Improvements of Myocardial Deformation Assessment by Three-Dimensional Speckle-Tracking versus Two-Dimensional Speckle-Tracking Revealed by Cardiac Magnetic Resonance Tagging.

BACKGROUND: In prior work, the authors demonstrated that two-dimensional speckle-tracking (2DST) correlated well but systematically overestimated global longitudinal strain (LS) and circumferential strain (CS) compared with two-dimensional cardiac magnetic resonance tagging (2DTagg) and had poor agreement on a segmental basis. Because three-dimensional speckle-tracking (3DST) has recently emerged as a new, more comprehensive evaluation of myocardial deformation, this study was undertaken to evaluate whether it would compare more favorably with 2DTagg than 2DST. METHODS: In a prospective two-center trial, 119 subjects (29 healthy volunteers, 63 patients with left ventricular dysfunction, and 27 patients with left ventricular hypertrophy) underwent 2DST, 3DST, and 2DTagg. Global, regional (basal, mid, and apical), and segmental (18 and 16 segments per patient) LS and CS by 2DST and 3DST were compared with 2DTagg using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Test-retest reproducibility of 3DST and 2DST was compared in 48 other patients. RESULTS: Both global LS and CS by 3DST agreed better with 2DTagg (ICC = 0.89 and ICC = 0.83, P < .001 for both; bias = 0.5 ± 2.3% and 0.2 ± 3%) than 2DST (ICC = 0.65 and ICC = 0.55, P < .001 for both; bias = -5.5 ± 2.5% and -7 ± 5.3%). Unlike 2DST, 3DST did not overestimate deformation at the regional and particularly the apical levels and at the segmental level had lower bias (LS, 0.8 ± 2.8% vs -5.3 ± 2.4%; CS, -0.01 ± 2.8% vs -7 ± 2.8%, respectively) but similar agreement with 2DST (LS: ICC = 0.58 ± 0.16 vs 0.56 ± 0.12; CS: ICC = 0.58 ± 0.12 vs 0.51 ± 0.1) with 2DTagg. Finally, 3DST had similar global LS, but better global CS test-retest variability than 2DST. CONCLUSIONS: Using 2DTagg as reference, 3DST had better agreement and less bias for global and regional LS and CS. At the segmental level, 3DST demonstrated comparable agreement but lower bias versus 2DTagg compared with 2DST. Also, test-retest variability for global CS by 3DST was better than by 2DST. This suggests that 3DST is superior to 2DST for analysis of global and regional myocardial deformation, but further refinement is needed for both 3DST and 2DST at the segmental level.

Head-to-Head Comparison of Global and Regional Two-Dimensional Speckle Tracking Strain Versus Cardiac Magnetic Resonance Tagging in a Multicenter Validation Study.

BACKGROUND: Despite widespread use to characterize and refine prognosis, validation data of two-dimensional (2D) speckle tracking (2DST) echocardiography myocardial strain measurement remain scarce. METHODS AND RESULTS: Global and regional subendocardial peak-systolic Lagrangian longitudinal (LS) and circumferential strain (CS) by 2DST and 2D-tagged (2DTagg) cardiac magnetic resonance imaging were compared against sonomicrometry in a dynamic heart phantom and among each other in 136 patients included prospectively at 2 centers. The ability of regional LS and CS 2DST and 2DTagg to identify late gadolinium enhancement was compared using receiver operating characteristics curves. In vitro, both LS-2DST and 2DTagg highly agreed with sonomicrometry (intraclass correlation coefficient [ICC], 0.89 and ICC, 0.90, both P<0.001 with -3±2.8% and 0.34±4.35% bias, respectively). In patients, both global LS and global CS 2DST agreed well with 2DTagg (ICC, 0.89 and ICC, 0.80; P<0.001); however, they provided systematically greater values (relative bias of -37±27% and -25±37% for global LS and global CS, respectively). On regional basis, however, ICC (from 0.17 to 0.81) and relative bias (from -9 to -98%) between 2DST and 2DTagg varied strongly among segments. Ability to discriminate infarcted versus noninfarcted segments by late gadolinium enhancement was similarly good for regional LS 2DTagg and 2DST (area under the curve, 0.66 versus 0.59; P=0.08), while it was lower for CS 2DST than 2DTagg (area under the curve, 0.61 versus 0.75; P<0.001). CONCLUSIONS: The high accuracy against sonomicrometry and good agreement of global LS and global CS by 2DST and 2DTagg confirm the overall validity of 2DST strain measurement. Yet, higher intertechnique segmental variability and lower ability for detecting infarct suggest that 2DST strain estimates may be less performant on regional than on global basis.

Infarct Localization From Myocardial Deformation: Prediction and Uncertainty Quantification by Regression From a Low-Dimensional Space.

