Displacement Encoding With Stimulated Echoes Research Articles

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.

Abstract 16153: Electrocardiographic Correlations With Cardiac Magnetic Resonance in Left Bundle Branch Area Pacing

Introduction: As the use of left bundle branch area pacing (LBBAP) continues to increase, there remains uncertainty about the mechanical effects of achieving selective LBB capture. Hypothesis: The hypothesis was that cardiac magnetic resonance (CMR) strain and functional parameters would be similar with and without LBBAP, and mechanical function would be more favorable with surface ECG parameters indicative of LBB capture. Methods: CMR was performed at 3T in two centers in the first year after implantation of dual-chamber pacemakers with LBBAP. Imaging sets were obtained with dual-chamber LBBAP (DOO mode) and, in patients with intact atrioventricular (A-V) conduction, atrial-only pacing (AOO mode), including strain imaging with displacement encoding with stimulated echoes (DENSE). Results: In 17 patients, the median LVEF with LBBAP was 50% (IQR 48-52%). The median LVEF and RVEF were similar with LBBAP on (DOO mode) and off (AOO mode) (p=0.27 and p=0.37, respectively); however, there were differences in synchrony (CURE-SVD) (p=0.0081), mean septal Ecc (p=0.011), and mean LV free wall Ecc (p=0.011) that favored atrial-only pacing with A-V conduction vs. sequential LBBAP (Figure 1). A shorter interval between R wave peaks in ECG leads V6 and V1 (V6-V1 interpeak interval), shown to be associated with selective LBB capture, was the best predictor of a greater LVEF with LBBAP (p=0.0016). A shorter paced QRS duration predicted greater global Ecc (p=0.044) and more synchrony by the CURE-SVD (p=0.027) (Figure 2). Conclusions: LBBAP reproduces biventricular function by CMR with some differences noted in CMR strain, and selective LBB capture was associated with a greater LVEF by CMR.

Estimating the viscoelastic properties of the human brain at 7 T MRI using intrinsic MRE and nonlinear inversion.

Intrinsic actuation magnetic resonance elastography (MRE) is a phase-contrast MRI technique that allows for in vivo quantification of mechanical properties of the brain by exploiting brain motion that arise naturally due to the cardiac pulse. The mechanical properties of the brain reflect its tissue microstructure, making it a potentially valuable parameter in studying brain disease. The main purpose of this study was to assess the feasibility of reconstructing the viscoelastic properties of the brain using high-quality 7 T MRI displacement measurements, obtained using displacement encoding with stimulated echoes (DENSE) and intrinsic actuation. The repeatability and sensitivity of the method for detecting normal regional variation in brain tissue properties was assessed as secondary goal. The displacement measurements used in this analysis were previously acquired for a separate study, where eight healthy subjects (27 ± 7 years) were imaged with repeated scans (spatial resolution approx. 2 mm isotropic, temporal resolution 75 ms, motion sensitivity 0.35 mm/2π for displacements in anterior-posterior and left-right directions, and 0.7 mm/2π for feet-head displacements). The viscoelastic properties of the brain were estimated using a subzone based non-linear inversion scheme. The results show comparable consistency to that of extrinsic MRE between the viscoelastic property maps obtained from repeated displacement measurements. The shear stiffness maps showed fairly consistent spatial patterns. The whole-brain repeatability coefficient (RC) for shear stiffness was (mean ± standard deviation) 8 ± 8% relative to the mean whole-brain stiffness, and the damping ratio RC was 28 ± 17% relative to the whole-brain damping ratio. The shear stiffness maps showed similar statistically significant regional trends as demonstrated in a publicly available atlas of viscoelastic properties obtained with extrinsic actuation MRE at 50 Hz. The damping ratio maps showed less consistency, likely due to data-model mismatch of describing the brain as a viscoelastic material under low frequencies. While artifacts induced by fluid flow within the brain remain a limitation of the technique in its current state, intrinsic actuation based MRE allow for consistent and repeatable estimation of the mechanical properties of the brain. The method provides enough sensitivity to investigate regional variation in such properties in the normal brain, which is likely sufficient to also investigate pathological changes.

Measuring Cardiac Dyssynchrony with DENSE (Displacement Encoding with Stimulated Echoes)—A Systematic Review

Measuring Cardiac Dyssynchrony with DENSE (Displacement Encoding with Stimulated Echoes)—A Systematic Review

Association between resistance to cerebrospinal fluid flow and cardiac-induced brain tissue motion for Chiari malformation type I.

