Quantification Of Myocardial Scar Research Articles

Quantification of myocardial scar of different etiology using dark- and bright-blood late gadolinium enhancement cardiovascular magnetic resonance

Dark-blood late gadolinium enhancement (LGE) has been shown to improve the visualization and quantification of areas of ischemic scar compared to standard bright-blood LGE. Recently, the performance of various semi-automated quantification methods has been evaluated for the assessment of infarct size using both dark-blood LGE and conventional bright-blood LGE with histopathology as a reference standard. However, the impact of this sequence on different quantification strategies in vivo remains uncertain. In this study, various semi-automated scar quantification methods were evaluated for a range of different ischemic and non-ischemic pathologies encountered in clinical practice. A total of 62 patients referred for clinical cardiovascular magnetic resonance (CMR) were retrospectively included. All patients had a confirmed diagnosis of either ischemic heart disease (IHD; n = 21), dilated/non-ischemic cardiomyopathy (NICM; n = 21), or hypertrophic cardiomyopathy (HCM; n = 20) and underwent CMR on a 1.5 T scanner including both bright- and dark-blood LGE using a standard PSIR sequence. Both methods used identical sequence settings as per clinical protocol, apart from the inversion time parameter, which was set differently. All short-axis LGE images with scar were manually segmented for epicardial and endocardial borders. The extent of LGE was then measured visually by manual signal thresholding, and semi-automatically by signal thresholding using the standard deviation (SD) and the full width at half maximum (FWHM) methods. For all quantification methods in the IHD group, except the 6 SD method, dark-blood LGE detected significantly more enhancement compared to bright-blood LGE (p < 0.05 for all methods). For both bright-blood and dark-blood LGE, the 6 SD method correlated best with manual thresholding (16.9% vs. 17.1% and 20.1% vs. 20.4%, respectively). For the NICM group, no significant differences between LGE methods were found. For bright-blood LGE, the 5 SD method agreed best with manual thresholding (9.3% vs. 11.0%), while for dark-blood LGE the 4 SD method agreed best (12.6% vs. 11.5%). Similarly, for the HCM group no significant differences between LGE methods were found. For bright-blood LGE, the 6 SD method agreed best with manual thresholding (10.9% vs. 12.2%), while for dark-blood LGE the 5 SD method agreed best (13.2% vs. 11.5%). Semi-automated LGE quantification using dark-blood LGE images is feasible in both patients with ischemic and non-ischemic scar patterns. Given the advantage in detecting scar in patients with ischemic heart disease and no disadvantage in patients with non-ischemic scar, dark-blood LGE can be readily and widely adopted into clinical practice without compromising on quantification.

Prognostic Power of Quantitative Assessment of Functional Mitral Regurgitation and Myocardial Scar Quantification by Cardiac Magnetic Resonance.

The severity classification of functional mitral regurgitation (FMR) remains controversial despite adverse prognosis and rapidly evolving interventions. Furthermore, it is unclear if quantitative assessment with cardiac magnetic resonance can provide incremental risk stratification for patients with ischemic cardiomyopathy (ICM) or non-ICM (NICM) in terms of FMR and late gadolinium enhancement (LGE). We evaluated the impact of quantitative cardiac magnetic resonance parameters on event-free survival separately for ICM and NICM, to assess prognostic FMR thresholds and interactions with LGE quantification. Patients (n=1414) undergoing cardiac magnetic resonance for cardiomyopathy (ejection fraction<50%) assessment from April 1, 2001 to December 31, 2017 were evaluated. The primary end point was all-cause death, heart transplant, or left ventricular assist device implantation during follow-up. Multivariable Cox analyses were conducted to determine the impact of FMR, LGE, and their interactions with event-free survival. There were 510 primary end points, 395/782 (50.5%) in ICM and 114/632 (18.0%) in NICM. Mitral regurgitation-fraction per 5% increase was independently associated with the primary end point, hazards ratios (95% CIs) of 1.04 (1.01-1.07; P=0.034) in ICM and 1.09 (1.02-1.16; P=0.011) in NICM. Optimal mitral regurgitation-fraction threshold for moderate and severe FMR were ≥20% and ≥35%, respectively, in both ICM and NICM, based on the prediction of the primary outcome. Similarly, optimal LGE thresholds were ≥5% in ICM and ≥2% in NICM. Mitral regurgitation-fraction×LGE emerged as a significant interaction for the primary end point in ICM (P=0.006), but not in NICM (P=0.971). Mitral regurgitation-fraction and LGE are key quantitative cardiac magnetic resonance biomarkers with differential associations with adverse outcomes in ICM and NICM. Optimal prognostic thresholds may provide important clinical risk prognostication and may further facilitate the ability to derive selection criteria to guide therapeutic decision-making.

