Tracking myocardial motion from cine DENSE images using spatiotemporal phase unwrapping and temporal fitting

DENSE-SIM: A modular pipeline for the evaluation of cine displacement encoding with stimulated echoes images with sub-voxel ground-truth strain

BACKGROUND

Myocardial strain is a valuable biomarker for diagnosing and predicting cardiac conditions, offering additional prognostic information to traditional metrics such as ejection fraction. While cardiovascular magnetic resonance (CMR) methods, particularly cine displacement encoding with stimulated echoes (DENSE), are the gold standard for strain estimation, evaluation of regional strain estimation requires precise ground truth. This study introduces DENSE-SIM, an open-source simulation pipeline for generating realistic cine DENSE images with high-resolution known ground-truth strain, enabling evaluation of accuracy and precision in strain analysis pipelines.

METHODS

This pipeline is a modular tool designed for simulating cine DENSE images and evaluating strain estimation performance. It comprises four main modules: 1) anatomy generation, for creating end-diastolic cardiac shapes; 2) motion generation, to produce myocardial deformations over time and Lagrangian strain; 3) DENSE image generation, using Bloch equation simulations with realistic noise, spiral sampling, and phase cycling; and 4) strain evaluation. To illustrate the pipeline, a synthetic dataset of 180 short-axis slices was created and analyzed using the commonly used DENSEanalysis tool. The impact of the spatial regularization parameter (k) in DENSEanalysis was evaluated against the ground-truth pixel strain, to particularly assess the resulting bias and variance characteristics.

RESULTS

Simulated strain profiles were generated with a myocardial signal-to-noise ratio (SNR) ranging from 3.9 to 17.7. For end-systolic radial strain, DENSEanalysis average signed error (ASE) in Green strain ranged from 0.04 ± 0.09 (true-calculated, mean ± std) for a typical regularization (k = 0.9), to -0.01 ± 0.21 at low regularization (k = 0.1). Circumferential strain ASE ranged from -0.00 ± 0.04 at k = 0.9 to -0.01 ± 0.10 at k = 0.1. This demonstrates that the circumferential strain closely matched the ground truth, while radial strain displayed more significant underestimations, particularly near the endocardium. A lower regularization parameter from 0.3 to 0.6 depending on the myocardial SNR would be more appropriate to estimate the radial strain, as a compromise between noise compensation and global strain accuracy.

CONCLUSION

Generating realistic cine DENSE images with high-resolution ground-truth strain and myocardial segmentation enables accurate evaluation of strain analysis tools, while reproducing key in-vivo acquisition features, and will facilitate the future development of deep-learning models for myocardial strain analysis, enhancing clinical CMR workflows.

MRI-Based Circumferential Strain in Boys with Early Duchenne Muscular Dystrophy Cardiomyopathy

Background: In boys with Duchenne muscular dystrophy (DMD), cardiomyopathy has become the primary cause of death. Although both positive late gadolinium enhancement (LGE) and reduced left ventricular ejection fraction (LVEF) are late findings in a DMD cohort, LV end-systolic circumferential strain at middle wall (Ecc) serves as a biomarker for detecting early impairment in cardiac function associated with DMD. However, Ecc derived from cine Displacement Encoding with Stimulated Echoes (DENSE) has not been quantified in boys with DMD. We aim to: (1) use cine DENSE to quantify regional Ecc in LGE negative (-) boys with DMD and healthy controls; and (2) compare Ecc with LVEF in terms of differentiating DMD boys with LGE (-) from healthy boys. Methods: 10 LGE (-) boys with DMD and 12 healthy boys were enrolled prospectively in an IRB-approved study for CMR at 3T. Navigator-gated cine DENSE was used to obtain short-axis mid-ventricular data and estimate global and regional Ecc. Group-wise differences were tested via a Wilcoxon rank-sum test. Within-group differences were tested via a Skillings-Mack test followed by pairwise Wilcoxon signed-rank tests. A binomial logistic regression model was adopted to differentiate between DMD boys with LGE (-) and healthy boys. Results: When compared to healthy boys, LGE (-) boys with DMD demonstrated significantly impaired septal Ecc [-0.13 (0.01) vs. -0.16 (0.03), p = 0.019]. In comparison to the Ecc in other segments, both groups of boys exhibited significantly reduced septal Ecc and significantly elevated lateral Ecc. Septal Ecc outperformed LVEF in distinguishing DMD boys with LGE (-) from healthy boys. Conclusions: Reduced septal Ecc may serve as an early indicator of cardiac involvement in LGE (-) DMD boys prior to reduced LVEF and a positive LGE finding.

Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not have a favorable response. A better way to identify patients expected to benefit from CRT that applies machine learning to accessible and cost-effective diagnostic tools such as the 12-lead electrocardiogram (ECG) could have a major impact on clinical care in HFrEF by helping providers personalize treatment strategies and avoid delays in initiation of other potentially beneficial treatments. This study addresses this need by demonstrating that a novel approach to ECG waveform analysis using functional principal component decomposition (FPCD) performs better than measures that require manual ECG analysis with the human eye and also at least as well as a previously validated but more expensive approach based on cardiac magnetic resonance (CMR). Analyses are based on five-fold cross validation of areas under the curve (AUCs) for CRT response and survival time after the CRT implant using Cox proportional hazards regression with stratification of groups using a Gaussian mixture model approach. Furthermore, FPCD and CMR predictors are shown to be independent, which demonstrates that the FPCD electrical findings and the CMR mechanical findings together provide a synergistic model for response and survival after CRT. In summary, this study provides a highly effective approach to prognostication after CRT in HFrEF using an accessible and inexpensive diagnostic test with a major expected impact on personalization of therapies.

Correction of phase offset errors and quantification of background noise, signal-to-noise ratio, and encoded-displacement uncertainty on DENSE MRI for kinematics of the descending thoracic and abdominal aorta

Displacement encoding with stimulated echoes (DENSE) MRI is a phase contrast technique that allows the encoding of tissue displacement into the phase of the magnetic resonance signal. Recent developments in this technique allow the imaging of relatively thin structures such as the aortic wall. Quantifying background noise associated to DENSE MRI is required to assess the uncertainty of derived displacement measurements and for the design and implementation of adequate noise-reduction techniques. Although noise and error management of cardiac DENSE MRI has been previously studied, developments for aortic applications are scarce. Herein, we evaluate the noise and uncertainty of DENSE MRI scans at three different locations along the descending aorta: the distal aortic arch (DAA), the descending thoracic aorta (DTA), and infrarenal abdominal aorta (IAA). Additionally, we analyze three datasets from in vitro validation experiments with polyvinyl alcohol phantoms. We implement and evaluate the effectiveness of an offset-error correction algorithm and noise filtering techniques on DENSE MRI for aortic motion applications. Our results show that the phase signal of pixels composing the static background was normally distributed, centered on average at 0.003 ± 0.02 rad and - 0.02 ± 0.024 rad for each phase directions, suggesting that background noise is random, isotropic, and DENSE MRI has little offset errors. However, background signal noise significantly increased with elapsed time of the cardiac cycle; and was spatially heterogeneous consistently increased towards the anterior space. Background noise showed no significant differences between the 3 aortic locations and the in vitro experiments. However, SNR depended on the displacement of the region of interest, in consequence it was found significantly larger at DAA (16.7 ± 8.5, p = 0.003) and DTA (15.4 ± 7.6, p = 0.008) than at the IAA (8.0 ± 4.1), but not significantly different than the SNR of in vitro experiments (8.0 ± 3.7), and had an overall average of 13 ± 7. The applied methods significantly reduced the offset error and effect of noise on the estimation of encoded displacements. Finally, this analysis suggests that the implemented DENSE MRI protocol is adequate to assess the motion of healthy human aortas. However, the relative effect of noise increased considerably on the analysis of an ageing or diseased aortas with impaired mobility, calling for further analyses on pathologically stiffened aortas.

Endurance Exercise Training Mitigates Diastolic Dysfunction in Diabetic Mice Independent of Phosphorylation of Ulk1 at S555

Millions of diabetic patients suffer from cardiovascular complications. One of the earliest signs of diabetic complications in the heart is diastolic dysfunction. Regular exercise is a highly effective preventive/therapeutic intervention against diastolic dysfunction in diabetes, but the underlying mechanism(s) remain poorly understood. Studies have shown that the accumulation of damaged or dysfunctional mitochondria in the myocardium is at the center of this pathology. Here, we employed a mouse model of diabetes to test the hypothesis that endurance exercise training mitigates diastolic dysfunction by promoting cardiac mitophagy (the clearance of mitochondria via autophagy) via S555 phosphorylation of Ulk1. High-fat diet (HFD) feeding and streptozotocin (STZ) injection in mice led to reduced endurance capacity, impaired diastolic function, increased myocardial oxidative stress, and compromised mitochondrial structure and function, which were all ameliorated by 6 weeks of voluntary wheel running. Using CRISPR/Cas9-mediated gene editing, we generated non-phosphorylatable Ulk1 (S555A) mutant mice and showed the requirement of p-Ulk1at S555 for exercise-induced mitophagy in the myocardium. However, diabetic Ulk1 (S555A) mice retained the benefits of exercise intervention. We conclude that endurance exercise training mitigates diabetes-induced diastolic dysfunction independent of Ulk1 phosphorylation at S555.

