Relation of MRI Aortic Wall Area and Plaque to Incident Cardiovascular Events: The Framingham Heart Study.

Background Arterial arteriosclerosis and atherosclerosis reflect vascular disease, the subclinical detection of which allows opportunity for cardiovascular disease (CVD) prevention. Larger cohort studies simultaneously quantifying anatomic thoracic and abdominal aortic pathologic abnormalities are lacking in the literature. Purpose To investigate the association of aortic wall area (AWA) and atherosclerotic plaque presence and burden as measured on MRI scans with incident CVD in a community sample. Materials and Methods In this prospective cohort study, participants in the Framingham Heart Study Offspring Cohort without prevalent CVD underwent 1.5-T MRI (between 2002-2005) of the descending thoracic and abdominal aorta with electrocardiogram-gated axial T2-weighted black-blood acquisitions. The wall thickness of the thoracic aorta was measured at the pulmonary bifurcation level and used to calculate the AWA as the difference between cross-sectional vessel area and lumen area. For primary or secondary analyses, multivariable Cox proportional hazards regression models were used to examine the association of aortic MRI measures with risk of first-incident CVD events or stroke and coronary heart disease, respectively. Results In 1513 study participants (mean age, 64 years ± 9 [SD]; 842 women [56%]), 223 CVD events occurred during follow-up (median, 13.1 years), of which 97 were major events (myocardial infarction, ischemic stroke, or CVD death). In multivariable analysis, thoracic AWA and prevalent thoracic plaque were associated with incident CVD (hazard ratio [HR], 1.20 per SD unit [95% CI: 1.05, 1.37] [P = .006] and HR, 1.63 [95% CI: 1.12, 2.35] [P = .01], respectively). AWA and prevalent thoracic plaque were associated with increased hazards: 1.32 (95% CI: 1.07, 1.62; P = .01) and 2.20 (95% CI: 1.28, 3.79; P = .005), for stroke and coronary heart disease, respectively. Conclusion In middle-aged community-dwelling adults, thoracic aortic wall area (AWA), plaque prevalence, and plaque volumes measured with MRI were independently associated with incident cardiovascular disease, with AWA associated in particular with stroke, and plaque associated with coronary heart disease. Clinical trial registration no. NCT00041418 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Peshock in this issue.

Dairy product consumption and calcified atherosclerotic plaques in the coronary arteries: The NHLBI Family Heart Study.

BACKGROUND & AIMS: Diet modification is a major component of non-pharmacological coronary heart disease (CHD) prevention. Few studies have examined the association between consumption of different dairy products with subclinical coronary artery disease. We sought to examine whether milk, yogurt, or cheese consumption is associated with calcified atherosclerotic plaques in the coronary arteries. METHODS: We cross-sectionally examined 2278 participants from the National Heart, Lung, and Blood Institute Family Heart Study. Dairy consumption was assessed by a semiquantitative food frequency questionnaire. Coronary artery calcium (CAC) was estimated by cardiac computed tomography. We used an Agatston score of ≥100 to indicate prevalent CAC and fitted multivariable logistic regression to calculate adjusted odds ratios. RESULTS: Mean age was 58 ± 13 years and 45% were male. The frequency of milk (≤1/week, 2-4/week, and ≥5/week; 22%, 14%, and 64%, respectively), yogurt (almost never, <1/week, and ≥1/week; 54%, 20%, and 26%, respectively), and cheese consumption (<1/week, 1/week, 2-4/week, and ≥5/week; 15%, 17%, 41%, and 27%, respectively) varied in the cohort. We observed an inverse association of cheese consumption with prevalent CAC: odds ratio (95% CI) of 0.63 (0.42-0.94) when comparing cheese intake of ≥5 servings/week with <1/week, adjusting for sex, age, body mass index, cigarette pack years, presence of CHD, family income, and education (p for linear trend 0.007). In contrast, there was no association between yogurt or milk consumption and CAC (p for linear trend 0.51 and 0.87, respectively). CONCLUSION: Our data suggest that cheese consumption but not yogurt or milk is associated with a lower odds of CAC in men and women.