Prognostic value of myocardial scar burden using cardiac magnetic resonance in ischemic and non-ischemic cardiomyopathy

Abstract Funding Acknowledgements Type of funding sources: None. Myocardium is a unique muscle type designed to perform a specific function; conducting electrical activity to both ventricle simultaneously to contract and relax in a synchronized manner. Therefore, evaluation of the myocardial muscle function only using left ventricular ejection fraction (LVEF) would be inaccurate. However, assessment of the myocardium on the tissue level, is expected to provide more precise data regarding the ability to conduct, contract and relax properly. Purpose is to evaluate the prognostic value of myocardial scar using CMR on the clinical outcome in patients with ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM). Methods 154 patients with either ICM or NICM underwent CMR (using 1.5T) for assessment of LVEF, detection and quantification of any myocardial scar by calculating the percentage (%) of scar mass to the whole myocardial mass (using manual quantification of scarred tissue in each myocardial segment, summed and divided by the total myocardial mass). Patients were followed up for at least 6 months for any cardiac events. These events were scaled according to severity from 1 (asymptomatic) to 7 (most severe), including; no symptoms, mild non-acute chest pain, mild dyspnoea (NYHA I-II), hospital admission due to decompensated heart failure, syncope, documented ventricular arrhythmia and sudden cardiac arrest/death, respectively. Statistical correlation between the amount of scarred myocardium in both ICM (group I) and NICM (group II) patients and the severity of the clinical events during the period of the follow up was performed. The study included 154 patients, 56% were males, mean age of 61 years with minimal follow up duration of 6 months. Patients were divided into: group I with ICM (58%) and group II with NICM (42%). Clinical presentation was ranging from eventless (10%), chest pain (18%), heart failure (15%), hospitalization (35%), syncope (1%), ventricular tachycardia (&amp;lt;1%) and up to cardiac arrest (&amp;lt;1%). Direct relationship was observed between scar size and event severity in the two groups (P value &amp;lt; 0.001). However, inverted relationship between LVEF and event severity in group I (P value of &amp;lt; 0.001) was detected but not in group II (P value 0.128), as shown in figure 1. Low EF was mainly linked with hospitalization in both ICM and NICM. It was observed that serious cardiac events were less detected in patient with mean scar mass &amp;lt; 5.4–8.4% . However, patients who have experienced sudden cardiac arrest have a mean scar mass of 15.9 % and patients with ventricular tachycardia with mean of 9.8%. Conclusion The amount of scarred myocardium is found to be directly linked to the severity of the clinical event in both ICM and NICM. Therefore, quantification of myocardial scar could be used as a predictor for cardiac events, hospitalization and sudden cardiac death.

Prognostic Value of Different Thresholds for Myocardial Scar Quantification on Cardiac MRI Late Gadolinium Enhancement Images in Patients Receiving Implantable Cardioverter Defibrillators

Purpose To compare the predictive value of different myocardial scar quantification thresholds using cardiac MRI for appropriate implantable cardioverter defibrillator (ICD) shock and mortality. Materials and Methods In this retrospective, two-center observational cohort study, patients with ischemic or nonischemic cardiomyopathy underwent cardiac MRI prior to ICD implantation. Late gadolinium enhancement (LGE) was first determined visually and then quantified by blinded cardiac MRI readers using different SDs above the mean signal of normal myocardium, full-width half-maximum, and manual thresholding. The intermediate signal “gray zone” was calculated as the differences between different SDs. Results Among 374 consecutive eligible patients (mean age, 61 years ± 13 [SD]; mean left ventricular ejection fraction, 32% ± 14; secondary prevention, 62.7%), those with LGE had a higher rate of appropriate ICD shock or death than those without (37.5% vs 26.6%, log-rank P = .04) over a median follow-up of 61 months. In multivariable analysis, none of the thresholds for quantifying scar was a significant predictor of mortality or appropriate ICD shock, while the extent of gray zone was an independent predictor (adjusted hazard ratio per 1 g = 1.025; 95% CI: 1.008, 1.043; P = .005) regardless of the presence or absence of ischemic heart disease (P interaction = .57). Model discrimination was highest for the model incorporating the gray zone (between 2 SD and 4 SD). Conclusion Presence of LGE was associated with a higher rate of appropriate ICD shock or death. Although none of the scar quantification techniques predicted outcomes, the gray zone both in infarct and nonischemic scar was an independent predictor and may refine risk stratification. Keywords: MRI, Scar Quantification, Implantable Cardioverter Defibrillator, Sudden Cardiac Death Supplemental material is available for this article. © RSNA, 2023

