Publications

2022
Morales MA, Cirillo J, Nakata K, Kucukseymen S, Ngo LH, Izquierdo-Gardia D, Catana C, Nezafat R. Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis. J. Magn Reson Imaging 2022;Abstract
Background: Myocardial feature tracking (FT) provides a comprehensive analysis of myocardial deformation from cine balanced steady-state free-precession images (bSSFP). However, FT remains time-consuming, precluding its clinical adoption.
Purpose: To compare left-ventricular global radial strain (GRS) and global circumferential strain (GCS) values measured using automated DeepStrain analysis of short-axis cine images to those calculated using manual commercially available FT analysis.
Study Type: Retrospective, single-center.
Population: A total of 30 healthy subjects and 120 patients with cardiac disease for DeepStrain development. For evaluation, 47 healthy subjects (36 male, 53 5 years) and 533 patients who had undergone a clinical cardiac MRI (373 male, 59 ±14 years).
Field Strength/Sequence: bSSFP sequence at 1.5 T (Phillips) and 3 T (Siemens).
Assessment: Automated DeepStrain measurements of GRS and GCS were compared to commercially available FT (Circle, cvi42) measures obtained by readers with 1 year and 3 years of experience. Comparisons were performed overall and stratified by scanner manufacturer.
Statistical Tests: Paired t-test, linear regression slope, Pearson correlation coefficient (r).
Results: Overall, FT and DeepStrain measurements of GCS were not significantly different (P=0.207), but measures of GRS were significantly different. Measurements of GRS from Philips (slope =1.06 [1.03 1.08], r=0.85) and Siemens (slope =1.04 [0.99 1.09], r=0.83) data showed a very strong correlation and agreement between techniques. Measurements of GCS from Philips (slope =0.98 [0.98 1.01], r=0.91) and Siemens (slope =1.0 [0.96 1.03], r=0.88) data similarly showed a very strong correlation. The average analysis time per subject was 4.1 1.2 minutes for FT and 34.7 3.3 seconds for DeepStrain, representing a 7-fold reduction in analysis time.
Data Conclusion: This study demonstrated high correlation of myocardial GCS and GRS measurements between freely available fully automated DeepStrain and commercially available manual FT software, with substantial time-saving in the analysis.
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Morales MA, Assana S, Cai X, Chow K, Haji-Valizadeh H, Sai E, Tsao C, Matos J, Rodriguez J, Berg S, Whitehead N, Pierce P, Goddu B, Manning WJ, Nezafat R. An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance 2022;24(47):1-14.Abstract

Background
Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR.
Methods
A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal).
Results
The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [− 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [− 1.3, 15.3], P < 0.001) and LV ejection fraction (− 5.0% [− 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame.
Conclusions
Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR.

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Amyar A, Guo R, Cai X, Assana S, Chow K, Rodriguez J, Yankama T, Cirillo J, Pierce P, Goddu B, Ngo L, Nezafat R. Impact of deep learning architectures on accelerated cardiac T1 mapping using MyoMapNet. NMR in Biomedicine 2022;(e4794):1-13.Abstract

The objective of the current study was to investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T1 estimation using accelerated cardiac T1 mapping from four T1-weighted images collected after a single inversion pulse (Look-Locker 4 [LL4]). We implemented and tested three DL architectures for MyoMapNet: (a) a fully connected neural network (FC), (b) convolutional neural networks (VGG19, ResNet50), and (c) encoder-decoder networks with skip connections (ResUNet, U-Net). Modified Look-Locker inversion recovery (MOLLI) images from 749 patients at 3 T were used for training, validation, and testing. The first four T1-weighted images from MOLLI5(3)3 and/or MOLLI4(1)3(1)2 protocols were extracted to create accelerated cardiac T1 mapping data. We also prospectively collected data from 28 subjects using MOLLI and LL4 to further evaluate model performance. Despite rigorous training, conventional VGG19 and ResNet50 models failed to produce anatomically correct T1 maps, and T1 values had significant errors. While ResUNet yielded good quality maps, it significantly underestimated T1. Both FC and U-Net, however, yielded excellent image quality with good T1 accuracy for both native (FC/U-Net/MOLLI = 1217 ± 64/1208 ± 61/1199 ± 61 ms, all p < 0.05) and postcontrast myocardial T1 (FC/U-Net/MOLLI = 578 ± 57/567 ± 54/574 ± 55 ms, all p < 0.05). In terms of precision, the U-Net model yielded better T1 precision compared with the FC architecture (standard deviation of 61 vs. 67 ms for the myocardium for native [p < 0.05], and 31 vs. 38 ms [p < 0.05], for postcontrast). Similar findings were observed in prospectively collected LL4 data. It was concluded that U-Net and FC DL models in MyoMapNet enable fast myocardial T1 mapping using only four T1-weighted images collected from a single LL sequence with comparable accuracy. U-Net also provides a slight improvement in precision.