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

BACKGROUND: Cardiac magnetic resonance (MR) images are often collected with different imaging parameters, which may impact the calculated values of myocardial radiomic features. PURPOSE: To investigate the sensitivity of myocardial radiomic features to changes in imaging parameters in cardiac MR images. STUDY TYPE: Prospective. POPULATION: A total of 11 healthy participants/five patients. FIELD STRENGTH/ SEQUENCE: A 3 T/cine balanced steady-state free-precession, T1 -weighted spoiled gradient-echo, T2 -weighted turbo spin-echo, and quantitative T1 and T2 mapping. For each sequence, the flip angle, in-plane resolution, slice thickness, and parallel imaging technique were varied to study the sensitivity of radiomic features to alterations in imaging parameters. ASSESSMENT: Myocardial contours were manually delineated by experienced readers, and a total of 1023 radiomic features were extracted using PyRadiomics with 11 image filters and six feature families. STATISTICAL TESTS: Sensitivity was defined as the standardized mean difference (D effect size), and the robust features were defined at sensitivity < 0.2. Sensitivity analysis was performed on predefined sets of reproducible features. The analysis was performed using the entire cohort of 16 subejcts. RESULTS: 64% of radiomic features were robust (sensitivity < 0.2) to changes in any imaging parameter. In qualitative sequences, radiomic features were most sensitive to changes in in-plane spatial resolution (spatial resolution: 0.6 vs. flip angle: 0.19, parallel imaging: 0.18, slice thickness: 0.07; P < 0.01 for all); in quantitative sequences, radiomic features were least sensitive to changes in spatial resolution (spatial resolution: 0.07 vs. slice thickness: 0.16, flip angle: 0.24; P < 0.01 for all). In an individual feature level, no singular feature family/image filter was identified as robust (sensitivity < 0.2) across sequences; however, highly sensitive features were predominantly associated with high-frequency wavelet filters across all sequences (32/50 features). DATA CONCLUSION: In cardiac MR, a considerable number of radiomic features are sensitive to changes in sequence parameters. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI.

PURPOSE: Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath-holding difficulty or non-sinus rhythms. To reduce scan time, we propose a multi-domain convolutional neural network (MD-CNN) for fast reconstruction of highly undersampled radial cine images. METHODS: MD-CNN is a complex-valued network that processes MR data in k-space and image domains via k-space interpolation and image-domain subnetworks for residual artifact suppression. MD-CNN exploits spatio-temporal correlations across timeframes and multi-coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective-gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD-CNN and k-t Radial Sparse-Sense(kt-RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD-CNN images were evaluated quantitatively using mean-squared-error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5-point Likert-scale (1-non-diagnostic, 2-poor, 3-fair, 4-good, and 5-excellent). RESULTS: MD-CNN showed improved MSE and SSIM compared to kt-RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD-CCN significantly outperformed kt-RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end-diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end-systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). CONCLUSION: MD-CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt-RASPS.

Association Between Left Ventricular Mechanical Deformation and Myocardial Fibrosis in Nonischemic Cardiomyopathy.

Background In patients with nonischemic cardiomyopathy, nonischemic fibrosis detected by late gadolinium enhancement (LGE) cardiovascular magnetic resonance is related to adverse cardiovascular outcomes. However, its relationship with left ventricular (LV) mechanical deformation parameters remains unclear. We sought to investigate the association between LV mechanics and the presence, location, and extent of fibrosis in patients with nonischemic cardiomyopathy. Methods and Results We retrospectively identified 239 patients with nonischemic cardiomyopathy (67% male; 55±14 years) referred for a clinical cardiovascular magnetic resonance. LGE was present in 109 patients (46%), most commonly (n=52; 22%) in the septum. LV deformation parameters did not differentiate between LGE-positive and LGE-negative groups. Global longitudinal, radial, and circumferential strains, twist and torsion showed no association with extent of fibrosis. Patients with septal fibrosis had a more depressed LV ejection fraction (30±12% versus 35±14%; P=0.032) and more impaired global circumferential strain (-7.9±3.5% versus -9.7±4.4%; P=0.045) and global radial strain (10.7±5.2% versus 13.3±7.7%; P=0.023) than patients without septal LGE. Global longitudinal strain was similar in both groups. While patients with septal-only LGE (n=28) and free wall-only LGE (n=32) had similar fibrosis burden, the septal-only LGE group had more impaired LV ejection fraction and global circumferential, longitudinal, and radial strains (all P<0.05). Conclusions There is no association between LV mechanical deformation parameters and presence or extent of fibrosis in patients with nonischemic cardiomyopathy. Septal LGE was associated with poor global LV function, more impaired global circumferential and radial strains, and more impaired global strain rates.