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.

Delayed Echo Enhancement Imaging to Quantify Myocardial Infarct Size

Perfluoropropane droplets formulated from commercial microbubbles exhibit different acoustic characteristics than their parent microbubbles, most likely from enhanced endothelial permeability. This enhanced permeability may permit delayed echo-enhancement imaging (DEEI) similar to delayed enhancement magnetic resonance imaging (DE-MRI). We hypothesized this would allow detection and quantification of myocardial scar. In 15 pigs undergoing 90minutes of left anterior descending ischemia by either balloon (n=13) or thrombotic occlusion (n=2), DE-MRI was performed at 2-24days postocclusion. Delayed echo-enhancement imaging was performed at 2-4minutes following an intravenous injection of 1mL of 50% Definity (Lantheus Medical) compressed into 180nm droplets; DEEI was attempted in all pigs with single-pulse harmonic imaging at 1.7 transmit/3.4MHz receive. Myocardial defects observed with DEEI were quantified (percentage of infarct area) and compared with DE-MRI as well as postmortem staining. In six pigs, multipulse low-mechanical index (MI) fundamental nonlinear imaging (FNLI) with intermittent high-MI impulses was performed to determine whether droplet activation within the infarct zone was achievable with a longer pulse duration. The range of infarct size area by DE-MRI ranged from 0% to 46% of total left ventricular area. Single-pulse harmonic imaging detected a contrast defect that correlated closely with infarct area by DE-MRI (r=0.81, P=.0001). The FNLI high-MI impulses resulted in droplet activation in both the infarct and normal zones. Harmonic subtraction of the FNLI images resulted in infarct zone enhancement that also correlated closely with infarct size (r=0.83; P=.04). Droplets were observed on postmortem transmission electron microscopy within myocytes of the infarct and remote normal zone. Intravenously Definity nanodroplets can be utilized to detect and quantify infarct zone at the bedside using DEEI techniques.

Fully automated artificial intelligence-based myocardial scar quantification for diagnostic and prognostic stratification in patients following acute myocardial infarction

Abstract Funding Acknowledgements Type of funding sources: None. Background Myocardial infarct size (IS) remains one of the strongest predictors of adverse cardiac events following acute myocardial infarction (AMI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) can precisely quantify the extent of injury but requires manual post-processing. Whether novel user-independent artificial intelligence (AI) based fully-automated analyses may facilitate clinical workflow and deliver similar information for risk stratification is unknown. Methods 913 AMI patients from two multi-center trials (AIDA-STEMI n = 704 with ST-elevation myocardial infarction [STEMI] and TATORT-NSTEMI n = 245 with non-ST-elevation-infarction [NSTEMI]) were included in this sub-study. IS was quantified manually using conventional software (Medis, Leiden Netherlands) and fully automated AI-based software (NeoSoft). All automatically detected IS were evaluated visually and corrected if necessary. Analyzed data were tested for agreement and prediction of major adverse clinical events (MACE) within one year after AMI. Results Automated and manual IS were similarly associated with outcome in cox regression analyses (HR 1.05 [95% CI 1-02-1.07] p &amp;lt; 0.001 for automated IS and HR 1.04 [95% CI 1.02-1.06]; p &amp;lt; 0.001 for manual IS). Comparison of C-statistics derived area under the curve (AUC) resulted in equivalent MACE prediction (AUC 0.65 for automated vs. AUC 0.66 for manual, p = 0.53). Manual correction of the automated scar detection did not lead to an improved risk prediction of MACE (AUC 0.65 to 0.66, p = 0.43). There was good agreement of automated and manually derived IS (intraclass correlation coefficient [ICC] 0.75 [0.07-0.89]) which was further improved after manual correction of the underlying contours (ICC 0.98 [0.97-0.98]). Conclusion AI-based software enables automated scar quantification with similar prognostic value compared to conventional methods in patients following AMI.

