MRI Assessment of Myocardial Deformation for Risk Stratification of Major Arrhythmic Events in Patients With Non-Ischemic Cardiomyopathy Eligible for Primary Prevention Implantable Cardioverter Defibrillators.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) intervention is an established prophylactic measure. Identifying high-benefit patients poses challenges. PURPOSE: To assess the prognostic value of cardiac magnetic resonance imaging (MRI) parameters including myocardial deformation for risk stratification of ICD intervention in non-ischemic cardiomyopathy (NICM) while accounting for competing mortality risk. STUDY TYPE: Retrospective and prospective. POPULATION: One hundred and fifty-nine NICM patients eligible for primary ICD (117 male, 54 ± 13 years) and 49 control subjects (38 male, 53 ± 5 years). FIELD STRENGTH/SEQUENCE: Balanced steady state free precession (bSSFP) and three-dimensional phase-sensitive inversion-recovery late gadolinium enhancement (LGE) sequences at 1.5 T or 3 T. ASSESSMENT: Patients underwent MRI before ICD implantation and were followed up. Functional parameters, left ventricular global radial, circumferential and longitudinal strain, right ventricular free wall longitudinal strain (RV FWLS) and left atrial strain were measured (Circle, cvi42). LGE presence was assessed visually. The primary endpoint was appropriate ICD intervention. Models were developed to determine outcome, with and without accounting for competing risk (non-sudden cardiac death), and compared to a baseline model including LGE and clinical features. STATISTICAL TESTS: Wilcoxon non-parametric test, Cox's proportional hazards regression, Fine-Gray competing risk model, and cumulative incidence functions. Harrell's c statistic was used for model selection. A P value <0.05 was considered statistically significant. RESULTS: Follow-up duration was 1176 ± 960 days (median: 896). Twenty-six patients (16%) met the primary endpoint. RV FWLS demonstrated a significant difference between patients with and without events (-12.5% ± 5 vs. -16.4% ± 5.5). Univariable analyses showed LGE and RV FWLS were significantly associated with outcome (LGE: hazard ratio [HR] = 3.69, 95% CI = 1.28-10.62; RV FWLS: HR = 2.04, 95% CI = 1.30-3.22). RV FWLS significantly improved the prognostic value of baseline model and remained significant in multivariable analysis, accounting for competing risk (HR = 1.73, 95% CI = 1.12-2.66). DATA CONCLUSIONS: In NICM, RV FWLS may provide additional predictive value for predicting appropriate ICD intervention. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 5.

Scanner-Independent MyoMapNet for Accelerated Cardiac MRI T1 Mapping Across Vendors and Field Strengths.

BACKGROUND: In cardiac T1 mapping, a series of T1 -weighted (T1 w) images are collected and numerically fitted to a two or three-parameter model of the signal recovery to estimate voxel-wise T1 values. To reduce the scan time, one can collect fewer T1 w images, albeit at the cost of precision or/and accuracy. Recently, the feasibility of using a neural network instead of conventional two- or three-parameter fit modeling has been demonstrated. However, prior studies used data from a single vendor and field strength; therefore, the generalizability of the models has not been established. PURPOSE: To develop and evaluate an accelerated cardiac T1 mapping approach based on MyoMapNet, a convolution neural network T1 estimator that can be used across different vendors and field strengths by incorporating the relevant scanner information as additional inputs to the model. STUDY TYPE: Retrospective, multicenter. POPULATION: A total of 1423 patients with known or suspected cardiac disease (808 male, 57 ± 16 years), from three centers, two vendors (Siemens, Philips), and two field strengths (1.5 T, 3 T). The data were randomly split into 60% training, 20% validation, and 20% testing. FIELD STRENGTH/SEQUENCE: A 1.5 T and 3 T, Modified Look-Locker inversion recovery (MOLLI) for native and postcontrast T1 . ASSESSMENT: Scanner-independent MyoMapNet (SI-MyoMapNet) was developed by altering the deep learning (DL) architecture of MyoMapNet to incorporate scanner vendor and field strength as inputs. Epicardial and endocardial contours and blood pool (by manually drawing a large region of interest in the blood pool) of the left ventricle were manually delineated by three readers, with 2, 8, and 9 years of experience, and SI-MyoMapNet myocardial and blood pool T1 values (calculated from four T1 w images) were compared with conventional MOLLI T1 values (calculated from 8 to 11 T1 w images). STATISTICAL TESTS: Equivalency test with 95% confidence interval (CI), linear regression slope, Pearson correlation coefficient (r), Bland-Altman analysis. RESULTS: The proposed SI-MyoMapNet successfully created T1 maps. Native and postcontrast T1 values measured from SI-MyoMapNet were strongly correlated with MOLLI, despite using only four T1 w images, at both field-strengths and vendors (all r > 0.86). For native T1 , SI-MyoMapNet and MOLLI were in good agreement for myocardial and blood T1 values in institution 1 (myocardium: 5 msec, 95% CI [3, 8]; blood: -10 msec, 95%CI [-16, -4]), in institution 2 (myocardium: 6 msec, 95% CI [0, 11]; blood: 0 msec, [-18, 17]), and in institution 3 (myocardium: 7 msec, 95% CI [-8, 22]; blood: 8 msec, [-14, 30]). Similar results were observed for postcontrast T1 . DATA CONCLUSION: Inclusion of field strength and vendor as additional inputs to the DL architecture allows generalizability of MyoMapNet across different vendors or field strength. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network.

Background Cardiac cine can benefit from deep learning-based image reconstruction to reduce scan time and/or increase spatial and temporal resolution. Purpose To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS). Materials and Methods The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network, trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 to September 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cine images collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS. The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected. Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-rank tests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis. Results The deep learning model was trained on 1616 patients (mean age ± SD, 56 years ± 16; 920 men) and evaluated in 181 individuals, 126 patients (mean age, 57 years ± 16; 77 men) and 55 healthy subjects (mean age, 27 years ± 10; 15 men). In breath-hold ECG-gated segmented cine and free-breathing real-time cine, the deep learning model and GRAPPA showed similar diagnostic quality scores (2.9 vs 2.9, P = .41, deep learning vs GRAPPA) and artifact score (4.4 vs 4.3, P = .55, deep learning vs GRAPPA). Deep learning acquired more sections per breath-hold than GRAPPA (3.1 vs one section, P < .001). In free-breathing real-time cine, the deep learning showed a similar diagnostic quality score (2.9 vs 2.9, P = .21, deep learning vs GRAPPA) and lower artifact score (3.9 vs 4.3, P < .001, deep learning vs GRAPPA). For both sequences, the deep learning model showed excellent agreement for LV parameters, with near-zero mean differences and narrow limits of agreement compared with GRAPPA. Conclusion Deep learning-accelerated cardiac cine showed similarly accurate quantification of cardiac function, volume, and strain to a standardized parallel imaging method. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis.

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. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Impact of deep learning architectures on accelerated cardiac T1 mapping using MyoMapNet.

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.

Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach.

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.