Noninvasive Electrical Mapping Compared with the Paced QRS Complex for Optimizing CRT Programmed Settings and Predicting Multidimensional Response

The aim was to test the hypothesis that left ventricular (LV) and right ventricular (RV) activation from body surface electrical mapping (CardioInsight 252-electrode vest, Medtronic) identifies optimal cardiac resynchronization therapy (CRT) pacing strategies and outcomes in 30 patients. The LV80, RV80, and BIV80 were defined as the times to 80% LV, RV, or biventricular electrical activation. Smaller differences in the LV80 and RV80 (|LV80-RV80|) with synchronized LV pacing predicted better LV function post-CRT (p = 0.0004) than the LV-paced QRS duration (p = 0.32). Likewise, a lower RV80 was associated with a better pre-CRT RV ejection fraction by CMR (r =  - 0.40, p = 0.04) and predicted post-CRT improvements in myocardial oxygen uptake (p = 0.01) better than the biventricular-paced QRS (p = 0.38), while a lower LV80 with BIV pacing predicted lower post-CRT B-type natriuretic peptide (BNP) (p = 0.02). RV pacing improved LV function with smaller |LV80-RV80| (p = 0.009). In conclusion, 3-D electrical mapping predicted favorable post-CRT outcomes and informed effective pacing strategies.

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE

Purpose

To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI.

Materials and Methods

In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.

Results

The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.

Conclusion

StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.

Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning

PURPOSE

To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order respiratory motion compensation for self-navigated free-breathing cine DENSE of the heart.

METHODS

The motion model includes conventional position shifts of magnetization and further describes the phase shift of the stimulated echo due to breathing. These image-domain effects correspond to linear and constant phase errors, respectively, in k-space. The model was validated using phantom experiments and Bloch-equation simulations and was used along with the simulation of respiratory motion to generate synthetic images with phase-shift artifacts to train a U-Net, DENSE-RESP-NET, to perform motion correction. DENSE-RESP-NET-corrected self-navigated free-breathing DENSE was evaluated in human subjects through comparisons with signal averaging, uncorrected self-navigated free-breathing DENSE, and breath-hold DENSE.

RESULTS

Phantom experiments and Bloch-equation simulations showed that breathing-induced constant phase errors in segmented DENSE leads to signal loss in magnitude images and phase corruption in phase images of the stimulated echo, and that these artifacts can be corrected using the known respiratory motion and the model. For self-navigated free-breathing DENSE where the respiratory motion is not known, DENSE-RESP-NET corrected the signal loss and phase-corruption artifacts and provided reliable strain measurements for systolic and diastolic parameters.

CONCLUSION

DENSE-RESP-NET is an effective method to correct for breathing-associated constant phase errors. DENSE-RESP-NET used in concert with self-navigation methods provides reliable free-breathing DENSE myocardial strain measurement.

Obesity-Induced Coronary Microvascular Disease Is Prevented by iNOS Deletion and Reversed by iNOS Inhibition

Coronary microvascular disease (CMD) caused by obesity and diabetes is major contributor to heart failure with preserved ejection fraction; however, the mechanisms underlying CMD are not well understood. Using cardiac magnetic resonance applied to mice fed a high-fat, high-sucrose diet as a model of CMD, we elucidated the role of inducible nitric oxide synthase (iNOS) and 1400W, an iNOS antagonist, in CMD. Global iNOS deletion prevented CMD along with the associated oxidative stress and diastolic and subclinical systolic dysfunction. The 1400W treatment reversed established CMD and oxidative stress and preserved systolic/diastolic function in mice fed a high-fat, high-sucrose diet. Thus, iNOS may represent a therapeutic target for CMD.

Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks

BACKGROUND

Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images.

METHODS

2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods.

RESULTS

Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies.

CONCLUSION

Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

Improved computation of Lagrangian tissue displacement and strain for cine DENSE MRI using a regularized spatiotemporal least squares method

Introduction

In displacement encoding with stimulated echoes (DENSE), tissue displacement is encoded in the signal phase such that the phase of each pixel in space and time provides an independent measurement of absolute tissue displacement. Previously for DENSE, estimation of Lagrangian displacement used two steps: first a spatial interpolation and, second, least squares fitting through time to a Fourier or polynomial model. However, there is no strong rationale for such a through-time model,

Methods

To compute the Lagrangian displacement field from DENSE phase data, a minimization problem is introduced to enforce fidelity with the acquired Eulerian displacement data while simultaneously providing model-independent regularization in space and time, enforcing only spatiotemporal smoothness. A regularized spatiotemporal least squares (RSTLS) method is used to solve the minimization problem, and RSTLS was tested using two-dimensional DENSE data from 71 healthy volunteers.

Results

The mean absolute percent error (MAPE) between the Lagrangian displacements and the corresponding Eulerian displacements was significantly lower for the RSTLS method vs. the two-step method for both x- and y-directions (0.73±0.59 vs 0.83 ±0.1, p < 0.05) and (0.75±0.66 vs 0.82 ±0.1, p < 0.05), respectively. Also, peak early diastolic strain rate (PEDSR) was higher (1.81±0.58 (s-1) vs. 1.56±0. 63 (s-1), p<0.05) and the strain rate during diastasis was lower (0.14±0.18 (s-1) vs 0.35±0.2 (s-1), p < 0.05) for the RSTLS vs. the two-step method, with the former suggesting that the two-step method was over-regularized.

Discussion

The proposed RSTLS method provides more realistic measurements of Lagrangian displacement and strain from DENSE images without imposing arbitrary motion models.

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.

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

Background

Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies.

Objective

The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term survival.

Methods

Associations of 39 baseline features (including cardiac magnetic resonance [CMR] findings and clinical parameters such as glomerular filtration rate [GFR]) with a multidimensional CRT response vector (consisting of post-CRT left ventricular end-systolic volume index [LVESVI] fractional change, post-CRT B-type natriuretic peptide, and change in peak VO₂) were evaluated. Machine learning generated response clusters, and cross-validation assessed associations of clusters with 4-year survival.

Results

Among 200 patients (median age 67.4 years, 27.0% women) with CRT and CMR, associations with more than 1 response parameter were noted for the CMR CURE-SVD dyssynchrony parameter (associated with post-CRT brain natriuretic peptide [BNP] and LVESVI fractional change) and GFR (associated with peak VO₂ and post-CRT BNP). Machine learning defined 3 response clusters: cluster 1 (n = 123, 90.2% survival [best]), cluster 2 (n = 45, 60.0% survival [intermediate]), and cluster 3 (n = 32, 34.4% survival [worst]). Adding the 6-month response cluster to baseline features improved the area under the receiver operating characteristic curve for 4-year survival from 0.78 to 0.86 (P = .02). A web-based application was developed for cluster determination in future patients.

Conclusion

Machine learning characterizes distinct CRT response clusters influenced by CMR features, kidney function, and other factors. These clusters have a strong and additive influence on long-term survival relative to baseline features.

Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease

BACKGROUND

While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (E_cc), longitudinal (E_ll) and radial (E_rr) strain, torsion, and segmental E_cc at multiple centers.

METHODS

Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global E_cc, E_ll and E_rr, torsion, and segmental E_cc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4.

RESULTS

Based on CV and ICC, segmental E_cc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good-excellent inter-scan reproducibility. Whole-slice E_cc and global E_ll provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good-excellent for all comparisons. For whole-slice E_rr, CV was in the fair-good range, and ICC was in the good-excellent range.

CONCLUSIONS

Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental E_cc, global E_ll, and torsion measurements in healthy subjects and heart disease patients.

Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease

BACKGROUND

While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers.

METHODS

Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4.

RESULTS

Based on CV and ICC, segmental Ecc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good-excellent inter-scan reproducibility. Whole-slice Ecc and global Ell provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good-excellent for all comparisons. For whole-slice Err, CV was in the fair-good range, and ICC was in the good-excellent range.

CONCLUSIONS

Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental Ecc, global Ell, and torsion measurements in healthy subjects and heart disease patients.

Fast magnetic resonance elastography with multiphase radial encoding and harmonic motion sparsity based reconstruction

Objective. To achieve fast magnetic resonance elastography (MRE) at a low frequency for better shear modulus estimation of the brain. Approach. We proposed a multiphase radial DENSE MRE (MRD-MRE) sequence and an improved GRASP algorithm utilizing the sparsity of the harmonic motion (SH-GRASP) for fast MRE at 20 Hz. For the MRD-MRE sequence, the initial position encoded by spatial modulation of magnetization (SPAMM) was decoded by an arbitrary number of readout blocks without increasing the number of phase offsets. Based on the harmonic motion, a modified total variation and temporal Fourier transform were introduced to utilize the sparsity in the temporal domain. Both phantom and brain experiments were carried out and compared with that from multiphase Cartesian DENSE-MRE (MCD-MRE), and conventional gradient echo sequence (GRE-MRE). Reconstruction performance was also compared with GRASP and compressed sensing. Main results. Results showed the scanning time of a fully sampled image with four phase offsets for MRD-MRE was only 1/5 of that from GRE-MRE. The wave patterns and estimated stiffness maps were similar to those from MCD-MRE and GRE-MRE. With SH-GRASP, the total scan time could be shortened by additional 4 folds, achieving a total acceleration factor of 20. Better metric values were also obtained using SH-GRASP for reconstruction compared with other algorithms. Significance. The MRD-MRE sequence and SH-GRASP algorithm can be used either in combination or independently to accelerate MRE, showing the potentials for imaging the brain as well as other organs.