Improvement of late gadolinium enhancement image quality using a deep learning\u2013based reconstruction algorithm and its influence on myocardial scar quantification

ObjectivesThe aim of this study was to assess the effect of a deep learning (DL)–based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification.MethodsSixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels.ResultsDLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06).ConclusionsLGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used.Key Points• The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning–based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique.• Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms.• The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.

Abstract 15535: Automated Artificial Intelligence-based Myocardial Scar Quantification for Risk Assessment Following Myocardial Infarction

Introduction: Myocardial infarct size (IS) remains one of the strongest predictors of adverse cardiac events following acute myocardial infarction (AMI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) can precisely quantify the extent of injury but requires manual post-processing. Whether novel user-independent artificial intelligence (AI) based fully-automated analyses may facilitate clinical workflow and deliver similar information for risk stratification is unknown. Methods: 913 AMI patients from two multi-center trials (AIDA-STEMI n = 704 with ST-elevation myocardial infarction [STEMI] and TATORT-NSTEMI n = 245 with non-ST-elevation-infarction [NSTEMI]) were included in this sub-study. IS was quantified manually using conventional software (Medis, Leiden Netherlands) and fully automated AI-based software (NeoSoft). All automatically detected IS were evaluated visually and corrected if necessary. Analyzed data were tested for agreement and prediction of major adverse clinical events (MACE) within one year after AMI. Results: Automated and manual IS were similarly associated with outcome in cox regression analyses (HR 1.05 [95% CI 1-02-1.07] p &lt; 0.001 for automated IS and HR 1.04 [95% CI 1.02-1.06]; p &lt; 0.001 for manual IS). Comparison of C-statistics derived area under the curve (AUC) resulted in equivalent MACE prediction (AUC 0.65 for automated vs. AUC 0.66 for manual, p = 0.53). Manual correction of the automated scar detection did not lead to an improved risk prediction of MACE (AUC 0.65 to 0.66, p = 0.43). There was good agreement of automated and manually derived IS (intraclass correlation coefficient [ICC] 0.75 [0.07-0.89]) which was further improved after manual correction of the underlying contours (ICC 0.98 [0.97-0.98]). Conclusion: AI-based software enables automated scar quantification with similar prognostic value compared to conventional methods in patients following AMI.

COMPARATIVE PROGNOSTIC SIGNIFICANCE OF DIFFERENT METHODS OF SCAR QUANTIFICATION IN PATIENTS RECEIVING IMPLANTABLE CARDIOVERTER-DEFIBRILLATORS

Late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMR) is the most accurate non-invasive method for assessing myocardial scar. Although various studies have demonstrated that myocardial scar quantification predicts occurrence of ventricular arrhythmias and sudden cardiac death, there is no uniform method for myocardial scar quantification. We sought to compare the predictive value of different scar quantification methods for appropriate implantable cardioverter-defibrillator (ICD) shock and mortality.

Automated electrocardiographic quantification of myocardial scar in patients undergoing primary prevention implantable cardioverter-defibrillator implantation: Association with mortality and subsequent appropriate and inappropriate therapies