T1 Mapping Tissue Heterogeneity Provides Improved Risk Stratification for ICDs Without Needing Gadolinium in Patients With Dilated Cardiomyopathy.

OBJECTIVES: This study sought to determine whether myocardial tissue heterogeneity scanned by native T1 mapping could improve risk stratification in patients with nonischemic dilated cardiomyopathy (NICM) evaluated for primary prevention by ICD. BACKGROUND: The benefit of insertable cardiac-defibrillator (ICD) as primary prevention ICD in patients with NICM remains to be fully clarified. METHODS: A total of 115 NICM candidates for primary prevention and 55 healthy controls with similar distributions of age and sex were prospectively enrolled. Imaging was performed at 1.5-T using a protocol that included cine magnetic resonance for left ventricular function, late gadolinium enhancement (LGE) for focal scarring, and 5-slice native T1 mapping for diffuse fibrosis and heterogeneity. The last method was assessed by mean absolute deviation of the segmental pixel-SD from the average pixel-SD (Mad-SD). The primary endpoint was a composite of appropriate ICD therapy and sudden cardiac death. RESULTS: During a median follow-up of 24 months, 13 patients (11%) experienced the primary endpoint. Dichotomized Mad-SD >0.24 provided a comparable outcome to the presence of LGE for the primary endpoint (annual event rate: 9.8% vs. 10.9%). The integration of Mad-SD to global native T1 showed excellent arrhythmic event-free survival (annual event rate: 0%), and high sensitivity of 85% (95% confidence interval [CI]: 55% to 98%) and moderate specificity of 72% (95% CI: 62% to 80%), with a C-statistic of 0.76 (95% CI: 0.64 to 0.87), which was comparable to the presence, location, or extent of LGE in its ability to predict arrhythmic events. CONCLUSIONS: Combined myocardium tissue heterogeneity and interstitial fibrosis assessment by native T1 mapping is an important predictor of ventricular tachycardia and ventricular fibrillation and provides additive risk stratification for primary prevention ICD in NICM patients without the need for gadolinium contrast.

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping.

BACKGROUND: The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses on myocardial native T1 mapping can differentiate between fibrosis patterns in patients with HCM and DCM. METHODS: We prospectively acquired native myocardial T1 mapping images for 321 subjects (55±15 years, 70% male): 65 control, 116 HCM, and 140 DCM patients. To quantify different fibrosis patterns, four sets of texture descriptors were used to extract 152 texture features from native T1 maps. Seven features were sequentially selected to identify HCM- and DCM-specific patterns in 70% of data (training dataset). Pattern reproducibility and generalizability were tested on the rest of data (testing dataset) using support vector machines (SVM) and regression models. RESULTS: Pattern-derived texture features were capable to identify subjects in HCM, DCM, and controls cohorts with 202/237(85.2%) accuracy of all subjects in the training dataset using 10-fold cross-validation on SVM (AUC = 0.93, 0.93, and 0.93 for controls, HCM and DCM, respectively), while pattern-independent global native T1 mapping was poorly capable to identify those subjects with 121/237(51.1%) accuracy (AUC = 0.78, 0.51, and 0.74) (P<0.001 for all). The pattern-derived features were reproducible with excellent intra- and inter-observer reliability and generalizable on the testing dataset with 75/84(89.3%) accuracy. CONCLUSION: Texture analysis of myocardial native T1 mapping can characterize fibrosis patterns in HCM and DCM patients and provides additional information beyond average native T1 values.

Negative synergism of diabetes mellitus and obesity in patients with heart failure with preserved ejection fraction: a cardiovascular magnetic resonance study.

