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.
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.
BACKGROUND: With the increasing potential of radiomics, repeatability and reproducibility are important issues that remain to be assessed. PURPOSE: To investigate reproducibility of myocardial radiomic features in cardiac MRI images. MATERIALS AND METHODS: Test/retest studies were performed on a 3T MRI system using commonly used cardiac MRI sequences of cine-balanced steady-state free-precession (cine- bSSFP), T1-weighted and T2-weighted imaging, and quantitative T1 and T2 mapping in phantom experiments and 10 healthy participants (29 (22 – 64) years). Additionally, we assessed repeatability in 51 patients (56 (25 – 79) years)) who were imaged twice within the same imaging session. Three readers independently delineated the myocardium to investigate inter-/intra-observer reproducibility of radiomic features. A total of 1023 radiomic features were extracted using PyRadiomics with 11 image filters and six feature families. The intraclass correlation coefficient (ICC) was estimated to assess reproducibility and repeatability, and features of ICC ≥ 0.8 were considered reproducible. RESULTS: Different reproducibility patterns were observed among sequences in in-vivo test/retest studies. In cine-bSSFP, gray level run-length matrix (GLRLM) was the most reproducible feature family, and the wavelet low pass filter applied horizontally and vertically (wavelet-LL) was the most reproducible image filter. In T1 and T2 maps, intensity- based statistics (firstorder) and gray level co-occurrence matrix (GLCM) were the most reproducible feature families, without a dominant reproducible image filter. Across all sequences, ‘gray level non-uniformity’ was the most frequently identified reproducible feature name. In inter-/intra-observer reproducibility studies, only 32-47 % and 61-73 % of features were identified reproducible in inter-observer and intra-observer studies, respectively. CONCLUSION: Only a small subset of myocardial radiomic features is reproducible and these reproducible radiomic features vary among different sequences.
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.
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.
PURPOSE To assess the performance of an automated myocardial T2 and extracellular volume (ECV) quantification method using transfer learning of a fully convolutional neural network (CNN) pretrained to segment the myocardium on T1 mapping images.
MATERIALS AND METHODS
A single CNN previously trained and tested using 11 550 manually segmented native T1-weighted images was used to segment the myocardium for automated myocardial T2 and ECV quantification. Reference measurements from 1525 manually processed T2 maps and 1525 ECV maps (from 305 patients) were used to evaluate the performance of the pretrained network. Correlation coefficient (R) and Bland-Altman analysis were used to assess agreement between automated and reference values on per-patient, per-slice, and per-segment analyses. Furthermore, transfer learning effectiveness in the CNN was evaluated by comparing its performance to four CNNs trained using manually segmented T2-weighted and postcontrast T1-weighted images and initialized using random-weights or weights of the pretrained CNN.
RESULTS T2 and ECV measurements using the pretrained CNN strongly correlated with reference values in per-patient (T2: R = 0.88, 95% confidence interval [CI]: 0.85, 0.91; ECV: R = 0.91, 95% CI: 0.89, 0.93), per-slice (T2: R = 0.83, 95% CI: 0.81, 0.85; ECV: R = 0.84, 95% CI: 0.82, 0.86), and per-segment (T2: R = 0.75, 95% CI: 0.74, 0.77; ECV: R = 0.76, 95% CI: 0.75, 0.77) analyses. In Bland-Altman analysis, the automatic and reference values were in good agreement in per-patient (T2: 0.3 msec ± 2.9; ECV: -0.3% ± 1.7), per-slice (T2: 0.1 msec ± 4.6; ECV: -0.3% ± 2.5), and per-segment (T2: 0.0 msec ± 6.5; ECV: -0.4% ± 3.5) analyses. The performance of the pretrained network was comparable to networks refined or trained from scratch using additional manually segmented images.
CONCLUSION Transfer learning extends the utility of pretrained CNN-based automated native T1 mapping analysis to T2 and ECV mapping without compromising performance.
AIMS Multielectrode mapping catheters can be advantageous for identifying surviving myocardial bundles in scar. This study aimed to evaluate the utility of a new multielectrode catheter with increased number of small and closely spaced electrodes for mapping ventricles with healed infarction.