 

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Guo R, CHen Z, Amyar A, El-Rewaidy H, Assana S, Rodriguez J, Pierce P, Goddu B, Nezafat R. Improving accuracy of myocardial T1 estimation in MyoMapNet. Magnetic Resonance in Medicine 2022;:1-10.Abstract

Purpose: To improve the accuracy and robustness of T1 estimation by MyoMap-Net, a deep learning–based approach using 4 inversion-recovery T1-weighted images for cardiac T1 mapping.

Methods: MyoMapNet is a fully connected neural network for T1 estimation of an accelerated cardiac T1 mapping sequence, which collects 4 T1-weighted images by a single Look-Locker inversion-recovery experiment (LL4).MyoMap-Net was originally trained using in vivo data from the modified Look-Locker inversion recovery sequence, which resulted in significant bias and sensitivity to various confounders. This study sought to train MyoMapNet using signals generated from numerical simulations and phantom MR data under multiple simulated confounders. The trained model was then evaluated by phantom data scanned using new phantom vials that differed from those used for training. The performance of the new model was compared with modified Look-Locker inversion recovery sequence and saturation-recovery single-shot acquisition for measuring native and postcontrast T1 in 25 subjects.

Results: In the phantom study, T1 values measured by LL4 with MyoMapNet were highly correlated with reference values from the spin-echo sequence. Furthermore, the estimated T1 had excellent robustness to changes in flip angle and off-resonance. Native and postcontrast myocardium T1 at 3 Tesla measured by saturation-recovery single-shot acquisition, modified Look-Locker inversion recovery sequence, and MyoMapNet were 1483±46.6 ms and 791±45.8 ms, 1169±49.0 ms and 612±36.0 ms, and 1443±57.5 ms and 700±57.5 ms, respectively. The corresponding extracellular volumes were 22.90%±3.20%, 28.88%±3.48%, and 30.65%±3.60%, respectively.

Conclusion: Training MyoMapNet with numerical simulations and phantom data will improve the estimation of myocardial T1 values and increase its robustness to confounders while also reducing the overall T1 mapping estimation time to only 4 heartbeats.

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Guo R, Qi H, Amyar A, Cai X, Kucukseymen S, Haji-Valizadeh H, Rodriguez J, Paskavitz A, Pierce P, Goddu B, Thompson RB. Quantification of changes inmyocardial T1* values with exercise cardiac MRI using a free-breathing non-electrocardiograph radial imaging. Magnetic Resonance in Medicine 2022;:1-14.Abstract

Purpose: To develop and evaluate a free breathing non-electrocardiograph (ECG) myocardial T1* mapping sequence using radial imaging to quantify the changes in myocardial T1* between rest and exercise (T1*reactivity) in exercise cardiac MRI (Ex-CMR).
Methods: A free-running T1* sequence was developed using a saturation pulse followed by three Look-Locker inversion-recovery experiments. Each Look-Locker continuously acquired data as radial trajectory using a low flip-angle spoiled gradient-echo readout. Self-navigation was performed with a temporal resolution of100 ms for retrospectively extracting respiratory motion. The mid-diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free-breathing acquisition. Finally, data acquired at both mid-diastole and end-expiration are picked and reconstructed by a low-rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise.
Results: Numerical simulation demonstrated that changes in T1* are related to the changes in T1; however, other factors such as breathing motion could influence T1* measurements. Phantom T1* values measured using free-running T1* mapping sequence had good correlation with spin-echo T1 values and was insensitive to heart rate. In the Ex-CMR study, the measured T1* reactivity was 10% immediately after exercise and declined over time.

Conclusion: The free-running T1* mapping sequence allows free-breathing non-ECG quantification of changes in myocardial T1* with physiological exercise. Although, absolute myocardial T1* value is sensitive to various confounders

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Fahmy A, Csecs I, Arafati A, Yankama T, Al-Otaibi T, Rodriguez J, Chen Y-Y, Ngo L, Manning WJ, Kwong R, Nezafat R. An Explainable Machine Learning Approach Reveals Prognostic Significance of Right Ventricular dysfunction in Nonischemic Cardiomyopathy. JACC Cardiovasc Imaging 2022;Abstract

OBJECTIVES: We implemented an explainable machine learning (ML) model to gain insight into the association between cardiac magnetic resonance markers and adverse outcomes of cardiovascular hospitalization and all-cause death (composite endpoint) in patients with nonischemic dilated cardiomyopathy (NICM).