Chiari malformation type I (CMI) patients have been independently shown to have both increased resistance to cerebrospinal fluid (CSF) flow in the cervical spinal canal and greater cardiac-induced neural tissue motion compared to healthy controls. The goal of this paper is to determine if a relationship exists between CSF flow resistance and brain tissue motion in CMI subjects. Computational fluid dynamics (CFD) techniques were employed to compute integrated longitudinal impedance (ILI) as a measure of unsteady resistance to CSF flow in the cervical spinal canal in thirty-two CMI subjects and eighteen healthy controls. Neural tissue motion during the cardiac cycle was assessed using displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) technique. The results demonstrate a positive correlation between resistance to CSF flow and the maximum displacement of the cerebellum for CMI subjects (r = 0.75, p = 6.77 × 10-10) but not for healthy controls. No correlation was found between CSF flow resistance and maximum displacement in the brainstem for CMI or healthy subjects. The magnitude of resistance to CSF flow and maximum cardiac-induced brain tissue motion were not statistically different for CMI subjects with and without the presence of five CMI symptoms: imbalance, vertigo, swallowing difficulties, nausea or vomiting, and hoarseness. This study establishes a relationship between CSF flow resistance in the cervical spinal canal and cardiac-induced brain tissue motion in the cerebellum for CMI subjects. Further research is necessary to understand the importance of resistance and brain tissue motion in the symptomatology of CMI.

Abstract P2183: Validation Of A Methodology For Left Ventricular Global Longitudinal Strain Measured With Deformation Encoding Mri To Detect Cardiotoxicity

This study sought to validate left-ventricular (LV) global longitudinal strain (GLS) derived from an MRI deformation encoding technique for detecting cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients. Displacement Encoding with Stimulated Echoes (DENSE) cardiac MRI and 2D echocardiographic images for validation were acquired on 32 breast cancer patients at baseline and 3- and 6-month follow-ups after chemotherapy with antineoplastic agents. The DENSE images were processed with automated Artificial Intelligence segmentation techniques for chamber quantification and 3D strain analysis. The corresponding echocardiographic data were processed with 2D speckle-tracking echocardiography (2D-STE). To establish the reliability of the DENSE-AI tool, intraclass correlation coefficients (ICC) were computed between DENSE and 2DE-derived GLS and other parameters at each time point with Cronbach’s Alpha (C-α) indices. CTRCD risk analyses were conducted on the DENSE-AI and 2D-STE measured parameters with Cox proportional hazard (PH) regression models. The ICC reliability results between DENSE and 2DE-derived GLS were C-α = 0.89 at baseline, C-α = 0.93 at 3 months, and C-α = 0.94 at 6 months. Both DENSE and 2D-STE GLS worsened from baseline to the 3- and 6-month follow-ups (-19.1 ± 2.1% vs. -16.0 ± 3.1% vs. -16.1 ± 2.9%, P<0.001) (Fig. 1). Cox regression showed the 3-month DENSE (HR-per-SD: 2.1; 95% CI: 1.4-3.1, P<0.001) GLS measurements as an independent prognosticator of CTRCD occurrence. In conclusion, LV GLS measurement with the DENSE-AI tool was validated to detect CTRCD in breast cancer patients treated with antineoplastic chemotherapy.

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

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. 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. 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. 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.

Multi-frame biomechanical and relaxometry analysis during in vivo loading of the human knee by spiral dualMRI and compressed sensing.

Knee cartilage experiences repetitive loading during physical activities, which is altered during the pathogenesis of diseases like osteoarthritis. Analyzing the biomechanics during motion provides a clear understanding of the dynamics of cartilage deformation and may establish essential imaging biomarkers of early-stage disease. However, in vivo biomechanical analysis of cartilage during rapid motion is not well established. We used spiral displacement encoding with stimulated echoes (DENSE) MRI on in vivo human tibiofemoral cartilage during cyclic varus loading (0.5 Hz) and used compressed sensing on the k-space data. The applied compressive load was set for each participant at 0.5 times body weight on the medial condyle. Relaxometry methods were measured on the cartilage before (T1ρ , T2 ) and after (T1ρ ) varus load. Displacement and strain maps showed a gradual shift of displacement and strain in time. Compressive strain was observed in the medial condyle cartilage and shear strain was roughly half of the compressive strain. Male participants had more displacement in the loading direction compared to females, and T1ρ values did not change after cyclic varus load. Compressed sensing reduced the scanning time up to 25% to 40% when comparing the displacement maps and substantially lowered the noise levels. These results demonstrated the ease of which spiral DENSE MRI could be applied to clinical studies because of the shortened imaging time, while quantifying realistic cartilage deformations that occur through daily activities and that could serve as biomarkers of early osteoarthritis.