METHODS AND RESULTS
In 12 swine (four healthy and eight with infarction), the left ventricle was mapped with investigational (OctarayTM) and standard (PentarayTM) multielectrode mapping catheters. The investigational catheter has more electrodes (48 vs. 20), each with a smaller surface area (0.9 vs. 2.0mm2) and spacing is fixed at 2mm (vs. 2–6–2 mm). Electrogram (EGM) characteristics, mapping efficiency and scar description were compared between the catheters and late gadolinium enhancement (LGE). Electrogram acquisition rate was faster with the investigational catheter (814 ± 126 vs.148 ± 58 EGM/min, P = 0.02) resulting in higher density maps (38 ± 10.3 vs. 10.1 ± 10.4 EGM/cm2, P = 0.02). Bipolar voltage amplitude was similar between the catheters in normal and infarcted myocardium (P = 0.265 and P = 0.44) and the infarct surface area was similar between the catheters (P = 0.12) and corresponded to subendocardial LGE. The investigational catheter identified a higher proportion of near-field local abnormal ventricular activities within the low-voltage area (53 ± 16% vs. 34 ± 16%, P = 0.03) that were considered far-field EGMs by the standard catheter. The investigational catheter was also advantageous for mapping haemodymically non-tolerated ventricular tachycardias due to its higher acquisition rate (P < 0.001).
CONCLUSION A novel multielectrode mapping catheter with higher number of small, and closely spaced electrodes increases the mapping speed, EGM density and the ability to recognize low amplitude near-field EGMs in ventricles with healed 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.
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.
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.
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.
BACKGROUND Quantifying reproducibility of native T1 and T2 mapping over a long period (>1 year) is necessary to assess whether changes in T1 and T2 over repeated sessions in a longitudinal study are associated with variability due to underlying tissue composition or technical confounders.
OBJECTIVES To carry out a single-center phantom study to 1) investigate measurement reproducibility of slice-interleaved T1 (STONE) and T2 mapping over 20 months, 2) quantify sources of variability, and 3) compare reproducibility and measurements against reference spin-echo measurements.
METHODS MR imaging was performed on a 1.5 Tesla Philips Achieva scanner every 2–3 weeks over 20 months using the T1MES phantom. In each session, slice-interleaved T1 and T2 mapping was repeated 3 times for 5 slices, and maps were reconstructed using both 2-parameter and 3-parameter fit models. Reproducibility between sessions, and repeatability between repetitions and slices were evaluated using coefficients of variation (CV). Different sources of variability were quantified using variance decomposition analysis. The slice-interleaved measurement was compared to the spin-echo reference and MOLLI.
RESULTS Slice-interleaved T1 had excellent reproducibility and repeatability with a CV<2%. The main sources of T1 variability were temperature in 2-parameter maps, and slice in 3-parameter maps. Superior between-session reproducibility to the spin-echo T1 was shown in 2 parameter maps, and similar reproducibility in 3-parameter maps. Superior reproducibility to MOLLI T1 was also shown. Similar measurements to the spin-echo T1 were observed with linear regression slopes of 0.94–0.99, but slight underestimation. Slice-interleaved T2 showed good reproducibility and repeatability with a CV<7%. The main source of T2 variability was slice location/orientation. Between-session reproducibility was lower than the spin-echo T2 reference and showed good measurement agreement with linear regression slopes of 0.78–1.06.
CONCLUSIONS Slice-interleaved T1 and T2 mapping sequences yield excellent long-term reproducibility over 20 months.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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).
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.
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.
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).
Radiomics analysis of native T1 images discriminates between HHD and HCM patients and provides incremental value over global native T1 mapping.
Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping.
A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers.
The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses).
The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.
To develop a gadolinium-free cardiac MR technique that simultaneously exploits native T1 and magnetization transfer (MT) contrast for the imaging of myocardial infarction.
A novel hybrid T one and magnetization transfer (HYTOM) method was developed based on the modified look-locker inversion recovery (MOLLI) sequence, with a train of MT-prep pulses placed before the balanced SSFP (bSSFP) readout pulses. Numerical simulations, based on Bloch-McConnell equations, were performed to investigate the effects of MT induced by (1) the bSSFP readout pulses, and (2) the MT-prep pulses, on the measured, "apparent," native T1 values. The HYTOM method was then tested on 8 healthy adult subjects, 6 patients, and a swine with prior myocardial infarction (MI). The resulting imaging contrast between normal myocardium and infarcted tissues was compared with that of MOLLI. Late gadolinium enhancement (LGE) images were also obtained for infarct assessment in patients and swine.
Numerical simulation and in vivo studies in healthy volunteers demonstrated that MT effects, resulting from on-resonance bSSFP excitation pulses and off-resonance MT-prep pulses, reduce the measured T1 in both MOLLI and HTYOM. In vivo studies in patients and swine showed that the HYTOM sequence can identify locations of MI, as seen on LGE. Furthermore, the HYTOM method yields higher myocardium-to-scar contrast than MOLLI (contrast-to-noise ratio: 7.33 ± 1.67 vs. 3.77 ± 0.66, P < 0.01).