A 3D model of the soleus reveals effects of aponeuroses morphology and material properties on complex muscle fascicle behavior

The soleus is an important plantarflexor muscle with complex fascicle and connective tissue arrangement. In this study we created an image-based finite element model representing the 3D structure of the soleus muscle and its aponeurosis connective tissue, including distinct fascicle architecture of the posterior and anterior compartments. The model was used to simulate passive and active soleus lengthening during ankle motion to predict tissue displacements and fascicle architecture changes. Both the model's initial architecture and changes incurred during passive lengthening were consistent with prior in vivo data from diffusion tensor imaging. Model predictions of active lengthening were consistent with axial plane muscle displacements that we measured in eight subjects' lower legs using cine DENSE (Displacement Encoding with Stimulated Echoes) MRI during eccentric dorsiflexion. Regional strains were variable and nonuniform in the model, but average fascicle strains were similar between the compartments for both passive (anterior: 0.18 ± 0.06, posterior: 0.19 ± 0.05) and active (anterior: 0.12 ± 0.05, posterior: 0.13 ± 0.06) lengthening and were two- to three-times greater than muscle belly strain (0.06). We used additional model simulations to investigate the effects of aponeurosis material properties on muscle deformation, by independently varying the longitudinal or transverse stiffness of the posterior or anterior aponeurosis. Results of model variations elucidate how properties of soleus aponeuroses contribute to fascicle architecture changes. Greater longitudinal stiffness of posterior compared to anterior aponeurosis promoted more uniform spatial distribution of muscle tissue deformation. Reduced transverse stiffness in both aponeuroses resulted in larger differences between passive and active soleus lengthening.

Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network

This study's purpose was to develop a direct MRI-based, deep-learning semantic segmentation approach for computing global longitudinal strain (GLS), a known metric for detecting left-ventricular (LV) cardiotoxicity in breast cancer. Displacement Encoding with Stimulated Echoes cardiac image phases acquired from 30 breast cancer patients and 30 healthy females were unwrapped via a DeepLabV3 + fully convolutional network (FCN). Myocardial strains were directly computed from the unwrapped phases with the Radial Point Interpolation Method. FCN-unwrapped phases of a phantom's rotating gel were validated against quality-guided phase-unwrapping (QGPU) and robust transport of intensity equation (RTIE) phase-unwrapping. FCN performance on unwrapping human LV data was measured with F1 and Dice scores versus QGPU ground-truth. The reliability of FCN-based strains was assessed against RTIE-based strains with Cronbach's alpha (C-α) intraclass correlation coefficient. Mean squared error (MSE) of unwrapping the phantom experiment data at 0 dB signal-to-noise ratio were 1.6, 2.7 and 6.1 with FCN, QGPU and RTIE techniques. Human data classification accuracies were F1 = 0.95 (Dice = 0.96) with FCN and F1 = 0.94 (Dice = 0.95) with RTIE. GLS results from FCN and RTIE were -16 ± 3% vs. -16 ± 3% (C-α = 0.9) for patients and -20 ± 3% vs. -20 ± 3% (C-α = 0.9) for healthy subjects. The low MSE from the phantom validation demonstrates accuracy of phase-unwrapping with the FCN and comparable human subject results versus RTIE demonstrate GLS analysis accuracy. A deep-learning methodology for phase-unwrapping in medical images and GLS computation was developed and validated in a heterogeneous cohort.

In Vitro Validation of Regional Circumferential Strain Assessment in a Phantom Aortic Model Using Cine Displacement Encoding With Stimulated Echoes MRI

BACKGROUND

A novel application of cine Displacement ENcoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE MRI) has recently been described to assess regional heterogeneities in circumferential strain around the aortic wall in vivo; however, validation is first required for successful clinical translation.

PURPOSE

To validate the quantification of regional circumferential strain around the wall of an aortic phantom using DENSE MRI.

STUDY TYPE

In vitro phantom study.

POPULATION

Three polyvinyl alcohol aortic phantoms with eight axially oriented nitinol wires embedded evenly around the walls.

FIELD STRENGTH/SEQUENCE

3 T; gradient-echo aortic DENSE MRI with spiral cine readout, gradient-echo phase-contrast MRI (PCMR) with Cartesian cine readout.

ASSESSMENT

Phantoms were connected to a pulsatile flow loop and peak DENSE-derived regional circumferential Green strains at 16 equally spaced sectors around the wall were assessed according to previously published algorithms. "True" regional circumferential strains were calculated by manually tracking displacements of the nitinol wires by two independent observers. Normalized circumferential strains (NCS) were calculated by dividing regional strains by the mean strain. Finally, DENSE-derived regional strain was corrected by multiplying regional DENSE NCS by the mean strain calculated from the diameter change on the PCMR.

STATISTICAL TESTS

One-sample t-test, Paired-sample t-test, and analysis of variance with Bonferroni correction, coefficient of variation (CoV), Bland-Altman analysis; P < 0.05 was considered statistically significant.

RESULTS

Aortic DENSE MRI significantly overestimated circumferential strain compared to the wire-tracking method (mean difference and SD 0.030 ± 0.014, CoV 0.31). However, NCS demonstrated good agreement between DENSE and wire-tracking data (mean difference 0.000 ± 0.172, CoV 0.15). After correcting the DENSE-derived regional strain, the mean difference in regional circumferential strain between DENSE and wire-tracking was significantly reduced to 0.006 ± 0.008, and the CoV was reduced to 0.18.

DATA CONCLUSION

For aortic phantoms with mild spatial heterogeneity in circumferential strain, the previously published aortic DENSE MRI technique successfully assessed the regional NCS distribution but overestimated the mean strain. This overestimation is correctable by computing a more accurate mean circumferential strain using a separate cine scan.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Suppression of artifact-generating echoes in cine DENSE using deep learning

PURPOSE

To use deep learning for suppression of the artifact-generating T1 -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.

METHODS

A U-Net was trained to suppress the artifact-generating T1 -relaxation echo using complementary phase-cycled data as the ground truth. A data-augmentation method was developed that generates synthetic DENSE images with arbitrary displacement-encoding frequencies to suppress the T1 -relaxation echo modulated for a range of frequencies. The resulting U-Net (DAS-Net) was compared with k-space zero-filling as an alternative method. Non-phase-cycled DENSE images acquired in shorter breath-holds were processed by DAS-Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain.

RESULTS

The DAS-Net method effectively suppressed the T1 -relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 ± 0.8 and structural similarity index = 0.85 ± 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS-Net method outperformed zero-filling (root Mean Square error = 5.8 ± 1.5 vs 13.5 ± 1.5, DAS-Net vs zero-filling, P < .01; and structural similarity index = 0.83 ± 0.04 vs 0.66 ± 0.03, DAS-Net vs zero-filling, P < .01). Strain data for non-phase-cycled DENSE images with DAS-Net showed close agreement with strain from phase-cycled DENSE.

CONCLUSION

The DAS-Net method provides an effective alternative approach for suppression of the artifact-generating T1 -relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.

Cardiac Magnetic Resonance Assessment of Response to Cardiac Resynchronization Therapy and Programming Strategies

OBJECTIVES

The objective was to determine the feasibility and effectiveness of cardiac magnetic resonance (CMR) cine and strain imaging before and after cardiac resynchronization therapy (CRT) for assessment of response and the optimal resynchronization pacing strategy.

BACKGROUND

CMR with cardiac implantable electronic devices can safely provide high-quality right ventricular/left ventricular (LV) ejection fraction (RVEF/LVEF) assessments and strain.

METHODS

CMR with cine imaging, displacement encoding with stimulated echoes for the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) dyssynchrony parameter, and scar assessment was performed before and after CRT. Whereas the pre-CRT scan constituted a single "imaging set" with complete volumetric, strain, and scar imaging, multiple imaging sets with complete strain and volumetric data were obtained during the post-CRT scan for biventricular pacing (BIVP), LV pacing (LVP), and asynchronous atrial pacing modes by reprogramming the device outside the scanner between imaging sets.

RESULTS

100 CMRs with a total of 162 imaging sets were performed in 50 patients (median age 70 years [IQR: 50 years-86 years]; 48% female). Reduction in LV end-diastolic volumes (P = 0.002) independent of CRT pacing were more prominent than corresponding reductions in right ventricular end-diastolic volumes (P = 0.16). A clear dependence of the optimal CRT pacing mode (BIVP vs LVP) on the PR interval (P = 0.0006) was demonstrated. The LVEF and RVEF improved more with BIVP than LVP with PR intervals ≥240 milliseconds (P = 0.025 and P = 0.002, respectively); the optimal mode (BIVP vs LVP) was variable with PR intervals <240 milliseconds. A lower pre-CRT displacement encoding with stimulated echoes CURE-SVD was associated with greater improvements in the post-CRT CURE-SVD (r = -0.69; P < 0.001), LV end-systolic volume (r = -0.58; P < 0.001), and LVEF (r = -0.52; P < 0.001).

CONCLUSIONS

CMR evaluation with assessment of multiple pacing modes during a single scan after CRT is feasible and provides useful information for patient care with respect to response and the optimal pacing strategy.

