Publications

Objectives
The aim of this study was to define the variability of maximal wall thickness (MWT) measurements across modalities and predict its impact on care in patients with hypertrophic cardiomyopathy (HCM).
Background
Left ventricular MWT measured by echocardiography or cardiovascular magnetic resonance (CMR) contributes to the diagnosis of HCM, stratifies risk, and guides key decisions, including whether to place an implantable cardioverter-defibrillator (ICD).
Methods
A 20-center global network provided paired echocardiographic and CMR data sets from patients with HCM, from which 17 paired data sets of the highest quality were selected. These were presented as 7 randomly ordered pairs (at 6 cardiac conferences) to experienced readers who report HCM imaging in their daily practice, and their MWT caliper measurements were captured. The impact of measurement variability on ICD insertion decisions was estimated in 769 separately recruited multicenter patients with HCM using the European Society of Cardiology algorithm for 5-year risk for sudden cardiac death.
Results
MWT analysis was completed by 70 readers (from 6 continents; 91% with >5 years’ experience). Seventy-nine percent and 68% scored echocardiographic and CMR image quality as excellent. For both modalities (echocardiographic and then CMR results), intramodality inter-reader MWT percentage variability was large (range –59% to 117% [SD ±20%] and –61% to 52% [SD ±11%], respectively). Agreement between modalities was low (SE of measurement 4.8 mm; 95% CI 4.3 mm-5.2 mm; r = 0.56 [modest correlation]). In the multicenter HCM cohort, this estimated echocardiographic MWT percentage variability (±20%) applied to the European Society of Cardiology algorithm reclassified risk in 19.5% of patients, which would have led to inappropriate ICD decision making in 1 in 7 patients with HCM (8.7% would have had ICD placement recommended despite potential low risk, and 6.8% would not have had ICD placement recommended despite intermediate or high risk).
Conclusions
Using the best available images and experienced readers, MWT as a biomarker in HCM has a high degree of inter-reader variability and should be applied with caution as part of decision making for ICD insertion. Better standardization efforts in HCM recommendations by current governing societies are needed to improve clinical decision making in patients with HCM.

Background:
Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important.
Purpose:
To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF-hospitalization.
Study Type:
Retrospective.
Population:
A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%).
Field strength:
A 1.5 T, balanced steady-state free precession (bSSFP) sequence.
Assessment:
Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI-based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF-hospitalization.
Statistical Tests:
ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P-value <0.05 was considered statistically significant.
Results:
During follow-up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI-based ML model using the XGBoost algorithm provided a significantly superior prediction of HF-hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and −15%, respectively.
Data Conclusions:
Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF.

Purpose: The purpose of this study was to compare the performance of deep learning

networks trained with complex-valued and magnitude images in suppressing the aliasing

artifact for highly accelerated real-time cine MRI.

Methods: Two3DU-netmodels(Complex-Valued-NetandMagnitude-Net)wereimplemented

to suppress aliasing artifacts in real-time cine images. ECG-segmented cine images (n 503)

generated from both complex k-space data and magnitude-only DICOM were used to

synthetize radial real-time cine MRI. Complex-Valued-Net and Magnitude-Net were trained

withfullysampledandsynthetizedradialreal-timecinepairsgeneratedfromhighlyundersampled

(12-fold) complex k-space and DICOM images, respectively. Real-time cine was prospectively

acquired in 29 patients with 12-fold accelerated free-breathing tiny golden-angle radial sequence

and reconstructed with both Complex-Valued-Net and Magnitude-Net. Cardiac function, left-

ventricular (LV) structure, and subjective image quality [1(non-diagnostic)-5(excellent)] were

calculated from Complex-Valued-Net–and Magnitude-Net–reconstructed real-time cine

datasets and compared to those of ECG-segmented cine (reference).

Results: Free-breathing real-time cine reconstructed by both networks had high correlation

(all R2 >0.7) and good agreement (all p >0.05) with standard clinical ECG-segmented cine

with respect to LV function and structural parameters. Real-time cine reconstructed by

Complex-Valued-Net had superior image quality compared to images from Magnitude-Net

in terms of myocardial edge sharpness (Complex-Valued-Net 3.5 ±0.5; Magnitude-Net

2.6 ±0.5), temporal fidelity (Complex-Valued-Net 3.1 ±0.4; Magnitude-Net 2.1 ±0.4), and

artifact suppression (Complex-Valued-Net 3.1 ±0.5; Magnitude-Net 2.0 ±0.0), which

were all inferior to those of ECG-segmented cine (4.1 ±1.4, 3.9 ±1.0, and 4.0 ±1.1).