The proposed HYTOM method simultaneously exploits native T1 and MT contrast and significantly boosts the imaging contrast for myocardial infarction.
To develop and evaluate an integrated motion correction and dictionary learning (MoDic) technique to accelerate data acquisition for myocardial T1 mapping with improved accuracy.
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.
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).
The MoDic method accelerates data acquisition for myocardial T1 mapping with improved T1 measurement accuracy.
Left atrial ( LA ) enlargement is a marker for increased risk of atrial fibrillation ( AF ). However, LA remodeling is a complex process that is poorly understood, and LA geometric remodeling may also be associated with the development of AF . We sought to determine whether LA spherical remodeling or its temporal change predict late AF recurrence after pulmonary vein isolation ( PVI ).
METHODS AND RESULTS:
Two hundred twenty-seven consecutive patients scheduled for their first PVI for paroxysmal or persistent AF who underwent cardiovascular magnetic resonance before and within 6 months after PVI were retrospectively identified. The LA sphericity index was computed as the ratio of the measured LA maximum volume to the volume of a sphere with maximum LA length diameter. During mean follow-up of 25 months, 88 patients (39%) experienced late recurrence of AF. Multivariable Cox regression analyses identified an increased pre- PVI LA sphericity index as an independent predictor of late AF recurrence (hazard ratio, 1.32; 95% confidence interval, 1.07-1.62, P=0.009). Patients in the highest LA sphericity index tertile were at highest risk of late recurrence (highest versus lowest: 59% versus 28%; P<0.001). The integration of the LA sphericity index to the LA minimum volume index and passive emptying fraction provided important incremental prognostic information for predicting late AF recurrence post PVI (categorical net reclassification improvement, 0.43; 95% confidence interval, 0.16-0.69, P=0.001).
The assessment of pre- PVI LA geometric remodeling provides incremental prognostic information regarding late AF recurrence and may be useful to identify those for whom PVI has reduced success or for whom more aggressive ablation or medications may be useful.
atrial fibrillation; cardiovascular magnetic resonance; late recurrence; left atrial sphericity index; left atrial volume; pulmonary vein isolation
Visualization of the complex 3D architecture of myocardial scar could improve guidance of radio-frequency ablation in the treatment of ventricular tachycardia (VT). In this study, we sought to develop a framework for 3D holographic visualization of myocardial scar, imaged using late gadolinium enhancement (LGE), on the augmented reality HoloLens. 3D holographic LGE model was built using the high-resolution 3D LGE image. Smooth endo/epicardial surface meshes were generated using Poisson surface reconstruction. For voxel-wise 3D scar model, every scarred voxel was rendered into a cube which carries the actual resolution of the LGE sequence. For surface scar model, scar information was projected on the endocardial surface mesh. Rendered layers were blended with different transparency and color, and visualized on HoloLens. A pilot animal study was performed where 3D holographic visualization of the scar was performed in 5 swines who underwent controlled infarction and electroanatomic mapping to identify VT substrate. 3D holographic visualization enabled assessment of the complex 3D scar architecture with touchless interaction in a sterile environment. Endoscopic view allowed visualization of scar from the ventricular chambers. Upon completion of the animal study, operator and mapping specialist independently completed the perceived usefulness questionnaire in the six-item usefulness scale. Operator and mapping specialist found it useful (usefulness rating: operator, 5.8; mapping specialist, 5.5; 1-7 scale) to have scar information during the intervention. HoloLens 3D LGE provides a true 3D perception of the complex scar architecture with immersive experience to visualize scar in an interactive and interpretable 3D approach, which may facilitate MR-guided VT ablation.
Left bundle branch block (LBBB) is associated with abnormal left ventricular (LV) contraction, and is frequently associated with co-morbid cardiovascular disease, but the effect of an isolated (i.e. in the absence of cardiovascular dissease) LBBB on biventricular volumes and ejection fraction (EF) is not well characterized. The objective of this study was to compare LV and right ventricular (RV) volumes and EF in adults with an isolated LBBB to matched healthy controls and to population-derived normative values, using cardiovascular magnetic resonance (CMR) imaging.
We reviewed our clinical echocardiography database and the Framingham Heart Study Offspring cohort CMR database to identify adults with an isolated LBBB. Age-, sex-, hypertension-status, and body-surface area (BSA)-matched controls were identified from the Offspring cohort. All study subjects were scanned using the same CMR hardware and imaging sequence. Isolated-LBBB cases were compared with matched controls using Wilcoxon paired signed-rank test, and to normative reference values via Z-score.
