experienced observers. Funding Acknowledgement Type of funding sources: None. Table 1Scar analysis performed by 3 users

Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks

Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p&lt; 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.

Extracellular volume-guided late gadolinium enhancement analysis for non-ischemic cardiomyopathy: The Women\u2019s Interagency HIV Study

BackgroundQuantification of non-ischemic myocardial scar remains a challenge due to the patchy diffuse nature of fibrosis. Extracellular volume (ECV) to guide late gadolinium enhancement (LGE) analysis may achieve a robust scar assessment.MethodsThree cohorts of 80 non-ischemic-training, 20 non-ischemic-validation, and 10 ischemic-validation were prospectively enrolled and underwent 3.0 Tesla cardiac MRI. An ECV cutoff to differentiate LGE scar from non-scar was identified in the training cohort from the receiver-operating characteristic curve analysis, by comparing the ECV value against the visually-determined presence/absence of the LGE scar at the highest signal intensity (SI) area of the mid-left ventricle (LV) LGE. Based on the ECV cutoff, an LGE semi-automatic threshold of n-times of standard-deviation (n-SD) above the remote-myocardium SI was optimized in the individual cases ensuring correspondence between LGE and ECV images. The inter-method agreement of scar amount in comparison with manual (for non-ischemic) or full-width half-maximum (FWHM, for ischemic) was assessed. Intra- and inter-observer reproducibility were investigated in a randomly chosen subset of 40 non-ischemic and 10 ischemic cases.ResultsThe non-ischemic groups were all female with the HIV positive rate of 73.8% (training) and 80% (validation). The ischemic group was all male with reduced LV function. An ECV cutoff of 31.5% achieved optimum performance (sensitivity: 90%, specificity: 86.7% in training; sensitivity: 100%, specificity: 81.8% in validation dataset). The identified n-SD threshold varied widely (range 3 SD–18 SD), and was independent of scar amount (β = −0.01, p = 0.92). In the non-ischemic cohorts, results suggested that the manual LGE assessment overestimated scar (%) in comparison to ECV-guided analysis [training: 4.5 (3.2–6.4) vs. 0.92 (0.1–2.1); validation: 2.5 (1.2–3.7) vs. 0.2 (0–1.6); P < 0.01 for both]. Intra- and inter-observer analyses of global scar (%) showed higher reproducibility in ECV-guided than manual analysis with CCC = 0.94 and 0.78 versus CCC = 0.86 and 0.73, respectively (P < 0.01 for all). In ischemic validation, the ECV-guided LGE analysis showed a comparable scar amount and reproducibility with the FWHM.ConclusionsECV-guided LGE analysis is a robust scar quantification method for a non-ischemic cohort.Trial registration ClinicalTrials.gov; NCT00000797, retrospectively-registered 2 November 1999; NCT02501811, registered 15 July 2015.