In patients with heart failure with preserved ejection fraction (HFpEF), diabetes mellitus (DM) and obesity are important comorbidities as well as major risk factors. Their conjoint impact on the myocardium provides insight into the HFpEF aetiology. We sought to investigate the association between obesity, DM, and their combined effect on alterations in the myocardial tissue in HFpEF patients. One hundred and sixty-two HFpEF patients (55 ± 12 years, 95 men) and 45 healthy subjects (53 ± 12 years, 27 men) were included. Patients were classified according to comorbidity prevalence (36 obese patients without DM, 53 diabetic patients without obesity, and 73 patients with both). Myocardial remodeling, fibrosis, and longitudinal contractility were quantified with cardiovascular magnetic resonance imaging using cine and myocardial native T1 images. Patients with DM and obesity had impaired global longitudinal strain (GLS) and increased myocardial native T1 compared to patients with only one comorbidity (DM + Obesity vs. DM and Obesity; GLS, - 15 ± 2.1 vs - 16.5 ± 2.4 and - 16.7 ± 2.2%; native T1, 1162 ± 37 vs 1129 ± 25 and 1069 ± 29 ms; P < 0.0001 for all). A negative synergistic effect of combined obesity and DM prevalence was observed for native T1 (np2 = 0.273, p = 0.002) and GLS (np2 = 0.288, p < 0.0001). Additionally, severity of insulin resistance was associated with GLS (R = 0.590, P < 0.0001), and native T1 (R = 0.349, P < 0.0001). The conjoint effect of obesity and DM in HFpEF patients is associated with diffuse myocardial fibrosis and deterioration in GLS. The negative synergistic effects observed on the myocardium may be related to severity of insulin resistance.

Deep complex convolutional network for fast reconstruction of 3D late gadolinium enhancement cardiac MRI.

Several deep-learning models have been proposed to shorten MRI scan time. Prior deep-learning models that utilize real-valued kernels have limited capability to learn rich representations of complex MRI data. In this work, we utilize a complex-valued convolutional network (ℂNet) for fast reconstruction of highly under-sampled MRI data and evaluate its ability to rapidly reconstruct 3D late gadolinium enhancement (LGE) data. ℂNet preserves the complex nature and optimal combination of real and imaginary components of MRI data throughout the reconstruction process by utilizing complex-valued convolution, novel radial batch normalization, and complex activation function layers in a U-Net architecture. A prospectively under-sampled 3D LGE cardiac MRI dataset of 219 patients (17 003 images) at acceleration rates R = 3 through R = 5 was used to evaluate ℂNet. The dataset was further retrospectively under-sampled to a maximum of R = 8 to simulate higher acceleration rates. We created three reconstructions of the 3D LGE dataset using (1) ℂNet, (2) a compressed-sensing-based low-dimensional-structure self-learning and thresholding algorithm (LOST), and (3) a real-valued U-Net (realNet) with the same number of parameters as ℂNet. LOST-reconstructed data were considered the reference for training and evaluation of all models. The reconstructed images were quantitatively evaluated using mean-squared error (MSE) and the structural similarity index measure (SSIM), and subjectively evaluated by three independent readers. Quantitatively, ℂNet-reconstructed images had significantly improved MSE and SSIM values compared with realNet (MSE, 0.077 versus 0.091; SSIM, 0.876 versus 0.733, respectively; p < 0.01). Subjective quality assessment showed that ℂNet-reconstructed image quality was similar to that of compressed sensing and significantly better than that of realNet. ℂNet reconstruction was also more than 300 times faster than compressed sensing. Retrospective under-sampled images demonstrate the potential of ℂNet at higher acceleration rates. ℂNet enables fast reconstruction of highly accelerated 3D MRI with superior performance to real-valued networks, and achieves faster reconstruction than compressed sensing.

Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar.

