Dataset Information

Late Gadolinium Enhancement Cardiovascular Magnetic Resonance Assessment of Substrate for Ventricular Tachycardia With Hemodynamic Compromise.

ABSTRACT: Background: The majority of data regarding tissue substrate for post myocardial infarction (MI) VT has been collected during hemodynamically tolerated VT, which may be distinct from the substrate responsible for VT with hemodynamic compromise (VT-HC). This study aimed to characterize tissue at diastolic locations of VT-HC in a porcine model. Methods: Late Gadolinium Enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging was performed in eight pigs with healed antero-septal infarcts. Seven pigs underwent electrophysiology study with venous arterial-extra corporeal membrane oxygenation (VA-ECMO) support. Tissue thickness, scar and heterogeneous tissue (HT) transmurality were calculated at the location of the diastolic electrograms of mapped VT-HC. Results: Diastolic locations had median scar transmurality of 33.1% and a median HT transmurality 7.6%. Diastolic activation was found within areas of non-transmural scar in 80.1% of cases. Tissue activated during the diastolic component of VT circuits was thinner than healthy tissue (median thickness: 5.5 mm vs. 8.2 mm healthy tissue, p < 0.0001) and closer to HT (median distance diastolic tissue: 2.8 mm vs. 11.4 mm healthy tissue, p < 0.0001). Non-scarred regions with diastolic activation were closer to steep gradients in thickness than non-scarred locations with normal EGMs (diastolic locations distance = 1.19 mm vs. 9.67 mm for non-diastolic locations, p < 0.0001). Sites activated late in diastole were closest to steep gradients in tissue thickness. Conclusions: Non-transmural scar, mildly decreased tissue thickness, and steep gradients in tissue thickness represent the structural characteristics of the diastolic component of reentrant circuits in VT-HC in this porcine model and could form the basis for imaging criteria to define ablation targets in future trials.

SUBMITTER: Whitaker J

PROVIDER: S-EPMC8576410 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Late Gadolinium Enhancement Cardiovascular Magnetic Resonance Assessment of Substrate for Ventricular Tachycardia With Hemodynamic Compromise.

Whitaker John J Neji Radhouene R Kim Steven S Connolly Adam A Aubriot Thierry T Calvo Justo Juliá JJ Karim Rashed R Roney Caroline H CH Murfin Brendan B Richardson Carla C Morgan Stephen S Ismail Tevfik F TF Harrison James J de Vos Judith J Aalders Maurice C G MCG Williams Steven E SE Mukherjee Rahul R O'Neill Louisa L Chubb Henry H Tschabrunn Cory C Anter Elad E Camporota Luigi L Niederer Steven S Roujol Sébastien S Bishop Martin J MJ Wright Matthew M Silberbauer John J Razavi Reza R O'Neill Mark M

Frontiers in cardiovascular medicine 20211026

<b>Background:</b> The majority of data regarding tissue substrate for post myocardial infarction (MI) VT has been collected during hemodynamically tolerated VT, which may be distinct from the substrate responsible for VT with hemodynamic compromise (VT-HC). This study aimed to characterize tissue at diastolic locations of VT-HC in a porcine model. <b>Methods:</b> Late Gadolinium Enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging was performed in eight pigs with healed antero-sept ...[more]

PMID: 34765656

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Project description:BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction.MethodsA generative AI-based image enhancement was used to improve the sharpness of 2D LGE images acquired with low spatial resolution in the phase-encode direction. The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. The model was implemented inline, allowing the reconstruction of images on the scanner console. We prospectively enrolled 100 patients (55 ± 14 years, 72 males) referred for clinical CMR at 3T. We collected three sets of LGE images in each subject, with in-plane spatial resolutions of 1.5 × 1.5-3-6 mm2. The generative AI model enhanced in-plane resolution to 1.5 × 1.5 mm2 from the low-resolution counterparts. Images were compared using a blur metric, quantifying the perceived image sharpness (0 = sharpest, 1 = blurriest). LGE image sharpness (using a 5-point scale) was assessed by three independent readers.ResultsThe scan times for the three imaging sets were 15 ± 3, 9 ± 2, and 6 ± 1 s, with inline generative AI-based images reconstructed time of ∼37 ms. The generative AI-based model improved visual image sharpness, resulting in lower blur metric compared to low-resolution counterparts (AI-enhanced from 1.5 × 3 mm2 resolution: 0.3 ± 0.03 vs 0.35 ± 0.03, P < 0.01). Meanwhile, AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images showed similar blur metric (0.30 ± 0.03 vs 0.31 ± 0.03, P = 1.0) Additionally, there was an overall 18% improvement in image sharpness between AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images in the subjective blurriness score (P < 0.01).ConclusionThe generative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis.