Cerebellar and Brainstem Displacement Measured with DENSE MRI in Chiari Malformation Following Posterior Fossa Decompression Surgery

Background Posterior fossa decompression (PFD) surgery is a treatment for Chiari malformation type I (CMI). The goals of surgery are to reduce cerebellar tonsillar crowding and restore posterior cerebral spinal fluid flow, but regional tissue biomechanics may also change. MRI-based displacement encoding with stimulated echoes (DENSE) can be used to assess neural tissue displacement. Purpose To assess neural tissue displacement by using DENSE MRI in participants with CMI before and after PFD surgery and examine associations between tissue displacement and symptoms. Materials and Methods In a prospective, HIPAA-compliant study of patients with CMI, midsagittal DENSE MRI was performed before and after PFD surgery between January 2017 and June 2020. Peak tissue displacement over the cardiac cycle was quantified in the cerebellum and brainstem, averaged over each structure, and compared before and after surgery. Paired t tests and nonparametric Wilcoxon signed-rank tests were used to identify surgical changes in displacement, and Spearman correlations were determined between tissue displacement and presurgery symptoms. Results Twenty-three participants were included (mean age ± standard deviation, 37 years ± 10; 19 women). Spatially averaged (mean) peak tissue displacement demonstrated reductions of 46% (79/171 µm) within the cerebellum and 22% (46/210 µm) within the brainstem after surgery (P < .001). Maximum peak displacement, calculated within a circular 30-mm² area, decreased by 64% (274/427 µm) in the cerebellum and 33% (100/300 µm) in the brainstem (P < .001). No significant associations were identified between tissue displacement and CMI symptoms (r < .74 and P > .012 for all; Bonferroni-corrected P = .0002). Conclusion Neural tissue displacement was reduced after posterior fossa decompression surgery, indicating that surgical intervention changes brain tissue biomechanics. For participants with Chiari malformation type I, no relationship was identified between presurgery tissue displacement and presurgical symptoms. © RSNA, 2021 Online supplemental material is available for this article.

3D-Encoded DENSE MRI with Zonal Excitation for Quantifying Biventricular Myocardial Strain During a Breath-Hold

PURPOSE

Right ventricular (RV) function is increasingly recognized for its prognostic value in many disease states. As with the left ventricle (LV), strain-based measurements may have better prognostic value than typical chamber volumes or ejection fraction. Complete functional characterization of the RV requires high-resolution, 3D displacement tracking methods, which have been prohibitively challenging to implement. Zonal excitation during Displacement ENcoding with Stimulated Echoes (DENSE) magnetic resonance imaging (MRI) has helped reduce scan time for 2D LV strain quantification. We hypothesized that zonal excitation could alternatively be used to reproducibly acquire higher resolution, 3D-encoded DENSE images for quantification of bi-ventricular strain within a single breath-hold.

METHODS

We modified sequence parameters for a 3D zonal excitation DENSE sequence to achieve in-plane resolution < 2 mm and acquired two sets of images in eight healthy adult male volunteers with median (IQR) age 32.5 (32.0-33.8) years. We assessed the inter-test reproducibility of this technique, and compared computed strains and torsion with previously published data.

RESULTS

Data for one subject was excluded based on image artifacts. Reproducibility for LV (CoV: 6.1-9.0%) and RV normal strains (CoV: 6.3-8.2%) and LV torsion (CoV = 7.1%) were all very good. Reproducibility of RV torsion was lower (CoV = 16.7%), but still within acceptable limits. Computed global strains and torsion were within reasonable agreement with published data, but further studies in larger cohorts are needed to confirm.

CONCLUSION

Reproducible acquisition of 3D-encoded biventricular myocardial strain data in a breath-hold is feasible using DENSE with zonal excitation.

A comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images

We addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ± 10.8% and 25.5 ± 14.8% using SP-HR, 11.9 ± 2.5% and 29.3 ± 6.5% using SinMod, and 6.4 ± 2.0% and 18.2 ± 4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best.

A kinematic model-based analysis framework for 3D Cine-DENSE-validation with an axially compressed gel phantom and application in sheep before and after antero-apical myocardial infarction

PURPOSE

Myocardial strain is increasingly used to assess left ventricular (LV) function. Incorporation of LV deformation into finite element (FE) modeling environment with subsequent strain calculation will allow analysis to reach its full potential. We describe a new kinematic model-based analysis framework (KMAF) to calculate strain from 3D cine-DENSE (displacement encoding with stimulated echoes) MRI.

METHODS

Cine-DENSE allows measurement of 3D myocardial displacement with high spatial accuracy. The KMAF framework uses cine cardiovascular magnetic resonance (CMR) to facilitate cine-DENSE segmentation, interpolates cine-DENSE displacement, and kinematically deforms an FE model to calculate strain. This framework was validated in an axially compressed gel phantom and applied in 10 healthy sheep and 5 sheep after myocardial infarction (MI).

RESULTS

Excellent Bland-Altman agreement of peak circumferential (E_cc ) and longitudinal (E_ll ) strain (mean difference = 0.021 ± 0.04 and -0.006 ± 0.03, respectively), was found between KMAF estimates and idealized FE simulation. E_rr had a mean difference of -0.014 but larger variation (±0.12). Cine-DENSE estimated end-systolic (ES) E_cc , E_ll and E_rr exhibited significant spatial variation for healthy sheep. Displacement magnitude was reduced on average by 27%, 42%, and 56% after MI in the remote, adjacent and MI regions, respectively.

CONCLUSIONS

The KMAF framework allows accurate calculation of 3D LV E_cc and E_ll from cine-DENSE.

The Potential for Quantifying Regional Distributions of Radial and Shear Strain in the Thoracic and Abdominal Aortic Wall Using Spiral Cine DENSE Magnetic Resonance Imaging

Aortic displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) was recently developed to assess heterogeneities in aortic wall circumferential strain (CS). However, previous studies neglected potential radial and shear strain (RSS) distributions. Herein, we present an improved aortic DENSE MRI postprocessing method to assess the feasibility of quantifying all components of the two-dimensional (2D) strain tensor. 32 previously acquired 2D DENSE scans from three distinct aortic locations were re-analyzed. Contrasting previous studies, displacements of the inner and outer aortic wall layers were processed separately to preserve RSS. Differences in regional strain between the new and old postprocessing methods were evaluated, along with interobserver, intraobserver, and interscan repeatability for all strain components. The new postprocessing method revealed an overall mean absolute difference in regional CS of 0.01 ± 0.01 compared to the prior method, with minimal impact on CS repeatability. Mean absolute magnitudes of regional RSS increased significantly compared to changes in CS (radial 0.04 ± 0.05, p < 0.001; shear 0.04 ± 0.04, p = 0.02). Most repeatability metrics for RSS were significantly worse than for CS. The unique distributions of RSS for each axial location associated well with local periaortic structures and mean aortic displacement. The new postprocessing method captures heterogeneous distributions of nonzero RSS which may provide new information for improving clinical diagnostics and computational modeling of heterogeneous aortic wall mechanics. However, future studies are required to improve the repeatability of RSS and assess the influence of partial volume effects.

Reproducibility of Left Ventricular CINE DENSE Strain in Pediatric Subjects with Duchenne Muscular Dystrophy

Cardiomyopathy is the leading cause of mortality in boys with Duchenne muscular dystrophy (DMD). Left ventricular (LV) peak mid-wall circumferential strain (E_cc) is a sensitive early biomarker for evaluating both the subtle and variable onset and the progression of cardiomyopathy in pediatric subjects with DMD. Cine Displacement Encoding with Stimulated Echoes (DENSE) has proven sensitive to changes in E_cc, but its reproducibility has not been reported in a pediatric cohort or a DMD cohort. The objective was to quantify the intra-observer repeatability, and intra-exam and inter-observer reproducibility of global and regional E_cc derived from cine DENSE in DMD patients (N = 10) and age-and sex-matched controls (N = 10). Global and regional E_cc measures were considered reproducible in the intra-exam, intra-observer, and inter-observer comparisons. Intra-observer repeatability was highest, followed by intra-exam reproducibility and then inter-observer reproducibility. The smallest detectable change in E_cc was 0.01 for the intra-observer comparison, which is below the previously reported yearly decrease of 0.013 ± 0.015 in E_cc in DMD patients.

Regional Brain Tissue Displacement and Strain is Elevated in Subjects with Chiari Malformation Type I Compared to Healthy Controls: A Study Using DENSE MRI

While the degree of cerebellar tonsillar descent is considered the primary radiologic marker of Chiari malformation type I (CMI), biomechanical forces acting on the brain tissue in CMI subjects are less studied and poorly understood. In this study, regional brain tissue displacement and principal strains in 43 CMI subjects and 25 controls were quantified using a magnetic resonance imaging (MRI) methodology known as displacement encoding with stimulated echoes (DENSE). Measurements from MRI were obtained for seven different brain regions-the brainstem, cerebellum, cingulate gyrus, corpus callosum, frontal lobe, occipital lobe, and parietal lobe. Mean displacements in the cerebellum and brainstem were found to be 106 and 64% higher, respectively, for CMI subjects than controls (p < .001). Mean compression and extension strains in the cerebellum were 52 and 50% higher, respectively, in CMI subjects (p < .001). Brainstem mean extension strain was 41% higher in CMI subjects (p < .001), but no significant difference in compression strain was observed. The other brain structures revealed no significant differences between CMI and controls. These findings demonstrate that brain tissue displacement and strain in the cerebellum and brainstem might represent two new biomarkers to distinguish between CMI subjects and controls.