BACKGROUND: In patients with suspected or known hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) provides diagnostic and prognostic value. However, contraindications and long-term retention of gadolinium have raised concern about repeated gadolinium administration in this population. Alternatively, native T1 -mapping enables identification of focal fibrosis, the substrate of LGE. However HCM-specific heterogeneous fibrosis distribution leads to subtle T1 -maps changes that are difficult to identify. PURPOSE: To apply radiomic texture analysis on native T1 -maps to identify patients with a low likelihood of LGE(+), thereby reducing the number of patients exposed to gadolinium administration. STUDY TYPE: Retrospective interpretation of prospectively acquired data. SUBJECTS: In all, 188 (54.7 ± 14.4 years, 71% men) with suspected or known HCM. FIELD STRENGTH/SEQUENCE: A 1.5T scanner; slice-interleaved native T1 -mapping (STONE) sequence and 3D LGE after administration of 0.1 mmol/kg of gadobenate dimeglumine. ASSESSMENT: Left ventricular LGE images were location-matched with native T1 -maps using anatomical landmarks. Using a split-sample validation approach, patients were randomly divided 3:1 (training/internal validation vs. test cohorts). To balance the data during training, 50% of LGE(-) slices were discarded. STATISTICAL TESTS: Four sets of texture descriptors were applied to the training dataset for capture of spatially dependent and independent pixel statistics. Five texture features were sequentially selected with the best discriminatory capacity between LGE(+) and LGE(-) T1 -maps and tested using a decision tree ensemble (DTE) classifier. RESULTS: The selected texture features discriminated between LGE(+) and LGE(-) T1 -maps with a c-statistic of 0.75 (95% confidence interval [CI]: 0.70-0.80) using 10-fold cross-validation during internal validation in the training dataset and 0.74 (95% CI: 0.65-0.83) in the independent test dataset. The DTE classifier provided adequate labeling of all (100%) LGE(+) patients and 37% of LGE(-) patients during testing. DATA CONCLUSION: Radiomic analysis of native T1 -images can identify ~1/3 of LGE(-) patients for whom gadolinium administration can be safely avoided. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020. J. Magn. Reson. Imaging 2020;52:906-919.

Changes in Myocardial Native T1 and T2 After Exercise Stress: A Noncontrast CMR Pilot Study.

OBJECTIVES: This study assessed changes in myocardial native T1 and T2 values after supine exercise stress in healthy subjects and in patients with suspected ischemia as potential imaging markers of ischemia. BACKGROUND: With emerging data on the long-term retention of gadolinium in the body and brain, there is a need for an alternative noncontrast cardiovascular magnetic resonance (CMR)-based myocardial ischemia assessment. METHODS: Twenty-eight healthy adult subjects and 14 patients with coronary artery disease (CAD) referred for exercise stress and/or rest single-photon emission computed tomography/myocardial perfusion imaging (SPECT/MPI) for evaluation of chest pain were prospectively enrolled. Free-breathing myocardial native T1 and T2 mapping were performed before and after supine bicycle exercise stress using a CMR-compatible supine ergometer positioned on the MR table. Differences in T1 rest, T2 rest and T1 post-exercise, T2 post-exercise values were calculated as T1 and T2 reactivity, respectively. RESULTS: The mean exercise intensity was 104 W, with exercise duration of 6 to 12 min. After exercise, native T1 was increased in healthy subjects (p < 0.001). T1 reactivity, but not T2 reactivity, correlated with the rate-pressure product as the index of myocardial blood flow during exercise (r = 0.62; p < 0.001). In patients with CAD, T1 reactivity was associated with the severity of myocardial perfusion abnormality on SPECT/MPI (normal: 4.9%; quartiles: 3.7% to 6.3%, mild defect: 1.2%, quartiles: 0.08% to 2.5%; moderate defect: 0.45%, quartiles: -0.35% to 1.4%; severe defect: 0.35%, quartiles: -0.44% to 0.8%) and had similar potential as SPECT/MPI to detect significant CAD (>50% diameter stenosis on coronary angiography). The area under the receiver-operating characteristic curve was 0.80 versus 0.72 (p = 0.40). The optimum cutoff value of T1 reactivity for predicting flow-limiting stenosis was 2.5%, with a sensitivity of 83% and a specificity of 92%, a negative predictive value of 96%, a positive predictive value of 71%, and an area under the curve of 0.86. CONCLUSIONS: Free-breathing stress/rest native T1 mapping, but not T2 mapping, can detect physiological changes in the myocardium during exercise. Our feasibility study in patients shows the potential of this technique as a method for detecting myocardial ischemia in patients with CAD without using a pharmacological stress agent.

Aortic regurgitation assessment by cardiovascular magnetic resonance imaging and transthoracic echocardiography: intermodality disagreement impacting on prediction of post-surgical left ventricular remodeling.