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

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

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

BackgroundCine 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. Methods2D + 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. ResultsSpatio-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. ConclusionSpatio-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.

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

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. 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. 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. 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.

Abstract 13581: Prognostic Value of Left Ventricular Global Longitudinal Strain for Early Prediction of Cancer Therapy-Related Cardiac Dysfunction

Introduction: This prospective study aimed to determine if MRI-imaging and Artificial Intelligence (AI)-based computation of global longitudinal strain (GLS) in the left ventricle (LV) is an early predictor of cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients. Hypothesis: To show with risk analysis that GLS is an independent prognosticator of CTRCD when compared to LV ejection fraction (LVEF) with the patient cohort administered antineoplastic chemotherapy treatment. Methods: Motion-encoded data with Displacement Encoding with Stimulated Echoes (DENSE) MRI were acquired on 32 breast cancer patients at baseline and 3- and 6-months follow-ups after chemotherapy. Automated image segmentation for chamber quantification and phase-unwrapping in the LV occurred with two DeepLabV3+ fully convolutional networks (FCNs), and 3D strains computed with the meshfree Radial Point Interpolation Method. CTRCD (including adverse cardiac events and cardiotoxicity) risk analyses with the measured clinical, chamber quantification, and strain parameters were conducted with univariable and bivariable Cox proportional hazard (PH) regressions. Results: GLS worsened from baseline to the 3- and 6-months follow-ups (-19.1 ± 2.1% vs. -16.0 ± 3.1% vs. -16.1 ± 2.9%, P&lt;0.001) (Fig. 1). With univariable Cox regression, the 3-months follow-up GLS was independently associated as an agonist (hazard ratio [HR]-per-SD: 2.1; 95% CI: 1.4-3.1, P&lt;0.001) and LVEF as protector (HR-per-SD: 0.8; 95% CI: 0.7-0.9, P&lt;0.001) for CTRCD occurrence. In bivariable regression, GLS (HR-per-SD: 2.0; 95% CI: 1.2-3.4, P=0.01) was an independent predictor of CTRCD regardless of LVEF (HR-per-SD: 1.0; 95% CI: 0.9-1.2, P=0.9) as covariate. Conclusions: The end-point risk analyses show that LV GLS is an early CTRCD predictor independent of LVEF in breast cancer patients treated with antineoplastic chemotherapy. GLS should be a standard metric for the clinical diagnosis of CTRCD.

Cardiac magnetic resonance defines mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy.

BackgroundMechanisms of sex-based differences in outcomes following cardiac resynchronization therapy (CRT) are poorly understood.ObjectiveTo use cardiac magnetic resonance (CMR) to define mechanisms of sex-based differences in outcomes after CRT and describe distinct CMR-based phenotypes of CRT candidates based on sex and non-ischemic/ischemic cardiomyopathy type.Materials and methodsIn a prospective study, sex-based differences in three short-term CRT response measures [fractional change in left ventricular end-systolic volume index 6 months after CRT (LVESVI-FC), B-type natriuretic peptide (BNP) 6 months after CRT, change in peak VO2 6 months after CRT], and long-term survival were evaluated with respect to 39 baseline parameters from CMR, exercise testing, laboratory testing, electrocardiograms, comorbid conditions, and other sources. CMR was also used to quantify the degree of left-ventricular mechanical dyssynchrony by deriving the circumferential uniformity ratio estimate (CURE-SVD) parameter from displacement encoding with stimulated echoes (DENSE) strain imaging. Statistical methods included multivariable linear regression with evaluation of interaction effects associated with sex and cardiomyopathy type (ischemic and non-ischemic cardiomyopathy) and survival analysis.ResultsAmong 200 patients, the 54 female patients (27%) pre-CRT had a smaller CMR-based LVEDVI (p = 0.04), more mechanical dyssynchrony based on the validated CMR CURE-SVD parameter (p = 0.04), a lower frequency of both late gadolinium enhancement (LGE) and ischemic cardiomyopathy (p < 0.0001), a greater RVEF (p = 0.02), and a greater frequency of LBBB (p = 0.01). After categorization of patients into four groups based on cardiomyopathy type (ischemic/non-ischemic cardiomyopathy) and sex, female patients with non-ischemic cardiomyopathy had the lowest CURE-SVD (p = 0.003), the lowest pre-CRT BNP levels (p = 0.01), the lowest post-CRT BNP levels (p = 0.05), and the most favorable LVESVI-FC (p = 0.001). Overall, female patients had better 3-year survival before adjustment for cardiomyopathy type (p = 0.007, HR = 0.45) and after adjustment for cardiomyopathy type (p = 0.009, HR = 0.67).ConclusionCMR identifies distinct phenotypes of female CRT patients with non-ischemic and ischemic cardiomyopathy relative to male patients stratified by cardiomyopathy type. The more favorable short-term response and long-term survival outcomes in female heart failure patients with CRT were associated with lower indexed CMR-based LV volumes, decreased presence of scar associated with prior myocardial infarction and ICM, and greater CMR-based dyssynchrony with the CURE-SVD.