BACKGROUND: Risk stratification of patients with NICM remains challenging. An explainable ML model has the potential to provide insight into the contributions of different risk markers in the prediction model.


METHODS: An explainable ML model based on extreme gradient boosting (XGBoost) machines was developed using cardiac magnetic resonance and clinical parameters. The study cohorts consist of patients with NICM from 2 academic medical centers: Beth Israel Deaconess Medical Center (BIDMC) and Brigham and Women’s Hospital (BWH), with 328 and 214 patients, respectively. XGBoost was trained on 70% of patients from the BIDMC cohort and evaluated based on the other 30% as internal validation. The model was externally validated using the BWH cohort. To investigate the contribution
of different features in our risk prediction model, we used Shapley additive explanations (SHAP) analysis.


RESULTS: During a mean follow-up duration of 40 months, 34 patients from BIDMC and 33 patients from BWH experienced the composite endpoint. The area under the curve for predicting the composite endpoint was 0.71 for the internal BIDMC validation and 0.69 for the BWH cohort. SHAP analysis identified parameters associated with right ventricular
(RV) dysfunction and remodeling as primary markers of adverse outcomes. High risk thresholds were identified by SHAP analysis and thus provided thresholds for top predictive continuous clinical variables.


CONCLUSIONS: An explainable ML-based risk prediction model has the potential to identify patients with NICM at risk for cardiovascular hospitalization and all-cause death. RV ejection fraction, end-systolic and end-diastolic volumes (as indicators of RV dysfunction and remodeling) were determined to be major risk markers.

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Guo R, El-Reiwady H, Assana S, Cai X, Amyar A, Chow K, Bi X, Yankama T, Cirillo J, Pierce P, Goddu B, Ngo L, Nezafat R, Manning W. Accelerated Cardiac T1 Mapping in Four Heartbearts with Inline MyoMapNet: A Deep Learning Based T1 Estimation Approach. Journal of Cardiovascular Magnetic Resonance 2022;24Abstract

Purpose
To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4).

Method
We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI.

Results
MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively.

Conclusion
A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.

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2021
Yaman B, Shenoy C, Deng Z, Moeller S, El-Rewaidy H, Nezafat R. Self-Supervised Physics-Guided Deep Learning Reconstruction for High-Resolution 3D LGE CMR. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France 2021;
Kucukseymen S, Arafati A, Al-Otaibi T, El-Rewaidy H, Manning WJ, Nezafat R. Incremental Role of Left Atrial and Right Ventricular Strains for Predicting Cardiovascular Outcome in Heart Failure Preserved Ejection Fraction Patients: A Machine Learning Approach. Journal of the American College of Cardiology 2021;77(18):1268.Abstract

Background: The role of cardiac magnetic resonance (CMR) in predictingoutcome in heart failure preserved ejection fraction (HFpEF) remains to be fully investigated. We sought to investigate CMR predictors for HF hospitalization in HFpEF patients using a machine learning risk model (ML).
Methods: In a retrospective study, we identified203 HFpEF patients (64±12 years of age, 48% women) who were referred for CMR. Left atrial (LA) and right ventricular (RV) strains were measured using CVI42® software. An explainable ML using XGBoost® was developed based on CMR and clinical data to predict future HF hospitalization as primary outcome. SHAP (SHapley Additive exPlanations) values were calculated to interpret contributions of different risk markers to outcome.
Results: During follow-up(50±39 months), 85 patients(42%) met the primary outcome. Demographics and ventricular functions were similar between groups with and without outcome (p>0.05). However, hospitalized patients had impaired LA (19.1±8.3% vs. 9.6±6.4%, p<0.001) and RV (-19.6±4.4% vs. -14.6±4.6%, p<0.001) strains. Figure 1A shows the performance of model with and without addition of strain data, demonstrating the incremental value of strain in predicting outcome. SHAP values demonstrated that LA and RV strains are the most prognostic predictors (Figure 1B&1C).
Conclusion: An explainable ML can identify HFpEF patients with high likelihood of hospitalization. SHAP analysis identifies LA and RV strains as major predictors of adverse outcome.