Transthoracic echocardiography (TTE) is the primary clinical imaging modality for the assessment of patients with isolated aortic regurgitation (AR) in whom TTE's linear left ventricular (LV) dimension is used to assess disease severity to guide aortic valve replacement (AVR), yet TTE is relatively limited with regards to its integrated semi-quantitative/qualitative approach. We therefore compared TTE and cardiovascular magnetic resonance (CMR) assessment of isolated AR and investigated each modality's ability to predict LV remodeling after AVR. AR severity grading by CMR and TTE were compared in 101 consecutive patients referred for CMR assessment of chronic AR. LV end-diastolic diameter and end-systolic diameter measurements by both modalities were compared. Twenty-four patients subsequently had isolated AVR. The pre-AVR estimates of regurgitation severity by CMR and TTE were correlated with favorable post-AVR LV remodeling. AR severity grade agreement between CMR and TTE was moderate (ρ = 0.317, P = 0.001). TTE underestimated CMR LV end-diastolic and LV end-systolic diameter by 6.6 mm (P < 0.001, CI 5.8-7.7) and 5.9 mm (P < 0.001, CI 4.1-7.6), respectively. The correlation of post-AVR LV remodeling with CMR AR grade (ρ = 0.578, P = 0.004) and AR volumes (R = 0.664, P < 0.001) was stronger in comparison to TTE (ρ = 0.511, P = 0.011; R = 0.318, P = 0.2). In chronic AR, CMR provides more prognostic relevant information than TTE in assessing AR severity. CMR should be considered in the management of chronic AR patients being considered for AVR.

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study.

Background Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); however, its clinical application is hindered by a lack of measurement standardization. Purpose To develop and evaluate a three-dimensional (3D) convolutional neural network (CNN)-based method for automated LGE scar quantification in patients with HCM. Materials and Methods We retrospectively identified LGE MRI data in a multicenter (n = 7) and multivendor (n = 3) HCM study obtained between November 2001 and November 2011. A deep 3D CNN based on U-Net architecture was used for LGE scar quantification. Independent CNN training and testing data sets were maintained with a 4:1 ratio. Stacks of short-axis MRI slices were split into overlapping substacks that were segmented and then merged into one volume. The 3D CNN per-site and per-vendor performances were evaluated with respect to manual scar quantification performed in a core laboratory setting using Dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analyses. Furthermore, the performance of 3D CNN was compared with that of two-dimensional (2D) CNN. Results This study included 1073 patients with HCM (733 men; mean age, 49 years ± 17 [standard deviation]). The 3D CNN-based quantification was fast (0.15 second per image) and demonstrated excellent correlation with manual scar volume quantification (r = 0.88, P < .001) and ratio of scar volume to total left ventricle myocardial volume (%LGE) (r = 0.91, P < .001). The 3D CNN-based quantification strongly correlated with manual quantification of scar volume (r = 0.82-0.99, P < .001) and %LGE (r = 0.90-0.97, P < .001) for all sites and vendors. The 3D CNN identified patients with a large scar burden (>15%) with 98% accuracy (202 of 207) (95% confidence interval [CI]: 95%, 99%). When compared with 3D CNN, 2D CNN underestimated scar volume (r = 0.85, P < .001) and %LGE (r = 0.83, P < .001). The DSC of 3D CNN segmentation was comparable among different vendors (P = .07) and higher than that of 2D CNN (DSC, 0.54 ± 0.26 vs 0.48 ± 0.29; P = .02). Conclusion In the hypertrophic cardiomyopathy population, a three-dimensional convolutional neural network enables fast and accurate quantification of myocardial scar volume, outperforms a two-dimensional convolutional neural network, and demonstrates comparable performance across different vendors. © RSNA, 2019 Online supplemental material is available for this article.

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.