Strain Tensor Imaging: Cardiac-induced brain tissue deformation in humans quantified with high-field MRI

The cardiac cycle induces blood volume pulsations in the cerebral microvasculature that cause subtle deformation of the surrounding tissue. These tissue deformations are highly relevant as a potential source of information on the brain's microvasculature as well as of tissue condition. Besides, cyclic brain tissue deformations may be a driving force in clearance of brain waste products. We have developed a high-field magnetic resonance imaging (MRI) technique to capture these tissue deformations with full brain coverage and sufficient signal-to-noise to derive the cardiac-induced strain tensor on a voxel by voxel basis, that could not be assessed non-invasively before. We acquired the strain tensor with 3 mm isotropic resolution in 9 subjects with repeated measurements for 8 subjects. The strain tensor yielded both positive and negative eigenvalues (principle strains), reflecting the Poison effect in tissue. The principle strain associated with expansion followed the known funnel shaped brain motion pattern pointing towards the foramen magnum. Furthermore, we evaluate two scalar quantities from the strain tensor: the volumetric strain and octahedral shear strain. These quantities showed consistent patterns between subjects, and yielded repeatable results: the peak systolic volumetric strain (relative to end-diastolic strain) was 4.19⋅10^-4 ± 0.78⋅10^-4 and 3.98⋅10^-4 ± 0.44⋅10^-4 (mean ± standard deviation for first and second measurement, respectively), and the peak octahedral shear strain was 2.16⋅10^-3 ± 0.31⋅10^-3 and 2.31⋅10^-3 ± 0.38⋅10^-3, for the first and second measurement, respectively. The volumetric strain was typically highest in the cortex and lowest in the periventricular white matter, while anisotropy was highest in the subcortical white matter and basal ganglia. This technique thus reveals new, regional information on the brain's cardiac-induced deformation characteristics, and has the potential to advance our understanding of the role of microvascular pulsations in health and disease.

HARP-I: A Harmonic Phase Interpolation Method for the Estimation of Motion From Tagged MR Images

We proposed a novel method called HARP-I, which enhances the estimation of motion from tagged Magnetic Resonance Imaging (MRI). The harmonic phase of the images is unwrapped and treated as noisy measurements of reference coordinates on a deformed domain, obtaining motion with high accuracy using Radial Basis Functions interpolations. Results were compared against Shortest Path HARP Refinement (SP-HR) and Sine-wave Modeling (SinMod), two harmonic image-based techniques for motion estimation from tagged images. HARP-I showed a favorable similarity with both methods under noise-free conditions, whereas a more robust performance was found in the presence of noise. Cardiac strain was better estimated using HARP-I at almost any motion level, giving strain maps with less artifacts. Additionally, HARP-I showed better temporal consistency as a new method was developed to fix phase jumps between frames. In conclusion, HARP-I showed to be a robust method for the estimation of motion and strain under ideal and non-ideal conditions.

Validation of a deep-learning semantic segmentation approach to fully automate MRI-based left-ventricular deformation analysis in cardiotoxicity

OBJECTIVE

Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to chemotherapy for breast cancer. This study investigated an automated and supervised deep convolutional neural network (DCNN) model for LV chamber quantification before strain analysis in DENSE images.

METHODS

The DeepLabV3 +DCNN with three versions of ResNet-50 backbone was designed to conduct chamber quantification on 42 female breast cancer data sets. The convolutional layers in the three ResNet-50 backbones were varied as non-atrous, atrous and modified, atrous with accuracy improvements like using Laplacian of Gaussian filters. Parameters such as LV end-diastolic diameter (LVEDD) and ejection fraction (LVEF) were quantified, and myocardial strains analyzed with the Radial Point Interpolation Method (RPIM). Myocardial classification was validated with the performance metrics of accuracy, Dice, average perpendicular distance (APD) and others. Repeated measures ANOVA and intraclass correlation (ICC) with Cronbach's α (C-Alpha) tests were conducted between the three DCNNs and a vendor tool on chamber quantification and myocardial strain analysis.

RESULTS

Validation results in the same test-set for myocardial classification were accuracy = 97%, Dice = 0.92, APD = 1.2 mm with the modified ResNet-50, and accuracy = 95%, Dice = 0.90, APD = 1.7 mm with the atrous ResNet-50. The ICC results between the modified ResNet-50, atrous ResNet-50 and vendor-tool were C-Alpha = 0.97 for LVEF (55±7%, 54±7%, 54±7%, p = 0.6), and C-Alpha = 0.87 for LVEDD (4.6 ± 0.3 cm, 4.6 ± 0.3 cm, 4.6 ± 0.4 cm, p = 0.7).

CONCLUSION

Similar performance metrics and equivalent parameters obtained from comparisons between the atrous networks and vendor tool show that segmentation with the modified, atrous DCNN is applicable for automated LV chamber quantification and subsequent strain analysis in cardiotoxicity.

ADVANCES IN KNOWLEDGE

A novel deep-learning technique for segmenting DENSE images was developed and validated for LV chamber quantification and strain analysis in cardiotoxicity detection.

Finite-element based optimization of left ventricular passive stiffness in normal volunteers and patients after myocardial infarction: Utility of an inverse deformation gradient calculation of regional diastolic strain

INTRODUCTION

Left ventricular (LV) diastolic dysfunction (DD) is common after myocardial infarction (MI). Whereas current clinical assessment of DD relies on indirect markers including LV filling, finite element (FE) -based computational modeling directly measures regional diastolic stiffness. We hypothesized that an inverse deformation gradient (DG) method calculation of diastolic strain (IDGDS) allows the FE model-based calculation of regional diastolic stiffness (material parameters; MP) in post-MI patients with DD.

METHODS

Cardiac magnetic resonance (CMR) with tags (CSPAMM) and late gadolinium enhancement (LGE) was performed in 10 patients with post-MI DD and 10 healthy volunteers. The 3-dimensional (3D) LV DG from end-diastole (ED) to early diastolic filling (EDF; DG_ED→EDF) was calculated from CSPAMM. Diastolic strain was calculated from DG_EDF→ED by inverting the DG_ED→EDF. FE models were created with MI and non-MI (remote; RM) regions determined by LGE. Guccione MPs C, and exponential fiber, b_f, and transverse, b_t , terms were optimized with IDGDS strain.

RESULTS

3D circumferential and longitudinal diastolic strain (E_cc;E_ll) calculated using IDGDS in CSPAMM obtained in volunteers and MI patients were [Formula: see text]  = 0.27 ± 0.01, [Formula: see text]  = 0.24 ± 0.03 and [Formula: see text]  = 0.21 ± 0.02, and [Formula: see text]  = 0.15 ± 0.02, respectively. MPs in the volunteer group were C_H = 0.013 [0.001, 0.235] kPa, [Formula: see text]  = 20.280 ± 4.994, and [Formula: see text]  = 7.460 ± 2.171 and C_RM = 0.0105 [0.010, 0.011] kPa, [Formula: see text]  = 50.786 ± 13.511 (p = 0.0846), and [Formula: see text]  = 17.355 ± 2.743 (p = 0.0208) in the remote myocardium of post-MI patients.

CONCLUSION

Diastolic strain, calculated from CSPAMM with IDGDS, enables calculation of FE model-based regional diastolic material parameters. Transverse stiffness of the remote myocardium, , may be a valuable new metric for determination of DD in patients after MI.

Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping

BACKGROUND

Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis.

METHODS

Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain.

RESULTS

LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p < 0.05), respectively. Bland-Altman analyses showed biases of 0.00 ± 0.03 and limits of agreement of - 0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods.

CONCLUSIONS

Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.

Myofiber strain in healthy humans using DENSE and cDTI

PURPOSE

Myofiber strain, E_ff , is a mechanistically relevant metric of cardiac cell shortening and is expected to be spatially uniform in healthy populations, making it a prime candidate for the evaluation of local cardiomyocyte contractility. In this study, a new, efficient pipeline was proposed to combine microstructural cDTI and functional DENSE data in order to estimate E_ff in vivo.

METHODS

Thirty healthy volunteers were scanned with three long-axis (LA) and three short-axis (SA) DENSE slices using 2D displacement encoding and one SA slice of cDTI. The total acquisition time was 11 minutes ± 3 minutes across volunteers. The pipeline first generates 3D SA displacements from all DENSE slices which are then combined with cDTI data to generate a cine of myofiber orientations and compute E_ff . The precision of the post-processing pipeline was assessed using a computational phantom study. Transmural myofiber strain was compared to circumferential strain, E_cc , in healthy volunteers using a Wilcoxon sign rank test.

RESULTS

In vivo, computed E_ff was found uniform transmurally compared to E_cc (-0.14[-0.15, -0.12] vs -0.18 [-0.20, -0.16], P < .001, -0.14 [-0.16, -0.12] vs -0.16 [-0.17, -0.13], P < .001 and -0.14 [-0.16, -0.12] vs E_{cc_C} = -0.14 [-0.15, -0.11], P = .002, E_{ff_C} vs E_{cc_C} in the endo, mid, and epi layers, respectively).