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Haji-Valizadeh H, Kucukseymen S, Cai X, Rodriguez J, Goddu B, Manning WJ, Nezafat R. Artifact Reduction in Free-Running Myocardial Perfusion Imaging with Interleaved Non-Selective RF Excitation. In: Proceedings from the 24th Annual Society for Cardiovascular Magnetic Resonance (SCMR) Virtual Scientific Sessions 2021;
Fahmy AS, Pashakhanloo F, Paskavitz A, Rowin E, Maron M, Nezafat R. Improved apical and basal left ventricular slice segmentation in late gadolinium enhancement using slice attentive deep convolutional neural networks. In: Proceedings from the 24th Annual Society for Cardiovascular Magnetic Resonance (SCMR) Virtual Scientific Sessions 2021;
Fahmy AS, Paskavitz A, Pashakhanloo F, Rowin E, Maron M, Nezafat R. Accurate scar quanitification using deep learning based fusion of late gadolinium enhancement and cine sequences. In: Proceedings from the 24th Annual Society for Cardiovascular Magnetic Resonance (SCMR) Virtual Scientific Sessions 2021;
El-Rewaidy H, Guo R, Nezafat R. Accelerating the Modified Look-Locker Inversion Recovery (MOLLI) T1 Mapping Using Neural Networks. In: Proceedings from the 24th Annual Society for Cardiovascular Magnetic Resonance (SCMR) Virtual Scientific Sessions 2021;
Captur G, Manisty CH, Raman B, Marchi A, Wong TC, Ariga R, Bhuva A, Ormondroyd E, Lobascio I, Camaioni C, Loizos S, Bonsu-Ofori J, Turer A, Zaha VG, Augutsto JB, Davies RH, Taylor AJ, Nasis A, Al-Mallah MH, Valetin S, Perez de Arenaza D, Patel V, Westwood M, Petersen SE, Li C, Tang L, Nakamori S, Nezafat R, Kellman P, Kwong RY, Ho CY, Fraser AG, Watkins H, Elliot PM, Neubauer S, Lloyd G, Olivotto I, Nihoyannopoulos P, Moon JC. Maximal Wall Thickness Measurement in Hypertrophic Cardiomyopathy: Biomarker Variability and its Impact on Clinical Care. JACC: Cardiovascular Imaging 2021;14(11):2123-2134.Abstract

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.

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Kucukseymen S, Arafati A, Al-Otaibi T, El-Rewaidy H, Fahmy A, Ngo L, Nezafat R. Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction. Journal of Magnetic Resonance Imaging 2021;Abstract

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.

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Haji-Valizadeh H, Guo R, Kucukseymen S, Tuyen Y, Rodriguez J, Paskavitz A, Pierce P, Goddu B, Ngo LH, Nezafat R. Comparison of Complex k-Space Data and Magnitude-Only for Training of Deep Learning-Based Artifact Suppression for Real-Time Cine MRI. Frontiers in Physics 2021;9Abstract

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

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Guo R, Weingartner S, Siuryte P, Stoeck CT, Fuetterer M, Campbell-Washburn A, Suinesiaputra A, Jerosch-Herold M, Nezafat R. Emerging Techniques in Cardiac Magnetic Resonance Imaging. Journal of Magnetic Resonance Imaging 2021;Abstract

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. 

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Fahmy AS, Rowin EJ, Manning WJ, Maron MS, Nezafat R. Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy. Frontiers in Cardiovascular Medicine 2021;8(647857)Abstract

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 (%ScarLGE-cine = 0.82 × %Scarmanual , r = 0.84 vs. %ScarLGE = 0.47 × %Scarmanual , r = 0.81) and myocardium volume (VolumeLGE-cine = 1.03 × Volumemanual , r = 0.96 vs. VolumeLGE = 0.91 × Volumemanual , r = 0.91).

DATA CONCLUSION
CNN based LGE-Cine fusion can improve the robustness and accuracy of automated scar quantification.

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Jang J, El-Rewaidy H, Ngo L, Mancio J, Csecs I, Rodriguez J, Pierce P, Goddu B, Neisius U, Manning W, Nezafat R. Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging. Journal of Magnetic Resonance Imaging 2021;Abstract

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.
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Haji-Valizadeh H, Guo R, Kucukseymen S, Paskavitz A, Cai X, Rodriguez J, Pierce P, Goddu B, Kim D, Manning W, Nezafat R. Highly accelerated free-breathing real-time phase contrast cardiovascular MRI via complex-difference deep learning. Magnetic Resonance in Medicine 2021;86:804-819.Abstract

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.

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