Isolated-LBBB subjects (n = 18, 10F) ranged in age from 37 to 82 years. An isolated LBBB was associated with larger LV end-diastolic and end-systolic volumes (both p < 0.01) and lower LVEF (56+/- 7% vs. 68+/- 6%; p <0.001) with similar myocardial contraction fraction. LVEF in isolated LBBB was nearly two standard deviations (Z = - 1.95) below mean sex and age-matched group values. LV stroke volume, cardiac output, and mass, and all RV parameters were similar (p = NS) between the groups.
Adults with an isolated LBBB have greater LV volumes and markedly reduced LVEF, despite the absence of overt cardiovascular disease. These data may be useful toward the clinical interpretation of imaging studies performed on patients with an isolated LBBB.
Wearable and implantable devices require conductive, stretchable and biocompatible materials. However, obtaining composites that simultaneously fulfil these requirements is challenging due to a trade-off between conductivity and stretchability. Here, we report on Ag–Au nanocomposites composed of ultralong gold-coated silver nanowires in an elastomeric block-copolymer matrix. Owing to the high aspect ratio and percolation network of the Ag–Au nanowires, the nanocomposites exhibit an optimized conductivity of 41,850 S cm−1 (maximum of 72,600 S cm−1). Phase separation in the Ag–Au nanocomposite during the solvent-drying process generates a microstructure that yields an optimized stretchability of 266% (maximum of 840%). The thick gold sheath deposited on the silver nanowire surface prevents oxidation and silver ion leaching, making the composite biocompatible and highly conductive. Using the nanocomposite, we successfully fabricate wearable and implantable soft bioelectronic devices that can be conformally integrated with human skin and swine heart for continuous electrophysiological recording, and electrical and thermal stimulation.
BACKGROUND: Recent studies demonstrated a strong association between atrial fibrillation (AF) and epicardial fat around the left atrium (LA). We sought to assess whether epicardial fat volume around the LA is associated with AF, and to determine the additive value of LA-epicardial fat measurements to LA structural remodeling for identifying patients with AF using 3-dimensional multi-echo Dixon fat-water separated cardiovascular magnetic resonance. METHODS AND RESULTS: A total of 105 subjects were studied: 53 patients with a history of AF and 52 age-matched patients with other cardiovascular diseases but no history of AF. The 3-dimensional multi-echo Dixon fat-water separated sequence was performed for LA-epicardial fat measurements. AF patients had significantly greater LA-epicardial fat (28.9±12.3 and 14.2±7.3 mL for AF and non-AF, respectively; <0.001) and LA volume (110.8±38.2 and 89.7±30.3 mL for AF and non-AF, respectively; =0.002). LA-epicardial fat adjusted for LA volume was still higher in patients with AF compared with those without AF (<0.001). LA-epicardial fat and hypertension were independently associated with the risk of AF (odds ratio, 1.17; 95% confidence interval, 1.10%-1.25%, <0.001, and odds ratio, 3.29; 95% confidence interval, 1.17%-9.27%, =0.03, respectively). In multivariable logistic regression analysis adjusted for body surface area, LA-epicardial fat remained significant and an increase per mL was associated with a 42% increase in the odds of AF presence (odds ratio, 1.42; 95% confidence interval, 1.23%-1.62%, <0.001). Combined assessment of LA-epicardial fat and LA volume provided greater discriminatory performance for detecting AF than LA volume alone (c-statistic=0.88 and 0.74, respectively, DeLong test; <0.001). CONCLUSIONS: Cardiovascular magnetic resonance 3-dimensional Dixon-based LA-epicardial fat volume is significantly increased in AF patients. LA-epicardial fat measured by 3-dimensional Dixon provides greater performance for detecting AF beyond LA structural remodeling.
PURPOSE: To develop a black blood heart-rate adaptive T -prepared balanced steady-state free-precession (BEATS) sequence for myocardial T 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 T 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 T values over a wide range of simulated heart rates. Black blood myocardial T maps were successfully obtained in all subjects. No significant difference was found between the average T measurements obtained from the BEATS and conventional bright-blood T ; 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 T maps with better definition of the blood-myocardium border. Further studies are warranted to evaluate diagnostic accuracy of black blood T mapping.