CONCLUSION

We demonstrate that it is possible to measure in vivo myofiber strain in a healthy human population in 10 minutes per subject. Myofiber strain was observed to be spatially uniform in healthy volunteers making it a potential biomarker for the evaluation of local cardiomyocyte contractility in assessing cardiovascular dysfunction.

A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity

Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to breast cancer chemotherapy. This study investigated an automated LV chamber quantification tool via segmentation with a supervised deep convolutional neural network (DCNN) before strain analysis with DENSE images. Segmentation for chamber quantification analysis was conducted with a custom DeepLabV3+ DCNN with ResNet-50 backbone on 42 female breast cancer datasets (22 training-sets, eight validation-sets and 12 independent test-sets). Parameters such as LV end-diastolic diameter (LVEDD) and ejection fraction (LVEF) were quantified, and myocardial strains analyzed with the Radial Point Interpolation Method (RPIM). Myocardial classification was validated against ground-truth with sensitivity-specificity analysis, the metrics of Dice, average perpendicular distance (APD) and Hausdorff-distance. Following segmentation, validation was conducted with the Cronbach's Alpha (C-Alpha) intraclass correlation coefficient between LV chamber quantification results with DENSE and Steady State Free Precession (SSFP) acquisitions and a vendor tool-based method to segment the DENSE data, and similarly for myocardial strain analysis in the chambers. The results of myocardial classification from segmentation of the DENSE data were accuracy = 97%, Dice = 0.89 and APD = 2.4 mm in the test-set. The C-Alpha correlations from comparing chamber quantification results between the segmented DENSE and SSFP data and vendor tool-based method were 0.97 for LVEF (56 ± 7% vs 55 ± 7% vs 55 ± 6%, p = 0.6) and 0.77 for LVEDD (4.6 ± 0.4 cm vs 4.5 ± 0.3 cm vs 4.5 ± 0.3 cm, p = 0.8). The validation metrics against ground-truth and equivalent parameters obtained from the SSFP segmentation and vendor tool-based comparisons show that the DCNN approach is applicable for automated LV chamber quantification and subsequent strain analysis in cardiotoxicity.

Estimating cardiomyofiber strain in vivo by solving a computational model

Since heart contraction results from the electrically activated contraction of millions of cardiomyocytes, a measure of cardiomyocyte shortening mechanistically underlies cardiac contraction. In this work we aim to measure preferential aggregate cardiomyocyte ("myofiber") strains based on Magnetic Resonance Imaging (MRI) data acquired to measure both voxel-wise displacements through systole and myofiber orientation. In order to reduce the effect of experimental noise on the computed myofiber strains, we recast the strains calculation as the solution of a boundary value problem (BVP). This approach does not require a calibrated material model, and consequently is independent of specific myocardial material properties. The solution to this auxiliary BVP is the displacement field corresponding to assigned values of myofiber strains. The actual myofiber strains are then determined by minimizing the difference between computed and measured displacements. The approach is validated using an analytical phantom, for which the ground-truth solution is known. The method is applied to compute myofiber strains using in vivo displacement and myofiber MRI data acquired in a mid-ventricular left ventricle section in N=8 swine subjects. The proposed method shows a more physiological distribution of myofiber strains compared to standard approaches that directly differentiate the displacement field.

Clinical validation of three cardiovascular magnetic resonance techniques to measure strain and torsion in patients with suspected coronary artery disease

BACKGROUND

Several cardiovascular magnetic resonance (CMR) techniques can measure myocardial strain and torsion with high accuracy. The purpose of this study was to compare displacement encoding with stimulated echoes (DENSE), tagging and feature tracking (FT) for measuring circumferential and radial myocardial strain and myocardial torsion in order to assess myocardial function and infarct scar burden both at a global and at a segmental level.

METHOD

116 patients with a high likelihood of coronary artery disease (European SCORE > 15%) underwent CMR examination including cine images, tagging, DENSE and late gadolinium enhancement (LGE) in the short axis direction. In total, 97 patients had signs of myocardial disease and 19 had no abnormalities in terms of left ventricular (LV) wall mass index, LV ejection fraction, wall motion, LGE or a history of myocardial infarction. Thirty-four patients had myocardial infarct scar with a transmural LGE extent (transmurality) that exceeded 50% of the wall thickness in at least one segment. Global circumferential strain (GCS) and global radial strain (GRS) was analyzed using FT of cine loops, deformation of tag lines or DENSE displacement.

RESULTS

DENSE and tagging both showed high sensitivity (82% and 71%) at a specificity of 80% for the detection of segments with > 50% LGE transmurality, and receiver operating characteristics (ROC) analysis showed significantly higher area under the curve-values (AUC) for DENSE (0.87) than for tagging (0.83, p < 0.001) and FT (0.66, p = 0.003). GCS correlated with global LGE when determined with DENSE (r = 0.41), tagging (r = 0.37) and FT (r = 0.15). GRS had a low but significant negative correlation with LGE; DENSE r = - 0.10, FT r = - 0.07 and tagging r = - 0.16. Torsion from DENSE and tagging had a weak correlation (- 0.20 and - 0.22 respectively) with global LGE.

CONCLUSION

Circumferential strain from DENSE detected segments with > 50% scar with a higher AUC than strain determined from tagging and FT at a segmental level. GCS and torsion computed from DENSE and tagging showed similar correlation with global scar size, while when computed from FT, the correlation was lower.

Comprehensive enhanced methodology of an MRI-based automated left-ventricular chamber quantification algorithm and validation in chemotherapy-related cardiotoxicity

Purpose: To comprehensively outline the methodology of a fully automated, MRI motion-guided, left-ventricular (LV) chamber quantification algorithm that enhances a similar, existing semi-automated approach. Additionally, to validate the motion-guided technique in comparison to chamber quantification with a vendor tool in post-chemotherapy breast cancer patients susceptible to cardiotoxicity. Approach: LV deformation data were acquired with the displacement encoding with stimulated echoes (DENSE) sequence on N = 21 post-chemotherapy female patients and N = 21 age-matched healthy females. The new chamber quantification algorithm consists of detecting LV boundary motion via a combination of image quantization and DENSE phase-encoded displacements. LV contractility was analyzed via chamber quantification and computations of 3D strains and torsion. For validation, estimates of chamber quantification with the motion-guided algorithm on DENSE and steady-state free precession (SSFP) acquisitions, and similar estimates with an existing vendor tool on DENSE acquisitions were compared via repeated measures analysis. Patient results were compared to healthy subjects for observing abnormalities. Results: Repeated measures analysis showed similar LV ejection fractions (LVEF), 59 % ± 6 % , 58 % ± 6 % , and 58 % ± 6 % , p = 0.2 , by applying the motion-guided algorithm on DENSE and SSFP and vendor tool on DENSE acquisitions, respectively. Differences found between patients and healthy subjects included enlarged basal diameters ( 5.0 ± 0.5    cm versus 4.4 ± 0.5    cm , p < 0.01 ), torsions ( p < 0.001 ), and longitudinal strains ( p < 0.001 ), but not LVEF ( p = 0.1 ). Conclusions: Measurement similarities between new and existing tools, and between DENSE and SSFP validated the motion-guided algorithm and differences found between subpopulations demonstrate the ability to detect contractile abnormalities.

Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain

The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool to identify responders would prevent unnecessary surgery, while targeting non-responders for early intervention. We used regional left ventricular (LV) contractile injury patterns in ML models to identify IDCM HF non-responders. MRI-based multiparametric strain analysis was performed in 178 test subjects (140 normal subjects and 38 IDCM patients), calculating longitudinal, circumferential, and radial strain over 18 LV sub-regions for inclusion in ML analyses. Patients were identified as responders based upon symptomatic and contractile improvement on medical therapy. We tested the predictive accuracy of support vector machines (SVM), logistic regression (LR), random forest (RF), and deep neural networks (DNN). The DNN model outperformed other models, predicting response to medical therapy with an area under the receiver operating characteristic curve (AUC) of 0.94. The top features were longitudinal strain in (1) basal: anterior, posterolateral and (2) mid: posterior, anterolateral, and anteroseptal sub-regions. Regional contractile injury patterns predict response to medical therapy in IDCM HF patients, and have potential application in ML-based HF patient care.

Nitroxide-enhanced MRI of cardiovascular oxidative stress

BACKGROUND

In vivo imaging of oxidative stress can facilitate the understanding and treatment of cardiovascular diseases. We evaluated nitroxide-enhanced MRI with 3-carbamoyl-proxyl (3CP) for the detection of myocardial oxidative stress.

METHODS

Three mouse models of cardiac oxidative stress were imaged, namely angiotensin II (Ang II) infusion, myocardial infarction (MI), and high-fat high-sucrose (HFHS) diet-induced obesity (DIO). For the Ang II model, mice underwent MRI at baseline and after 7 days of Ang II (n = 8) or saline infusion (n = 8). For the MI model, mice underwent MRI at baseline (n = 10) and at 1 (n = 8), 4 (n = 9), and 21 (n = 8) days after MI. For the HFHS-DIO model, mice underwent MRI at baseline (n = 20) and 18 weeks (n = 13) after diet initiation. The 3CP reduction rate, K_red , computed using a tracer kinetic model, was used as a metric of oxidative stress. Dihydroethidium (DHE) staining of tissue sections was performed on Day 1 after MI.