Quantification of the heterogeneous effect of static and dynamic perivascular structures on patient-specific local aortic wall mechanics using inverse finite element modeling and DENSE MRI

Recent studies have highlighted the relevance of perivascular interactions on aortic wall mechanics. Most of the approaches assume static perivascular structures; however, the beating heart dynamically displaces the neighboring aorta. We develop a model to account for the effect of periaortic interactions due to static and dynamic structures by prescribing a moving elastic foundation boundary condition (EFBC) embedded into an inverse finite element algorithm using in vivo displacements from 2D displacement encoding with stimulated echoes (DENSE) MRI as target data. We applied this method at three different locations of interest, the distal aortic arch (DAA), descending thoracic aorta (DTA), and infrarenal abdominal aorta (IAA) for a total of 27 cases in healthy humans. The model reproduces the target diastole-to-systole deformation and bulk displacement of the aortic wall with median displacement errors below 0.5mm. The EFBC showed good agreement with the location of anatomical features and was consistent among individuals of similar characteristics. Results show that an energy source acting on the adventitia is required to reproduce the displacements measured at the vicinity of the heart, but not at the abdomen. The average adventitial load as a percentage of the luminal pulse-pressure was found to increase with age and to decrease along the descending aorta, from 61% at the DAA to 37% at the DTA, and 30% at the IAA. This approach offers a patient-specific method to estimate in vivo adventitial loads and aortic wall stiffness, which can bring a better understanding of normal and pathological in vivo aortic function.

Reproducibility of global and segmental myocardial strain using cine DENSE at 3\xa0T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with\xa0heart disease

BackgroundWhile 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.MethodsSix 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.ResultsBased 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.ConclusionsMulticenter 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.

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.

Abstract 13731: Randomized Trial of CMR Guidance for Cardiac Resynchronization Therapy: Findings in Nonischemic Cardiomyopathy From an Interim Analysis

Introduction: Cardiac resynchronization therapy (CRT) response rates in unselected cohorts are suboptimal. Cardiac magnetic resonance (CMR) provides high-quality imaging of strain and scar, and post-CRT CMR offers reliable assessment of left ventricular (LV) reverse remodeling. Hypothesis: Cardiac magnetic resonance (CMR) guidance for CRT will increase CRT response rates, particularly in nonischemic cardiomyopathy (NICM). Methods: Patients were randomized 1:1 to standard CRT or CMR guidance for CRT using circumferential strain from Displacement Encoding with Stimulated Echoes (DENSE) and scar (late gadolinium enhancement [LGE]) by CMR. The primary endpoint is the percent change in LV end-systolic volume (LVESV) calculated from CMRs before CRT and 6 months after CRT. A pre-specified interim analysis by cardiomyopathy type was performed after completion of enrollment of 45 of the projected 90 patients needed for adequate power. Results: Of 45 patients enrolled (age 65.9 +/- 10.3 years, 29% female), two patients did not undergo a CRT procedure, and the LV lead could not be placed in one patient. Of the remaining 42 patients, 26/42 had NICM and 21/42 were randomized to CMR guidance. In a linear regression model for the primary endpoint adjusted for baseline QRS duration (p=0.04) and cardiomyopathy type (p=0.01), a favorable trend for the intervention on response was noted (p=0.06). Box plots demonstrate a significant difference in the primary endpoint among NICM patients (p=0.03) and a favorable trend for QRS shortening (0.07) in NICM patients (Figure). In all patients (including ICM patients), the overall p-value for the intervention relative to the primary endpoint was 0.08. Conclusion: An interim analysis in CRT patients with NICM demonstrated a favorable effect of CMR guidance on LV reverse remodeling after CRT, as assessed with post-CRT CMRs. Enrollment is continuing to evaluate the effect of the intervention on the entire cohort and other endpoints.