Conclusion: Compared to Magnitude-Net, Complex-Valued-Net produced improved

subjective image quality for reconstructed real-time cine images and did not show any

difference in quantitative measures of LV function and structure

Cardiovascular disease is the leading cause of death and a significant contributor of health care costs. Noninvasive imaging plays an essential role in the management of patients with cardiovascular disease. Cardiac magnetic resonance (MR) can noninvasively assess heart and vascular abnormalities, including biventricular structure/function, blood hemodynamics, myocardial tissue composition, microstructure, perfusion, metabolism, coronary microvascular function, and aortic distensibility/stiffness. Its ability to characterize myocardial tissue composition is unique among alternative imaging modalities in cardiovascular disease. Significant growth in cardiac MR utilization, particularly in Europe in the last decade, has laid the necessary clinical groundwork to position cardiac MR as an important imaging modality in the workup of patients with cardiovascular disease. Although lack of availability, limited training, physician hesitation, and reimbursement issues have hampered widespread clinical adoption of cardiac MR in the United States, growing clinical evidence will ultimately overcome these challenges. Advances in cardiac MR techniques, particularly faster image acquisition, quantitative myocardial tissue characterization, and image analysis have been critical to its growth. In this review article, we discuss recent advances in established and emerging cardiac MR techniques that are expected to strengthen its capability in managing patients with cardiovascular disease. 

BACKGROUND
Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced readers often use conventional cine sequences to accurately identify the myocardium borders.
PURPOSE
To develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification.
STUDY TYPE
Retrospective
POPULATION
A total of 191 hypertrophic cardiomyopathy patients: 1) 162 patients from two sites randomly split into training (50%; 81 patients), validation (25%, 40 patients), and testing (25%; 41 patients); and 2) an external testing dataset (29 patients) from a third site.
FIELD STRENGTH/SEQUENCE
1.5T, inversion-recovery segmented gradient-echo LGE and balanced steady-state free-precession cine sequences ASSESSMENT: Two convolutional neural networks (CNN) were trained for myocardium and scar segmentation, one with and one without LGE-Cine fusion. For CNN with fusion, the input was two aligned LGE and cine images at matched cardiac phase and anatomical location. For CNN without fusion, only LGE images were used as input. Manual segmentation of the datasets was used as reference standard.
STATISTICAL TESTS
Manual and CNN-based quantifications of LGE scar burden and of myocardial volume were assessed using Pearson linear correlation coefficients (r) and Bland-Altman analysis.
RESULTS
Both CNN models showed strong agreement with manual quantification of LGE scar burden and myocardium volume. CNN with LGE-Cine fusion was more robust than CNN without LGE-Cine fusion, allowing for successful segmentation of significantly more slices (603 [95%] vs. 562 (89%) of 635 slices; P < 0.001). Also, CNN with LGE-Cine fusion showed better agreement with manual quantification of LGE scar burden than CNN without LGE-Cine fusion (%Scar_LGE-cine = 0.82 × %Scar_manual , r = 0.84 vs. %Scar_LGE = 0.47 × %Scar_manual , r = 0.81) and myocardium volume (Volume_LGE-cine = 1.03 × Volume_manual , r = 0.96 vs. Volume_LGE = 0.91 × Volume_manual , r = 0.91).
DATA CONCLUSION
CNN based LGE-Cine fusion can improve the robustness and accuracy of automated scar quantification.

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

PURPOSE
To develop and evaluate a real-time phase contrast (PC) MRI protocol via complex-difference deep learning (DL) framework.

METHODS
DL used two 3D U-nets to separately filter aliasing artifact from radial real-time velocity-compensated and complex-difference images. U-nets were trained with synthetic real-time PC generated from electrocardiograph (ECG) -gated, breathhold, segmented PC (ECG-gated segmented PC) acquired at the ascending aorta of 510 patients. In 21 patients, free-breathing, ungated real-time (acceleration rate = 28.8) and ECG-gated segmented (acceleration rate = 2) PC were prospectively acquired at the ascending aorta. Hemodynamic parameters (cardiac output [CO], stroke volume [SV], and mean velocity at peak systole [peak mean velocity]) were measured for ECG-gated segmented and DL-filtered synthetic real-time PC and compared using Bland-Altman and linear regression analyses. Additionally, hemodynamic parameters were quantified from DL-filtered, compressed-sensing (CS) -reconstructed, and gridding reconstructed prospective real-time PC and compared to ECG-gated segmented PC.

RESULTS
Synthetic real-time PC with DL showed strong correlation (R > 0.98) and good agreement with ECG-gated segmented PC for quantified hemodynamic parameters (mean-difference: CO = −0.3 L/min, SV = −4.3 mL, peak mean velocity = −2.3 cm/s). On average, DL required 0.39 s/frame to filter prospective real-time PC, which was 4.6-fold faster than CS. Compared to CS, DL showed superior correlation, tighter limits of agreement (LOAs), better bias for peak mean velocity, and worse bias for CO and SV. Compared to gridding, DL showed similar correlation, tighter LOAs for CO and SV, similar bias for CO, and worse bias for SV and peak mean velocity.
CONCLUSION
The complex-difference DL framework accelerated real-time PCMRI by nearly 28-fold, enabling rapid free-running real-time assessment of flow hemodynamics.