RESULTS

For the Ang II model, K_red was higher after 7 days of Ang II versus other groups (p < 0.05). For the MI model, K_red , in the infarct region was significantly elevated on Days 1 and 4 after MI (p < 0.05), whereas K_red in the noninfarcted region did not change after MI. DHE confirmed elevated oxidative stress in the infarct zone on Day 1 after MI. After 18 weeks of HFHS diet, K_red was higher in mice after diet versus baseline (p < 0.05).

CONCLUSIONS

Nitroxide-enhanced MRI noninvasively quantifies tissue oxidative stress as one component of a multiparametric preclinical MRI examination. These methods may facilitate investigations of oxidative stress in cardiovascular disease and related therapies.

Cardiac MRI Myocardial Functional and Tissue Characterization Detects Early Cardiac Dysfunction in a Mouse Model of Chemotherapy-Induced Cardiotoxicity

BACKGROUND

Doxorubicin and doxorubicin-trastuzumab combination chemotherapy have been associated with cardiotoxicity that eventually leads to heart failure and may limit dose-effective cancer treatment. Current diagnostic strategies rely on decreased ejection fraction (EF) to diagnose cardiotoxicity.

PURPOSE

The aim of this study is to explore the potential of cardiac MR (CMR) imaging to identify imaging biomarkers in a mouse model of chemotherapy-induced cardiotoxicity.

METHODS

A cumulative dose of 25 mg/kg doxorubicin was administered over three weeks using subcutaneous pellets (n = 9, Dox). Another group (n = 9) received same dose of Dox and a total of 10 mg/kg trastuzumab (DT). Mice were imaged at baseline, 5/6 weeks and 10 weeks post-treatment on a 7T MRI system. The protocol included short-axis cine MRI covering the left ventricle (LV) and mid-ventricular short-axis tissue phase mapping (TPM), pre- and post-contrast T1 mapping, T2 mapping and Displacement Encoding with Stimulated Echoes (DENSE) strain encoded MRI. EF, peak myocardial velocities, native T1, T2, extracellular volume (ECV), and myocardial strain were quantified. N = 7 mice were sacrificed for histopathologic assessment of apoptosis at 5/6 weeks.

RESULTS

Global peak systolic longitudinal velocity was reduced at 5/6 weeks in Dox (0.6 ± 0.3 vs 0.9 ± 0.3, p = 0.02). In the Dox group, native T1 was reduced at 5/6 weeks (1.3 ± 0.2 ms vs 1.6 ± 0.2 ms, p = 0.02), and relatively normalized at week 10 (1.4 ± 0.1 ms vs 1.6 ± 0.2 ms, p > 0.99). There was no change in EF and other MRI parameters and histopathologic results demonstrated minimal apoptosis in all mice (~1-2 apoptotic cell/high power field), suggesting early-stage cardiotoxicity.

CONCLUSIONS

In a mouse model of chemotherapy-induced cardiotoxicity using doxorubicin and trastuzumab, advanced CMR shows promise in identifying treatment-related decrease in myocardial velocity and native T1 prior to the onset of cardiomyocyte apoptosis and reduction of EF.

Accuracy of cardiac-induced brain motion measurement using displacement-encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI): A phantom study

PURPOSE

The goal of this study was to determine the accuracy of displacement-encoding with stimulated echoes (DENSE) MRI in a tissue motion phantom with displacements representative of those observed in human brain tissue.

METHODS

The phantom was comprised of a plastic shaft rotated at a constant speed. The rotational motion was converted to a vertical displacement through a camshaft. The phantom generated repeatable cyclical displacement waveforms with a peak displacement ranging from 92 µm to 1.04 mm at 1-Hz frequency. The surface displacement of the tissue was obtained using a laser Doppler vibrometer (LDV) before and after the DENSE MRI scans to check for repeatability. The accuracy of DENSE MRI displacement was assessed by comparing the laser Doppler vibrometer and DENSE MRI waveforms.

RESULTS

Laser Doppler vibrometer measurements of the tissue motion demonstrated excellent cycle-to-cycle repeatability with a maximum root mean square error of 9 µm between the ensemble-averaged displacement waveform and the individual waveforms over 180 cycles. The maximum difference between DENSE MRI and the laser Doppler vibrometer waveforms ranged from 15 to 50 µm. Additionally, the peak-to-peak difference between the 2 waveforms ranged from 1 to 18 µm.

CONCLUSION

Using a tissue phantom undergoing cyclical motion, we demonstrated the percent accuracy of DENSE MRI to measure displacement similar to that observed for in vivo cardiac-induced brain tissue.

Displacement Encoding With Stimulated Echoes Enables the Identification of Infarct Transmurality Early Postmyocardial Infarction

BACKGROUND

Segmental extent of infarction assessed by late gadolinium enhancement (LGE) imaging early post-ST-segment elevation myocardial infarction (STEMI) has utility in predicting left ventricular functional recovery.

HYPOTHESIS

We hypothesized that segmental circumferential strain with displacement encoding with stimulated echoes (DENSE) would be a stronger predictor of infarct transmurality than feature-tracking strain, and noninferior to extracellular volume fraction (ECV).

STUDY TYPE

Prospective.

POPULATION

Fifty participants (mean ± SD, 59 ± 9 years, 40 [80%] male) underwent cardiac MRI on day 1 post-STEMI.

FIELD-STRENGTH/SEQUENCES

1.5T/cine, DENSE, T₁ mapping, ECV, LGE.

ASSESSMENT

Two observers assessed segmental percentage LGE extent, presence of microvascular obstruction (MVO), circumferential and radial strain with DENSE and feature-tracking, T₁ relaxation times, and ECV.

STATISTICAL TESTS

Normality was tested using the Shapiro-Wilk test. Skewed distributions were analyzed utilizing Mann-Whitney or Kruskal-Wallis tests and normal distributed data using independent t-tests. Diagnostic cutoff values were identified using the Youden index. The difference in area under the curve was compared using the z-statistic.

RESULTS

Segmental circumferential strain with DENSE was associated with the extent of infarction ≥50% (AUC [95% CI], cutoff value = 0.9 [0.8, 0.9], -10%) similar to ECV (AUC = 0.8 [0.8, 0.9], 37%) (P = 0.117) and superior to feature-tracking circumferential strain (AUC = 0.7[0.7, 0.8], -19%) (P < 0.05). For the detection of segmental infarction ≥75%, circumferential strain with DENSE (AUC = 0.9 [0.8, 0.9], -10%) was noninferior to ECV (AUC = 0.8 [0.7, 0.9], 42%) (P = 0.132) and superior to feature-tracking (AUC = 0.7 [0.7, 0.8], -13%) (P < 0.05). For MVO detection, circumferential strain with DENSE (AUC = 0.8 [0.8, 0.9], -12%) was superior to ECV (AUC = 0.8 [0.7, 0.8] 34%) (P < 0.05) and feature-tracking (AUC = 0.7 [0.6, 0.7] -21%) (P < 0.05).

DATA CONCLUSION

Circumferential strain with DENSE is a functional measure of infarct severity and may remove the need for gadolinium contrast agents in some circumstances.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 5 J. MAGN. RESON. IMAGING 2020;52:1722-1731.

Diffusion Tensor Cardiovascular Magnetic Resonance in Cardiac Amyloidosis

Background Cardiac amyloidosis (CA) is a disease of interstitial myocardial infiltration, usually by light chains or transthyretin. We used diffusion tensor cardiovascular magnetic resonance (DT-CMR) to noninvasively assess the effects of amyloid infiltration on the cardiac microstructure. Methods DT-CMR was performed at diastole and systole in 20 CA, 11 hypertrophic cardiomyopathy, and 10 control subjects with calculation of mean diffusivity, fractional anisotropy, and sheetlet orientation (secondary eigenvector angle). Results Mean diffusivity was elevated and fractional anisotropy reduced in CA compared with both controls and hypertrophic cardiomyopathy (P<0.001). In CA, mean diffusivity was correlated with extracellular volume (r=0.68, P=0.004), and fractional anisotropy was inversely correlated with circumferential strain (r=-0.65, P=0.02). In CA, diastolic secondary eigenvector angle was elevated, and secondary eigenvector angle mobility was reduced compared with controls (both P<0.001). Diastolic secondary eigenvector angle was correlated with amyloid burden measured by extracellular volume in transthyretin, but not light chain amyloidosis. Conclusions DT-CMR can characterize the microstructural effects of amyloid infiltration and is a contrast-free method to identify the location and extent of the expanded disorganized myocardium. The diffusion biomarkers mean diffusivity and fractional anisotropy effectively discriminate CA from hypertrophic cardiomyopathy. DT-CMR demonstrated that failure of sheetlet relaxation in diastole correlated with extracellular volume in transthyretin, but not light chain amyloidosis. This indicates that different mechanisms may be responsible for impaired contractility in CA, with an amyloid burden effect in transthyretin, but an idiosyncratic effect in light chain amyloidosis. Consequently, DT-CMR offers a contrast-free tool to identify novel pathophysiology, improve diagnostics, and monitor disease through noninvasive microstructural assessment.