BACKGROUND: Hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) are both associated with an increased left ventricular (LV) wall thickness. Whilst LV ejection fraction is frequently normal in both, LV strain assessment could differentiate between the diseases. We sought to establish if cardiovascular magnetic resonance myocardial feature tracking (CMR-FT), an emerging method allowing accurate assessment of myocardial deformation, differentiates between both diseases. Additionally, CMR assessment of fibrosis and LV hypertrophy allowed association analyses and comparison of diagnostic capacities. METHODS: Two-hundred twenty-four consecutive subjects (53 HHD, 107 HCM, and 64 controls) underwent 1.5T CMR including native myocardial T1 mapping and late gadolinium enhancement (LGE). Global longitudinal strain (GLS) was assessed by CMR-FT (CVi42, Circle Cardiovascular Imaging Inc.). RESULTS: GLS was significantly higher in HCM patients (-14.7±3.8 vs. -16.5±3.3% [HHD], P = 0.004; or vs. -17.2±2.0% [controls], P<0.001). GLS was associated with LV mass index (HHD, R = 0.419, P = 0.002; HCM, R = 0.429, P<0.001), and LV ejection fraction (HHD, R = -0.493, P = 0.002; HCM, R = -0.329, P<0.001). In HCM patients, GLS was also associated with global native T1 (R = 0.282, P = 0.003), and LGE volume (ρ = 0.380, P<0.001). Discrimination between HHD and HCM by GLS (c = 0.639, 95% confidence interval [CI] 0.550-0.729) was similar to LV mass index (c = 0.643, 95% CI 0.556-0.731), global myocardial native T1 (c = 0.718, 95% CI 0.638-0.799), and LGE volume (c = 0.680, 95% CI 0.585-0.775). CONCLUSION: CMR-FT GLS differentiates between HHD and HCM. In HCM patients GLS is associated with myocardial fibrosis. The discriminatory capacity of CMR-FT GLS is similar to LV hypertrophy and fibrosis imaging markers.

Local Conduction Velocity in the Presence of Late Gadolinium Enhancement and Myocardial Wall Thinning: A Cardiac Magnetic Resonance Study in a Swine Model of Healed Left Ventricular Infarction.

BACKGROUND: Conduction velocity (CV) is an important property that contributes to the arrhythmogenicity of the tissue substrate. The aim of this study was to investigate the association between local CV versus late gadolinium enhancement (LGE) and myocardial wall thickness in a swine model of healed left ventricular infarction. METHODS: Six swine with healed myocardial infarction underwent cardiovascular magnetic resonance imaging and electroanatomic mapping. Two healthy controls (one treated with amiodarone and one unmedicated) underwent electroanatomic mapping with identical protocols to establish the baseline CV. CV was estimated using a triangulation technique. LGE+ regions were defined as signal intensity >2 SD than the mean of remote regions, wall thinning+ as those with wall thickness <2 SD than the mean of remote regions. LGE heterogeneity was defined as SD of LGE in the local neighborhood of 5 mm and wall thickness gradient as SD within 5 mm. Cardiovascular magnetic resonance and electroanatomic mapping data were registered, and hierarchical modeling was performed to estimate the mean difference of CV (LGE+/-, wall thinning+/-), or the change of the mean of CV per unit change (LGE heterogeneity, wall thickness gradient). RESULTS: Significantly slower CV was observed in LGE+ (0.33±0.25 versus 0.54±0.36 m/s; P<0.001) and wall thinning+ regions (0.38±0.28 versus 0.55±0.37 m/s; P<0.001). Areas with greater LGE heterogeneity ( P<0.001) and wall thickness gradient ( P<0.001) exhibited slower CV. CONCLUSIONS: Slower CV is observed in the presence of LGE, myocardial wall thinning, high LGE heterogeneity, and a high wall thickness gradient. Cardiovascular magnetic resonance may offer a valuable imaging surrogate for estimating CV, which may support noninvasive identification of the arrhythmogenic substrate.

Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy.

OBJECTIVES: This study sought to examine the diagnostic ability of radiomic texture analysis (TA) on quantitative cardiovascular magnetic resonance images to differentiate between hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM). BACKGROUND: HHD and HCM are associated with increased left ventricular wall thickness (LVWT). Contemporary guidelines define HCM as LVWT ≥15 mm that is unexplained by other disease, which complicates diagnosis in cases of co-occurrences. Conventional global native T1 mapping involves calculation of mean T1 values as a surrogate for fibrosis. However, there may be differences in its spatial localization, such as diffuse and more focal fibrosis in HHD and HCM, respectively. METHODS: This study identified 232 subjects (53 with HHD, 108 with HCM, and 71 control subjects) for TA who consecutively underwent free-breathing multislice native T1 mapping. Four sets of texture descriptors were applied to capture spatially dependent and independent pixel statistics. Six texture features were sequentially selected with the best discriminatory capacity between HHD and HCM and were tested using a support vector machine (SVM) classifier. Each disease group was randomly split 4:1 (feature selection/test validation), in which the reproducibility of the pattern was analyzed in the test validation dataset. RESULTS: The selected texture features provided the maximum diagnostic accuracy of 86.2% (c-statistic: 0.820; 95% confidence interval [CI]: 0.769 to 0.903) using the SVM. For the test validation dataset, the accuracy of the pattern remained high at 80.0% (c-statistic: 0.89; 95% CI: 0.77 to 1.00). Global native T1, with an accuracy of 64%, separated HHD and HCM patients modestly (c-statistic: 0.549; 95% CI: 0.452 to 0.640). CONCLUSIONS: Radiomics analysis of native T1 images discriminates between HHD and HCM patients and provides incremental value over global native T1 mapping.