Project description:BackgroundWhile late gadolinium enhancement (LGE) is proposed as a diagnostic criterion for arrhythmogenic right ventricular cardiomyopathy (ARVC), the potential of LGE to distinguish ARVC from differentials remains unknown. We aimed to assess the diagnostic value of LGE for ARVC diagnosis.MethodsWe included 132 subjects (60% male, 47 ± 11 years) who had undergone cardiac magnetic resonance imaging with LGE assessment for ARVC or ARVC differentials. ARVC was diagnosed as per 2010 Task Force Criteria (n = 55). ARVC differentials consisted of familial/genetic dilated cardiomyopathy (n = 25), myocarditis (n = 13), sarcoidosis (n = 20), and amyloidosis (n = 19). The diagnosis of all differentials was based on the most current standard of reference. The presence of LGE was evaluated using a 7-segment right ventricle (RV) and 17-segment left ventricle (LV) model. Subsequently, we assessed LGE patterns for every patient individually for fulfilling LV- and/or RV-LGE per Padua criteria, independent of their clinical diagnosis (i.e. phenotype). Diagnostic values were analyzed using sensitivity and specificity for any RV-LGE, any LV-LGE, RV-LGE per Padua criteria, and prevalence graphs for LV-LGE per Padua criteria. The optimal integration of LGE for ARVC diagnosis was determined using classification and regression tree analysis.ResultsOne-third (38%) of ARVC patients had RV-LGE, while half (51%) had LV-LGE. RV-LGE was less frequently observed in ARVC vs non-ARVC patients (38% vs 58%, p = 0.034) leading to a poor discriminatory potential (any RV-LGE: sensitivity 38%, specificity 42%; RV-LGE per Padua criteria: sensitivity 36%, specificity 44%). Compared to ARVC patients, non-ARVC patients more often had LV-LGE (91% vs 51%, p < 0.001) which was also more globally distributed (median 9 [interquartile range (IQR): 3-13] vs 0 [IQR: 0-3] segments, p < 0.001). The absence of anteroseptal and absence of extensive (≥5 segments) mid-myocardial LV-LGE, and absence of moderate (≥2 segments) mid-myocardial LV-LGE predicted ARVC with good diagnostic performance (sensitivity 93%, specificity 78%).ConclusionLGE is often present in ARVC differentials and may lead to false positive diagnoses when used without knowledge of LGE patterns. Moderate RV-LGE without anteroseptal and mid-myocardial LV-LGE is typically observed in ARVC.