Force-dependent recruitment from myosin OFF-state increases end-systolic pressure-volume relationship in left ventricle

Finite element (FE) modeling is becoming increasingly prevalent in the world of cardiac mechanics; however, many existing FE models are phenomenological and thus do not capture cellular-level mechanics. This work implements a cellular-level contraction scheme into an existing nonlinear FE code to model ventricular contraction. Specifically, this contraction model incorporates three myosin states: OFF-, ON-, and an attached force-generating state. It has been speculated that force-dependent transitions from the OFF- to ON-state may contribute to length-dependent activation at the cellular level. The current work investigates the contribution of force-dependent recruitment out of the OFF-state to ventricular-level function, specifically the Frank-Starling relationship, as seen through the end-systolic pressure-volume relationship (ESPVR). Five FE models were constructed using geometries of rat left ventricles obtained via cardiac magnetic resonance imaging. FE simulations were conducted to optimize parameters for the cellular contraction model such that the differences between FE predicted ventricular pressures for the models and experimentally measured pressures were minimized. The models were further validated by comparing FE predicted end-systolic strain to experimentally measured strain. Simulations mimicking vena cava occlusion generated descending pressure volume loops from which ESPVRs were calculated. In simulations with the inclusion of the OFF-state, using a force-dependent transition to the ON-state, the ESPVR calculated was steeper than in simulations excluding the OFF-state. Furthermore, the ESPVR was also steeper when compared to models that included the OFF-state without a force-dependent transition. This suggests that the force-dependent recruitment of thick filament heads from the OFF-state at the cellular level contributes to the Frank-Starling relationship observed at the organ level.

Quantification of regional myocardial mean intracellular water lifetime: A nonhuman primate study in myocardial stress

Heart failure with preserved ejection fraction (HFpEF) is typically associated with early metabolic remodeling. Noninvasive imaging biomarkers that reflect these changes will be crucial in determining responses to early drug interventions in these patients. Mean intracellular water lifetime (τ_i ) has been shown to be partially inversely related to Na, K-ATPase transporter activity and may thus provide insight into the metabolic status in HFpEF patients. Here, we aim to perform regional quantification of τ_i using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the nonhuman primate (NHP) heart and evaluate its region-specific variations under conditions of myocardial stress in the context of perturbed myocardial function. Cardiac stress was induced in seven naïve cynomolgus macaques using a dobutamine stepwise infusion protocol. All animals underwent 3 T cardiac dual-bolus DCE and tagging MRI experiments. The shutter-speed model was employed to quantify regional τ_i from the DCE-MR images. Additionally, τ_i values were correlated with myocardial strains. During cardiac stress, there was a significant decrease in global τ_i (192.9 ± 76.3 ms vs 321.6 ± 70 ms at rest, P < 0.05) in the left ventricle, together with an increase in global peak circumferential strain (-15.4% ± 2.7% vs -10.1% ± 2.9% at rest, P < 0.05). Specifically, slice-level analysis further revealed that a greater significant decrease in mean τ_i was observed in the apical region (Δτ_I = 182.4 ms) compared with the basal (Δτ_i = 113.2 ms) and midventricular regions (Δτ_i = 108.4 ms). Regional analysis revealed that there was a greater significant decrease in mean τ_i in the anterior (Δτ_i = 243.9 ms) and antero-lateral (Δτ_i = 177.2 ms) regions. In the inferior and infero-septal regions, although a decrease in τ_i was observed, it was not significant. Whole heart regional quantification of τ_i is feasible using DCE-MRI. τ_i is sensitive to regional changes in metabolic state during cardiac stress, and its value correlates with strain.

Estimating Aggregate Cardiomyocyte Strain Using In Vivo Diffusion and Displacement Encoded MRI

Changes in left ventricular (LV) aggregate cardiomyocyte orientation and deformation underlie cardiac function and dysfunction. As such, in vivo aggregate cardiomyocyte "myofiber" strain ( [Formula: see text]) has mechanistic significance, but currently there exists no established technique to measure in vivo [Formula: see text]. The objective of this work is to describe and validate a pipeline to compute in vivo [Formula: see text] from magnetic resonance imaging (MRI) data. Our pipeline integrates LV motion from multi-slice Displacement ENcoding with Stimulated Echoes (DENSE) MRI with in vivo LV microstructure from cardiac Diffusion Tensor Imaging (cDTI) data. The proposed pipeline is validated using an analytical deforming heart-like phantom. The phantom is used to evaluate 3D cardiac strains computed from a widely available, open-source DENSE Image Analysis Tool. Phantom evaluation showed that a DENSE MRI signal-to-noise ratio (SNR) ≥20 is required to compute [Formula: see text] with near-zero median strain bias and within a strain tolerance of 0.06. Circumferential and longitudinal strains are also accurately measured under the same SNR requirements, however, radial strain exhibits a median epicardial bias of -0.10 even in noise-free DENSE data. The validated framework is applied to experimental DENSE MRI and cDTI data acquired in eight ( N=8 ) healthy swine. The experimental study demonstrated that [Formula: see text] has decreased transmural variability compared to radial and circumferential strains. The spatial uniformity and mechanistic significance of in vivo [Formula: see text] make it a compelling candidate for characterization and early detection of cardiac dysfunction.

CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT

OBJECTIVES

This study sought to determine if combining the Seattle Heart Failure Model (SHFM-D) and cardiac magnetic resonance (CMR) provides complementary prognostic data for patients with cardiac resynchronization therapy (CRT) defibrillators.

BACKGROUND

The SHFM-D is among the most widely used risk stratification models for overall survival in patients with heart failure and implantable cardioverter-defibrillators (ICDs), and CMR provides highly detailed information regarding cardiac structure and function.

METHODS

CMR Displacement Encoding with Stimulated Echoes (DENSE) strain imaging was used to generate the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) circumferential strain dyssynchrony parameter, and the SHFM-D was determined from clinical parameters. Multivariable Cox proportional hazards regression was used to determine adjusted hazard ratios and time-dependent areas under the curve for the primary endpoint of death, heart transplantation, left ventricular assist device, or appropriate ICD therapies.

RESULTS

The cohort consisted of 100 patients (65.5 [interquartile range 57.7 to 72.7] years; 29% female), of whom 47% had the primary clinical endpoint and 18% had appropriate ICD therapies during a median follow-up of 5.3 years. CURE-SVD and the SHFM-D were independently associated with the primary endpoint (SHFM-D: hazard ratio: 1.47/SD; 95% confidence interval: 1.06 to 2.03; p = 0.02) (CURE-SVD: hazard ratio: 1.54/SD; 95% confidence interval: 1.12 to 2.11; p = 0.009). Furthermore, a favorable prognostic group (Group A, with CURE-SVD <0.60 and SHFM-D <0.70) comprising approximately one-third of the patients had a very low rate of appropriate ICD therapies (1.5% per year) and a greater (90%) 4-year survival compared with Group B (CURE-SVD ≥0.60 or SHFM-D ≥0.70) patients (p = 0.02). CURE-SVD with DENSE had a stronger correlation with CRT response (r = -0.57; p < 0.0001) than CURE-SVD with feature tracking (r = -0.28; p = 0.004).

CONCLUSIONS

A combined approach to risk stratification using CMR DENSE strain imaging and a widely used clinical risk model, the SHFM-D, proved to be effective in this cohort of patients referred for CRT defibrillators. The combined use of CMR and clinical risk models represents a promising and novel paradigm to inform prognosis and device selection in the future.

Frequency of Coronary Microvascular Dysfunction and Diffuse Myocardial Fibrosis (Measured by Cardiovascular Magnetic Resonance) in Patients With Heart Failure and Preserved Left Ventricular Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is frequently accompanied by co-morbidities and a systemic proinflammatory state, resulting in coronary microvascular dysfunction (CMD), as well as myocardial fibrosis. The purpose of this study is to examine the relation between myocardial perfusion reserve (MPR) and diffuse myocardial fibrosis in patients with HFpEF using cardiovascular magnetic resonance. A single center study was performed in 19 patients with clinical HFpEF and 15 healthy control subjects who underwent quantitative first-pass perfusion imaging to calculate global MPR. T1 mapping was used to assess fibrosis and to calculate extracellular volume. Spiral cine displacement encoded stimulated echo was used to calculate myocardial strain. Comprehensive 2D echocardiograms with speckle tracking, cardiopulmonary exercise testing, and brain natriuretic peptide levels were also obtained. In patients with HFpEF, mean left ventricular EF was 61% ± 9% and left ventricular mass index 45 ± 12 g/m². Compared with controls, HFpEF patients had reduced global MPR (2.29 ± 0.64 vs 3.38 ± 0.76, p = 0.002) and VO₂ max (16.5 ± 6.8 vs 30.9 ± 7.7 ml/kg min, p <0.001) whereas extracellular volume (0.29 ± 0.04 vs 0.25 ± 0.04, p = 0.02), pulmonary artery systolic pressure (35.4 ± 13.7 vs 22.3 ± 5.4 mm Hg, p = 0.004), and average E/e' (15.0 ± 7.6 vs 8.6 ± 2.0, p = 0.005) were increased. Displacement encoded stimulated echo peak systolic circumferential strain (p = 0.60) as well as echocardiographic derived global longitudinal strain (p = 0.07) were similar between both groups. The prevalence of CMD, defined as global MPR <2.5, in the HFpEF group was 69%. In conclusion, HFpEF patients have a high prevalence of CMD and diffuse fibrosis. These parameters may be useful clinical end points for future therapeutic trials.