Myocardial scarring from infarction or nonischemic fibrosis forms an arrhythmogenic substrate. The Selvester QRS score has been developed to estimate myocardial scar from the 12-lead electrocardiogram. We aimed to assess the value of an automated version of the Selvester QRS score for the prediction of implantable cardioverter-defibrillator (ICD) therapy and death in patients undergoing primary prevention ICD implantation. Unselected patients undergoing primary prevention ICD implantation were included in this retrospective, observational, multicenter study. The QRS score was calculated automatically from a digital standard preimplantation 12-lead electrocardiogram and was correlated to the occurrence of death and appropriate and inappropriate shocks during follow-up. Analyses were performed in groups defined by QRS duration < 130 ms vs ≥ 130 ms. Overall, 1047 patients (872 [83%] men; median age 64 years IQR [55-71]) with ischemic (648, 62%) or nonischemic (399, 38%) cardiomyopathy were included. The median QRS duration was 123 ms (interquartile range [IQR] 111-157 ms), and the median QRS score was 5 (IQR 2-8). The QRS duration was <130 ms in 59% and ≥130 ms in 41%. During a median follow-up of 45 months (IQR 24-72 months), a QRS score of ≥5 was independently associated with a significantly higher risk of mortality (hazard ratio [HR] 1.67; 95% confidence interval [CI] 1.05-2.66; P = .031) and appropriate (HR 1.83; 95% CI 1.07-3.14; P = .028) and inappropriate (HR 2.32; 95% CI 1.04-5.17; P= .039) shocks in patients with QRS duration ≥ 130 ms. No association of the QRS score and outcome was observed in patients with QRS duration < 130 ms (P > .05). The automatically calculated Selvester QRS score, an indicator of myocardial scar burden, predicts mortality and appropriate and inappropriate shocks in patients undergoing primary prevention ICD implantation with a prolonged QRS duration.

Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi-planar U-Net (CMPU-Net).

Three-dimensional (3D) late gadolinium enhancement magnetic resonance (LGE-MR) imaging enables the quantification of myocardial scar at high resolution with unprecedented volumetric visualization. Automated segmentation of myocardial scar is critical for the potential clinical translation of this technique given the number of tomographic images acquired. In this paper, we describe the development of cascaded multi-planar U-Net (CMPU-Net) to efficiently segment the boundary of the left ventricle (LV) myocardium and scar from 3D LGE-MR images. In this approach, two subnets, each containing three U-Nets, were cascaded to first segment the LV myocardium and then segment the scar within the presegmented LV myocardium. The U-Nets were trained separately using two-dimensional (2D) slices extracted from axial, sagittal, and coronal slices of 3D LGE-MR images. We used 3D LGE-MR images from 34 subjects with chronic ischemic cardiomyopathy. The U-Nets were trained using 8430 slices, extracted in three orthogonal directions from 18 images. In the testing phase, the outputs of U-Nets of each subnet were combined using the majority voting system for final label prediction of each voxel in the image. The developed method was tested for accuracy by comparing its results to manual segmentations of LV myocardium and LV scar from 7250 slices extracted from 16 3D LGE-MR images. Our method was also compared to numerous alternative methods based on machine learning, energy minimization, and intensity-thresholds. Our algorithm reported a mean dice similarity coefficient (DSC), absolute volume difference (AVD), and Hausdorff distance (HD) of 85.14%±3.36%, 43.72±27.18cm3 , and 19.21±4.74mm for determining the boundaries of LV myocardium from LGE-MR images. Our method also yielded a mean DSC, AVD, and HD of 88.61%±2.54%, 9.33±7.24cm3 , and 17.04±9.93mm for LV scar segmentation on the unobserved test dataset. Our method significantly outperformed the alternative techniques in segmentation accuracy (P<0.05). The CMPU-Net method provided fully automated segmentation of LV scar from 3D LGE-MR images and outperformed the alternative techniques.

Automatically computed ECG algorithm for the quantification of myocardial scar and the prediction of mortality

Myocardial scar is associated with adverse cardiac outcomes. The Selvester QRS-score was developed to estimate myocardial scar from the 12-lead ECG, but its manual calculation is difficult. An automatically computed QRS-score would allow identification of patients with myocardial scar and an increased risk of mortality. To assess the diagnostic and prognostic value of the automatically computed QRS-score. The diagnostic value of the QRS-score computed automatically from a standard digital 12-lead was prospectively assessed in 2742 patients with suspected myocardial ischemia referred for myocardial perfusion imaging (MPI). The prognostic value of the QRS-score was then prospectively tested in 1151 consecutive patients presenting to the emergency department (ED) with suspected acute heart failure (AHF). Overall, the QRS-score was significantly higher in patients with more extensive myocardial scar: the median QRS-score was 3 (IQR 2-5), 4 (IQR 2-6), and 7 (IQR 4-10) for patients with 0, 5-20 and > 20% myocardial scar as quantified by MPI (p < 0.001 for all pairwise comparisons). A QRS-score ≥ 9 (n = 284, 10%) predicted a large scar defined as > 20% of the LV with a specificity of 91% (95% CI 90-92%). Regarding clinical outcomes in patients presenting to the ED with symptoms suggestive of AHF, mortality after 1year was 28% in patients with a QRS-score ≥ 3 as opposed to 20% in patients with a QRS-score < 3 (p = 0.001). The QRS-score can be computed automatically from the 12-lead ECG for simple, non-invasive and inexpensive detection and quantification of myocardial scar and for the prediction of mortality. TRIAL-REGISTRATION: http://www.clinicaltrials.gov . Identifier, NCT01838148 and NCT01831115.