Project description:PurposeTo develop a free-breathing whole-heart isotropic-resolution 3D late gadolinium enhancement (LGE) sequence with Dixon-encoding, which provides co-registered 3D grey-blood phase-sensitive inversion-recovery (PSIR) and complementary 3D fat volumes in a single scan of < 7 min.MethodsA free-breathing 3D PSIR LGE sequence with dual-echo Dixon readout with a variable density Cartesian trajectory with acceleration factor of 3 is proposed. Image navigators are acquired to correct both inversion recovery (IR)-prepared and reference volumes for 2D translational respiratory motion, enabling motion compensated PSIR reconstruction with 100% respiratory scan efficiency. An intermediate PSIR reconstruction is performed between the in-phase echoes to estimate the signal polarity which is subsequently applied to the IR-prepared water volume to generate a water grey-blood PSIR image. The IR-prepared water volume is obtained using a water/fat separation algorithm from the corresponding dual-echo readout. The complementary fat-volume is obtained after water/fat separation of the reference volume. Ten patients (6 with myocardial scar) were scanned with the proposed water/fat grey-blood 3D PSIR LGE sequence at 1.5 T and compared to breath-held grey-blood 2D LGE sequence in terms of contrast ratio (CR), contrast-to-noise ratio (CNR), scar depiction, scar transmurality, scar mass and image quality.ResultsComparable CRs (p = 0.98, 0.40 and 0.83) and CNRs (p = 0.29, 0.40 and 0.26) for blood-myocardium, scar-myocardium and scar-blood respectively were obtained with the proposed free-breathing 3D water/fat LGE and 2D clinical LGE scan. Excellent agreement for scar detection, scar transmurality, scar mass (bias = 0.29%) and image quality scores (from 1: non-diagnostic to 4: excellent) of 3.8 ± 0.42 and 3.6 ± 0.69 (p > 0.99) were obtained with the 2D and 3D PSIR LGE approaches with comparable total acquisition time (p = 0.29). Similar agreement in intra and inter-observer variability were obtained for the 2D and 3D acquisition respectively.ConclusionThe proposed approach enabled the acquisition of free-breathing motion-compensated isotropic-resolution 3D grey-blood PSIR LGE and fat volumes. The proposed approach showed good agreement with conventional 2D LGE in terms of CR, scar depiction and scan time, while enabling free-breathing acquisition, whole-heart coverage, reformatting in arbitrary views and visualization of both water and fat information.

Project description:AimTo evaluate the ability of single heartbeat fast-strain encoded (SENC) cardiovascular magnetic resonance (CMR) derived myocardial strain to discriminate between different forms of left ventricular (LV) hypertrophy (LVH).Methods314 patients (228 with hypertensive heart disease (HHD), 45 with hypertrophic cardiomyopathy (HCM), 41 with amyloidosis, 22 competitive athletes, and 33 healthy controls) were systematically analysed. LV ejection fraction (LVEF), LV mass index and interventricular septal (IVS) thickness, T1 mapping and atypical late gadolinium enhancement (LGE) were assessed. In addition, the percentage of LV myocardial segments with strain ≤ - 17% (%normal myocardium) was determined.ResultsPatients with amyloidosis and HCM exhibited the highest IVS thickness (17.4 ± 3.3 mm and 17.4 ± 6 mm, respectively, p < 0.05 vs. all other groups), whereas patients with amyloidosis showed the highest LV mass index (95.1 ± 20.1 g/m2, p < 0.05 vs all others) and lower LVEF compared to controls (50.5 ± 9.8% vs 59.2 ± 5.5%, p < 0.05). Analysing subjects with mild to moderate hypertrophy (IVS 11-15 mm), %normal myocardium exhibited excellent and high precision, respectively for the differentiation between athletes vs. HCM (sensitivity and specificity = 100%, Area under the curve; AUC%normalmyocardium = 1.0, 95%CI = 0.85-1.0) and athletes vs. HHD (sensitivity = 83%, specificity = 75%, AUC%normalmyocardium = 0.85, 95%CI = 0.78-0.90). Combining %normal myocardial strain with atypical LGE provided high accuracy also for the differentiation of HHD vs. HCM (sensitivity = 82%, specificity = 100%, AUCcombination = 0.92, 95%CI = 0.88-0.95) and HCM vs. amyloidosis (sensitivity = 83%, specificity = 100%, AUCcombination = 0.83, 95%CI = 0.60-0.96).ConclusionFast-SENC derived myocardial strain is a valuable tool for differentiating between athletes vs. HCM and athletes vs. HHD. Combining strain and LGE data is useful for differentiating between HHD vs. HCM and HCM vs. cardiac amyloidosis.

Dataset Information

Late Gadolinium Enhancement Cardiovascular Magnetic Resonance Assessment of Substrate for Ventricular Tachycardia With Hemodynamic Compromise.

Publications

Late Gadolinium Enhancement Cardiovascular Magnetic Resonance Assessment of Substrate for Ventricular Tachycardia With Hemodynamic Compromise.

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