Publications

PURPOSE
To develop and validate a saturation‐delay‐inversion recovery preparation, slice tracking and multi‐slice based sequence for measuring whole‐heart native T₁.

METHOD
The proposed free‐breathing sequence performs T₁ mapping of multiple left‐ventricular slices by slice‐interleaved acquisition to collect 10 electrocardiogram‐triggered single‐shot slice‐selective images for each slice. A saturation‐delay‐inversion recovery pulse is used for T₁ preparation. Prospective slice tracking by the diaphragm navigator and retrospective registration are used to reduce through‐plane and in‐plane motion, respectively. The proposed sequence was validated in both phantom and human subjects (12 healthy subjects and 15 patients who were referred for a clinical cardiac MR exam) and compared with saturation recovery single‐shot acquisition (SASHA) and modified Look‐Locker inversion recovery (MOLLI).

RESULTS
Phantom T₁ measured by the proposed sequence had excellent agreement (R² = 0.99) with the ground‐truth T₁ and was insensitive to heart rate. In both healthy subjects and patients, the proposed sequence yielded nine left‐ventricular T₁ maps per volume in less than 2 minutes (healthy volunteers: 1.8 ± 0.4 minutes; patients: 1.9 ± 0.2 minutes). The average T₁ of whole left ventricle for all healthy subjects and patients were 1560 ± 61 and 1535 ± 49 ms by SASHA, 1208 ± 42 and 1233 ± 56 ms by MOLLI5(3)3, and 1397 ± 34 and 1433 ± 56 ms by the proposed sequence, respectively. The corresponding coefficient of variation of T₁ were 6.2 ± 1.4% and 5.8 ± 1.6%, 5.3 ± 1.1% and 5.1 ± 0.8%, and 4.9 ± 0.8% and 4.5 ± 0.8%, respectively.

CONCLUSION
The proposed sequence enables quantification of whole heart T₁ with good accuracy and precision in less than 2 minutes during free breathing.

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-spaceand 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 andprovides superior image quality compared to kt-RASPS.

OBJECTIVES
This study sought to determine whether myocardial tissue heterogeneity scanned by native T1mapping 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.

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.

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
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.

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.