Three-dimensional (3D) printing technologies are increasingly used to convert medical imaging studies into tangible (physical) models of individual patient anatomy, allowing physicians, scientists, and patients an unprecedented level of interaction with medical data. To date, virtually all 3D-printable medical data sets are created using traditional image thresholding, subsequent isosurface extraction, and the generation of .stl surface mesh file formats. These existing methods, however, are highly prone to segmentation artifacts that either over or underexaggerate the features of interest, thus resulting in anatomically inaccurate 3D prints. In addition, they often omit finer detailed structures and require time- and labor-intensive processes to visually verify their accuracy. To circumvent these problems, we present a bitmap-based multimaterial 3D printing workflow for the rapid and highly accurate generation of physical models directly from volumetric data stacks. This workflow employs a thresholding-free approach that bypasses both isosurface creation and traditional mesh slicing algorithms, hence significantly improving speed and accuracy of model creation. In addition, using preprocessed binary bitmap slices as input to multimaterial 3D printers allows for the physical rendering of functional gradients native to volumetric data sets, such as stiffness and opacity, opening the door for the production of biomechanically accurate models.
Low scar-to-blood contrast in late gadolinium enhanced (LGE) MRI limits the visualization of scars adjacent to the blood pool. Nulling the blood signal improves scar detection but results in lack of contrast between myocardium and blood, which makes clinical evaluation of LGE images more difficult.
GB-LGE contrast is achieved through partial suppression of the blood signal using T2magnetization preparation between the inversion pulse and acquisition. The timing parameters of GB-LGE sequence are determined by optimizing a cost-function representing the desired tissue contrast. The proposed 3D GB-LGE sequence was evaluated using phantoms, human subjects (n = 45) and a swine model of myocardial infarction (n = 5). Two independent readers subjectively evaluated the image quality and ability to identify and localize scarring in GB-LGE compared to black-blood LGE (BB-LGE) (i.e., with complete blood nulling) and conventional (bright-blood) LGE.
GB-LGE contrast was successfully generated in phantoms and all in-vivo scans. The scar-to-blood contrast was improved in GB-LGE compared to conventional LGE in humans (1.1 ± 0.5 vs. 0.6 ± 0.4, P < 0.001) and in animals (1.5 ± 0.2 vs. -0.03 ± 0.2). In patients, GB-LGE detected more tissue scarring compared to BB-LGE and conventional LGE. The subjective scores of the GB-LGE ability for localizing LV scar and detecting papillary scar were improved as compared with both BB-LGE (P < 0.024) and conventional LGE (P < 0.001). In the swine infarction model, GB-LGE scores for the ability to localize LV scar scores were consistently higher than those of both BB-LGE and conventional-LGE.
GB-LGE imaging improves the ability to identify and localize myocardial scarring compared to both BB-LGE and conventional LGE. Further studies are warranted to histologically validate GB-LGE.
Myocardial infarction (MI) survivors are at risk of complications including heart failure and malignant arrhythmias.
We undertook serial imaging of swine following MI with the aim of characterizing the longitudinal left ventricular (LV) remodeling in a translational model of ischemia-reperfusion-mediated MI.
Eight Yorkshire swine underwent mid left anterior descending coronary artery balloon occlusion to create an ischemia-reperfusion experimental model of MI.
1.5T Philips Achieva scanner. Serial cardiac MRI was performed at 16, 33, and 62 days post-MI, including cine imaging, native and postcontrast T1 , T2 and dark-blood late gadolinium enhanced (DB-LGE) scar imaging.
Regions of interest were selected on the parametric maps to assess native T1 and T2 in the infarct and in remote tissue. Volume of enhanced tissue, nonenhanced tissue, and gray zone were assessed from DB-LGE imaging. Volumes, cardiac function, and strain were calculated from cine imaging.
Parameters estimated at more than two timepoints were compared with a one-way repeated measures analysis of variance. Parametric mapping data were analyzed using a generalized linear mixed model corrected for multiple observations. A result was considered statistically significant at P < 0.05.
All animals developed anteroseptal akinesia and hyperenhancement on DB-LGE with a central core of nonenhancing tissue. Mean hyperenhancement volume did not change during the observation period, while the central core contracted from 2.2 ± 1.8 ml at 16 days to 0.08 ± 0.19 ml at 62 days (P = 0.008). Native T1 of ischemic myocardium increased from 1173 ± 93 msec at 16 days to 1309 ± 97 msec at 62 days (P < 0.001). Mean radial and circumferential strain rate magnitude in remote myocardium increased with time from the infarct (P < 0.05).
In this swine model of MI, serial quantitative cardiac MR exams allow characterization of LV remodeling and scar formation.
LEVEL OF EVIDENCE:
2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.