Black blood myocardial T2 mapping.

PURPOSE: To develop a black blood heart-rate adaptive T2 -prepared balanced steady-state free-precession (BEATS) sequence for myocardial T2 mapping. METHODS: In BEATS, blood suppression is achieved by using a combination of preexcitation and double inversion recovery pulses. The timing and flip angles of the preexcitation pulse are auto-calculated in each patient based on heart rate. Numerical simulations, phantom studies, and in vivo studies were conducted to evaluate the performance of BEATS. BEATS T2 maps were acquired in 36 patients referred for clinical cardiac MRI and in 1 swine with recent myocardial infarction. Two readers assessed all images acquired in patients to identify the presence of artifacts associated with slow blood flow. RESULTS: Phantom experiments showed that the BEATS sequence provided accurate T2 values over a wide range of simulated heart rates. Black blood myocardial T2 maps were successfully obtained in all subjects. No significant difference was found between the average T2 measurements obtained from the BEATS and conventional bright-blood T2 ; however, there was a decrease in precision using the BEATS sequence. A suppression of the blood pool resulted in sharper definition of the blood-myocardium border and reduced partial voluming effect. The subjective assessment showed that 16% (18 out of 108) of short-axis slices have residual blood artifacts (12 in the apical slice, 4 in the midventricular slice, and 2 in the basal slice). CONCLUSION: The BEATS sequence yields dark blood myocardial T2 maps with better definition of the blood-myocardium border. Further studies are warranted to evaluate diagnostic accuracy of black blood T2 mapping.

Integrated motion correction and dictionary learning for free-breathing myocardial T1 mapping.

PURPOSE: To develop and evaluate an integrated motion correction and dictionary learning (MoDic) technique to accelerate data acquisition for myocardial T1 mapping with improved accuracy. METHODS: MoDic integrates motion correction with dictionary learning-based reconstruction. A random undersampling scheme was implemented for slice-interleaved T1 mapping sequence to allow prospective undersampled data acquisition. Phantom experiments were performed to evaluate the effect of reconstruction on T1 measurement. In vivo T1 mappings were acquired in 8 healthy subjects using 6 different acceleration approaches: uniform or randomly undersampled k-space data with reduction factors (R) of 2, 3, and 4. Uniform undersampled data were reconstructed with SENSE, and randomly undersampled k-space data were reconstructed using dictionary learning, compressed sensing SENSE, and MoDic methods. Three expert readers subjectively evaluated the quality of T1 maps using a 4-point scoring system. The agreement between T1 values was assessed by Bland-Altman analysis. RESULTS: In the phantom study, the accuracy of T1 measurements improved with increasing reduction factors ( - 31 ± 35 ms, - 13 ± 18 ms, and - 5 ± 11 ms for reduction factor (R) = 2 to 4, respectively). The image quality of in vivo T1 maps assessed by subjective scoring using MoDic was similar to that of SENSE at R = 2 (P = .61) but improved at R = 3 and 4 (P < .01). The scores of dictionary learning (2.98 ± 0.71, 2.91 ± 0.60, and 2.67 ± 0.71 for R = 2 to 4) and CS-SENSE (3.32 ± 0.42, 3.05 ± 0.43, and 2.53 ± 0.43) were lower than those of MoDic (3.48 ± 0.46, 3.38 ± 0.52, and 2.9 ± 0.60) for all reduction factors (P < .05 for all). CONCLUSION: The MoDic method accelerates data acquisition for myocardial T1 mapping with improved T1 measurement accuracy.