High spatial resolution free-breathing 3D late gadolinium enhancement cardiac magnetic resonance imaging in ischaemic and non-ischaemic cardiomyopathy: quantitative assessment of scar mass and image quality

PurposeTo compare breath-hold (BH) with navigated free-breathing (FB) 3D late gadolinium enhancement cardiac MRI (LGE-CMR)Materials and methodsFifty-one patients were retrospectively included (34 ischaemic cardiomyopathy, 14 non-ischaemic cardiomyopathy, three discarded). BH and FB 3D phase sensitive inversion recovery sequences were performed at 3T. FB datasets were reformatted into normal resolution (FB-NR, 1.46x1.46x10mm) and high resolution (FB-HR, isotropic 0.91-mm voxels). Scar mass, scar edge sharpness (SES), SNR and CNR were compared using paired-samples t-test, Pearson correlation and Bland-Altman analysis.ResultsScar mass was similar in BH and FB-NR (mean ± SD: 15.5±18.0 g vs. 15.5±16.9 g, p=0.997), with good correlation (r=0.953), and no bias (mean difference ± SD: 0.00±5.47 g). FB-NR significantly overestimated scar mass compared with FB-HR (15.5±16.9 g vs 14.4±15.6 g; p=0.007). FB-NR and FB-HR correlated well (r=0.988), but Bland-Altman demonstrated systematic bias (1.15±2.84 g). SES was similar in BH and FB-NR (p=0.947), but significantly higher in FB-HR than FB-NR (p<0.01). SNR and CNR were lower in BH than FB-NR (p<0.01), and lower in FB-HR than FB-NR (p<0.01).ConclusionNavigated free-breathing 3D LGE-CMR allows reliable scar mass quantification comparable to breath-hold. During free-breathing, spatial resolution can be increased resulting in improved sharpness and reduced scar mass.Key Points• Navigated free-breathing 3D late gadolinium enhancement is reliable for myocardial scar quantification.• High-resolution 3D late gadolinium enhancement increases scar sharpness• Ischaemic and non-ischaemic cardiomyopathy patients can be imaged using free-breathing LGE CMR.

Relationship between vectorcardiographic QRSarea, myocardial scar quantification, and response to cardiac resynchronization therapy

PurposeTo investigate the relationship between vectorcardiography (VCG) and myocardial scar on cardiac magnetic resonance (CMR) imaging, and whether combining these metrics may improve cardiac resynchronization therapy (CRT) response prediction. MethodsThirty-three CRT patients were included. QRSarea, Tarea and QRSTarea were derived from the ECG-synthesized VCG. CMR parameters reflecting focal scar core (Scar2SD, Gray2SD) and diffuse fibrosis (pre-T1, extracellular volume [ECV]) were assessed. CRT response was defined as ≥15% reduction in left ventricular end-systolic volume after six months' follow-up. ResultsVCG QRSarea, Tarea and QRSTarea inversely correlated with focal scar (R = −0.44–−0.58 for Scar2SD, p ≤ 0.010), but not with diffuse fibrosis. Scar2SD, Gray2SD and QRSarea predicted CRT response with AUCs of 0.692 (p = 0.063), 0.759 (p = 0.012) and 0.737 (p = 0.022) respectively. A combined ROC-derived threshold for Scar2SD and QRSarea resulted in 92% CRT response rate for patients with large QRSarea and small Scar2SD or Gray2SD. ConclusionQRSarea is inversely associated with focal scar on CMR. Incremental predictive value for CRT response is achieved by a combined CMR-QRSarea analysis.