Abstract 12414: 3D Fusion of MRI Mechanical Activation, Cardioinsight Electrical Activation, and Fluoroscopic Lead Location for CRT

Introduction: 3D image integration can help achieve personalized medicine for patients undergoing device therapy for heart failure, such as cardiac resynchronization therapy (CRT). Hypothesis: Electrical mapping data and mechanical activation from cardiac magnetic resonance (CMR) integrated with live fluoroscopic data will provide complementary information to optimize CRT procedures. Methods: Ten patients underwent cardiac CT and electrical mapping with the CardioInsight 252-electrode vest (Medtronic) during CRT implantation using a 3D-capable fluoroscopy system (Artis zeego, Siemens) and a 3D image integration software prototype (Guide-CRT, Siemens). A myocardial mechanical activation map based on strain from CMR Displacement Encoding with Stimulated Echoes (DENSE) and an electrical activation map from CardioInsight were fused with fluoroscopic images using Guide-CRT. Results: In a representative patient with LBBB and QRS duration 140 ms, integrated 3D maps of electrical and mechanical activation with the fluoroscopic lead location are shown for right/left anterior oblique (RAO/LAO) and anterior-posterior (AP) views (Figure). While the area of late mechanical activation is centered at about 3 o’clock on the mitral annulus, a slightly more anterolateral and smaller focus of late electrical activation is noted on the CardioInsight maps (Figure). Furthermore, more basal activation data is possible with the electrical maps, as strain imaging typically starts below the mitral valve. While a middle LV electrode (LV2-4) was chosen for CRT pacing based on the CMR map, the more proximal and anterolateral LV electrode could have been chosen based on the electrical map. Conclusions: Multimodality mapping and 3D fusion of mechanical activation, electrical activation, and lead position provide important information regarding electromechanical coupling and the choice of the optimal pacing location.

Abstract P345: A MRI Displacement-based Deep-learning Semantic Segmentation Tool For Left-ventricular Longitudinal Strain Analysis In Cardiotoxicity

Global longitudinal strain (GLS) computed in the left-ventricle (LV) is an established metric for detecting cardiotoxicity in breast cancer patients treated with antineoplastic agents. The purpose of this study was to develop a novel, MRI-based, deep-learning semantic segmentation tool that automates the phase-unwrapping for LV displacement computation in GLS. Strain analysis via phase-unwrapping was conducted on 30 breast cancer patients and 30 healthy females acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence. A ResNet-50 deep convolutional neural network (DCNN) architecture for automated phase-unwrapping, a previously validated ResNet-50 DCNN for chamber quantification and the Radial Point Interpolation Method were used for GLS computation (Figure 1). The DCNN's performance was measured with F1 and Dice scores, and validated in comparison to the robust transport of intensity equation (RTIE) and quality guided phase-unwrapping (QGPU) conventional algorithms. The three techniques were compared by intraclass correlation coefficient with Cronbach’s alpha (C-alpha) index. Classification accuracy with the DCNN was F1 score of 0.92 and Dice score of 0.89. The GLS results from RTIE, QGPU and DCNN were -16.0 ± 2%, -16.1 ± 3% and -15.9 ± 3% (C-alpha = 0.89) for patients and -18.9 ± 3%, -19.0 ± 4% and -18.9 ± 3% (C-alpha = 0.92) for healthy subjects. Comparable validation results from the three techniques show the feasibility of a DCNN-based approach to LV displacement and GLS analysis. The dissimilarities between patients and healthy subjects demonstrate that DCNN-based GLS computation may detect LV abnormalities related to cardiotoxicity.

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

ObjectivesThe 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. BackgroundCMR with cardiac implantable electronic devices can safely provide high-quality right ventricular/left ventricular (LV) ejection fraction (RVEF/LVEF) assessments and strain. MethodsCMR 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. Results100 CMRs with a total of 162 imaging sets were performed in 50 patients (median age 70 years [IQR: 50-86 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 (DENSE) 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). ConclusionsCMR 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.