Accurate late gadolinium enhancement prediction by early T1- based quantitative synthetic mapping

ObjectivesEarly synthetic gadolinium enhancement (ESGE) imaging from post-contrast T1 mapping after adenosine stress-perfusion cardiac magnetic resonance (CMR) was compared to conventional late gadolinium enhancement (LGE) imaging for assessing myocardial scar.MethodsTwo hundred fourteen consecutive patients suspected of myocardial ischaemia were referred for stress-perfusion CMR. Myocardial infarct volume was quantified on a per-subsegment basis in both synthetic (2–3 min post-gadolinium) and conventional (9 min post-gadolinium) images by two independent observers. Sensitivity, specificity, PPV and NPV were calculated on a per-patient and per-subsegment basis.ResultsBoth techniques detected 39 gadolinium enhancement areas in 23 patients. The median amount of scar was 2.0 (1.0–3.1) g in ESGE imaging and 2.2 (1.1–3.1) g in LGE imaging (p=0.39). Excellent correlation (r=0.997) and agreement (mean absolute difference: -0.028±0.289 ml) were found between ESGE and LGE images. Sensitivity, specificity, PPV and NPV of ESGE imaging were 96 (78.9–99.9), 99 (97.1–100.0)%, 96 (76.5–99.4) and 99.5 (96.6–99.9) in patient-based and 99 (94.5–100.0), 100 (99.9–100.0)%, 97.0 (91.3–99.0) and 100.0 (99.8–100.0) in subsegment-based analysis.ConclusionESGE based on post-contrast T1 mapping after adenosine stress-perfusion CMR imaging shows excellent agreement with conventional LGE imaging for assessing myocardial scar, and can substantially shorten clinical acquisition time.Key Points• Synthetic gadolinium enhancement images can be used for detection of myocardial scar.• Early synthetic gadolinium enhancement images can substantially shorten clinical acquisition time.• ESGE has high diagnostic accuracy as compared to conventional late gadolinium enhancement.• Quantification of myocardial scar with ESGE closely correlates with conventional LGE.• ESGE after stress perfusion CMR avoids need for additional gadolinium administration.

P2457Automated ECG quantification of myocardial scar in patients with and without conduction defects: correlation with myocardial perfusion imaging and clinical outcome in acute heart failure patients

P2457Automated ECG quantification of myocardial scar in patients with and without conduction defects: correlation with myocardial perfusion imaging and clinical outcome in acute heart failure patients

Cardiac MRI Anatomy and Function as a Substrate for Arrhythmias.

The use of cardiovascular magnetic resonance (CMR) has been implemented in the diagnostic work-up of patients with cardiomyopathies by providing an accurate assessment of biventricular volumes and function and a detailed myocardial tissue characterization in a one-stop-shop multi-parametric imaging technique. Its unique capability to perform an accurate tissue characterization of the myocardium, which is superior to other imaging modalities, has prompt its use in the analysis of myocardial arrhythmic substrates and in the prognostic risk stratification of patients. Although left ventricular ejection fraction (LVEF) has always been the best-known predictor of arrhythmic risk, the quantification of myocardial scar by CMR has been recognised as a powerful risk stratification tool, independent of LVEF. Moreover, due to its ability to identify myocardial arrhythmic substrate, both ventricular but more recently also atrial, CMR is increasingly offered as a guide to ablation procedures.

A NOVEL METRIC FOR THE PREDICTION OF LEFT VENTRICULAR UNDERLYING TISSUE SCAR: INTEGRAL RADIAL STRAIN. VALIDATION IN PATIENTS UNDERGOING CARDIAC RESYNCHRONIZATION THERAPY

Chronic Resynchronization Therapy (CRT) is aimed at restoring synchronous contraction of the Left Ventricle. Currently approximately 60-70% of patients realize objective improvements, the remainder exposed without benefit. Non-responsiveness has been associated with both lack of dyssynchrony and transmural scar in the region of both the LV and RV pacing leads. Late Gadolinum Enhancement (LGE) following gadolinium administration is a golden standard for the localization and quantification of myocardial scar, albeit contraindicated in up to one-third of heart failure patients due to renal insufficiency.

Selvester QRS score: an automated algorithm for the quantification of myocardial scar

Selvester QRS score: an automated algorithm for the quantification of myocardial scar