Diagnosing and localizing myocardial infarct is crucial for early patient management and therapy planning. We propose a new method for predicting the location of myocardial infarct from local wall deformation, which has value for risk stratification from routine examinations such as (3D) echocardiography. The pipeline combines non-linear dimensionality reduction of deformation patterns and two multi-scale kernel regressions. Confidence in the diagnosis is assessed by a map of local uncertainties, which integrates plausible infarct locations generated from the space of reduced dimensionality. These concepts were tested on 500 synthetic cases generated from a realistic cardiac electromechanical model, and 108 pairs of 3D echocardiographic sequences and delayed-enhancement magnetic resonance images from real cases. Infarct prediction is made at a spatial resolution around 4 mm, more than 10 times smaller than the current diagnosis, made regionally. Our method is accurate, and significantly outperforms the clinically-used thresholding of the deformation patterns (on real data: sensitivity/specificity of 0.828/0.804, area under the curve: 0.909 versus 0.742 for the most predictive strain component). Uncertainty adds value to refine the diagnosis and eventually re-examine suspicious cases.

3D harmonic phase tracking with anatomical regularization.

This paper presents a novel algorithm that extends HARP to handle 3D tagged MRI images. HARP results were regularized by an original regularization framework defined in an anatomical space of coordinates. In the meantime, myocardium incompressibility was integrated in order to correct the radial strain which is reported to be more challenging to recover. Both the tracking and regularization of LV displacements were done on a volumetric mesh to be computationally efficient. Also, a window-weighted regression method was extended to cardiac motion tracking which helps maintain a low complexity even at finer scales. On healthy volunteers, the tracking accuracy was found to be as accurate as the best candidates of a recent benchmark. Strain accuracy was evaluated on synthetic data, showing low bias and strain errors under 5% (excluding outliers) for longitudinal and circumferential strains, while the second and third quartiles of the radial strain errors are in the (-5%,5%) range. In clinical data, strain dispersion was shown to correlate with the extent of transmural fibrosis. Also, reduced deformation values were found inside infarcted segments.

Diagnostic value of three-dimensional contrast-enhanced echocardiography for left ventricular volume and ejection fraction measurement in patients with poor acoustic windows: a comparison of echocardiography and magnetic resonance imaging.

BACKGROUND: Three-dimensional echocardiography (3DE) is a reliable and reproducible tool for assessing left ventricular (LV) function but remains sensitive to patient echogenicity. Contrast-enhanced 3DE (C3DE) has the potential to improve quantification in challenging patients. The aim of this study was to evaluate the impact of temporal resolution, spatial resolution, and image dynamic range on LV function assessed using C3DE compared with cardiac magnetic resonance imaging (MRI) in patients with poor echogenicity. METHODS: Forty-one patients with poor echogenicity who underwent two-dimensional echocardiography (2DE), 3DE, C3DE, and MRI were retrospectively investigated. RESULTS: Before contrast injection, 24 patients had three or more nonvisible segments. Three cases of 2DE and 12 cases of 3DE were not suitable for quantification. LV end-diastolic volumes were systematically underestimated by 2DE (142 ± 58 mL), 3DE (146 ± 69 mL), and C3DE (172 ± 61 mL) compared with MRI (216 ± 85 mL) (P < .001). Similar results were found for LV end-systolic volumes (81 ± 65 mL for 2DE, 82 ± 69 mL for 3DE, and 102 ± 80 mL for C3DE vs 129 ± 94 mL for MRI; P < .001). C3DE provided the best agreement with MRI (Lin concordance correlation coefficients of 0.67, 0.93, and 0.99, respectively, for end-diastolic volume, end-systolic volume, and ejection fraction) as well as the best measurement reproducibility. Finally, ultrasound settings had no significant effect on LV volumes and ejection fraction measurements. CONCLUSIONS: In these patients with poor ultrasound image quality, C3DE, regardless of instrument settings, outperformed 2DE and 3DE to assess LV volumes and ejection fraction and can thus be proposed as an acceptable alternative when MRI cannot be performed in this subgroup.

Cardiac electrophysiological activation pattern estimation from images using a patient-specific database of synthetic image sequences.

While abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases (e.g., arrhythmia, asynchrony), the only clinically available method to observe detailed left ventricular endocardial surface activation pattern is through invasive catheter mapping. However, this electrophysiological activation controls the onset of the mechanical contraction; therefore, important information about the electrophysiology could be deduced from the detailed observation of the resulting motion patterns. In this paper, we present the study of this inverse cardiac electrokinematic relationship. The objective is to predict the activation pattern knowing the cardiac motion from the analysis of cardiac image sequences. To achieve this, we propose to create a rich patient-specific database of synthetic time series of the cardiac images using simulations of a personalized cardiac electromechanical model, in order to study this complex relationship between electrical activity and kinematic patterns in the context of this specific patient. We use this database to train a machine-learning algorithm which estimates the depolarization times of each cardiac segment from global and regional kinematic descriptors based on displacements or strains and their derivatives. Finally, we use this learning to estimate the patient's electrical activation times using the acquired clinical images. Experiments on the inverse electrokinematic learning are demonstrated on synthetic sequences and are evaluated on clinical data with promising results. The error calculated between our prediction and the invasive intracardiac mapping ground truth is relatively small (around 10 ms for ischemic patients and 20 ms for nonischemic patient). This approach suggests the possibility of noninvasive electrophysiological pattern estimation using cardiac motion imaging.

Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images.

We propose a new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4-D image sequences. This is achieved by combining three steps. The first step is the simulation of a cardiac motion using an electromechanical model of the heart and the segmentation of the end diastolic image of a cardiac sequence. We use biophysical parameters related to the desired condition of the simulated subject. The second step extracts the cardiac motion from the real sequence using nonrigid image registration. Finally, a synthetic time series of cardiac images corresponding to the simulated motion is generated in the third step by combining the motion estimated by image registration and the simulated one. With this approach, image processing algorithms can be evaluated as we know the ground-truth motion underlying the image sequence. Moreover, databases of visually realistic images of controls and patients can be generated for which the underlying cardiac motion and some biophysical parameters are known. Such databases can open new avenues for machine learning approaches.

Synthetic echocardiographic image sequences for cardiac inverse electro-kinematic learning.

In this paper, we propose to create a rich database of synthetic time series of 3D echocardiography (US) images using simulations of a cardiac electromechanical model, in order to study the relationship between electrical disorders and kinematic patterns visible in medical images. From a real 4D sequence, a software pipeline is applied to create several synthetic sequences by combining various steps including motion tracking and segmentation. We use here this synthetic database to train a machine learning algorithm which estimates the depolarization times of each cardiac segment from invariant kinematic descriptors such as local displacements or strains. First experiments on the inverse electrokinematic learning are demonstrated on the synthetic 3D US database and are evaluated on clinical 3D US sequences from two patients with Left Bundle Branch Block.

Integrating functional and anatomical information to facilitate cardiac resynchronization therapy.

Multiple imaging modalities are required in patients receiving cardiac resynchronization therapy. We have developed a strategy to integrate echocardiographic and angiographic information to facilitate left ventricle (LV) lead position. Full three-dimensional LV-volumes (3DLVV) and dyssynchrony maps were acquired before and after resynchronization. At the time of device implantation, 3D-rotational coronary venous angiography was performed. 3D-models of the veins were then integrated with the pre- and post-3DLVV. In the case displayed, prior to implantation, the lateral wall was delayed compared to the septum. The LV lead was positioned into the vein over the most delayed region, resulting in improved LV synchrony.

Rapid online quantification of left ventricular volume from real-time three-dimensional echocardiographic data.

AIMS: Determination of left ventricular (LV) volumes and ejection fraction (EF) from two-dimensional echocardiographic (2DE) images is subjective, time-consuming, and relatively inaccurate because of foreshortened views and the use of geometric assumptions. Our aims were (1) to validate a new method for rapid, online measurement of LV volumes from real-time three-dimensional echocardiographic (RT3DE) data using cardiac magnetic resonance (CMR) as the reference and (2) to compare its accuracy and reproducibility with standard 2DE measurements. METHODS AND RESULTS: CMR, 2DE, and RT3DE datasets were obtained in 50 patients. End-systolic and end-diastolic volumes (ESV and EDV) were calculated from the 2DE images using biplane method of disks. ES and ED RT3DE datasets were analysed using prototype software designed to automatically detect the endocardial surface using a deformable shell model and calculate ESV and EDV from voxel counts. 2DE and RT3DE-derived volumes were compared with CMR (linear regression, Bland-Altman analysis). In most patients, analysis of RT3DE data required <2 min per patient. RT3DE measurements correlated highly with CMR (r: 0.96, 0.97, and 0.93 for EDV, ESV, and EF, respectively) with small biases (-14 mL, -6.5 mL, -1%) and narrow limits of agreement (SD: 17 mL, 16 mL, 6.4%). 2DE measurements correlated less well with CMR (r: 0.89, 0.92, 0.86) with greater biases (-23 mL, -15 mL, 1%) and wider limits of agreement (SD: 29 mL, 24 mL, 9.5%). RT3DE resulted in lower intra-observer (EDV: 7.9 vs. 23%; ESV: 7.6 vs. 26%) and inter-observer variability (EDV: 11 vs. 26%; ESV: 13 vs. 31%). CONCLUSION: Semi-automated detection of the LV endocardial surface from RT3DE data is suitable for clinical use because it allows rapid, accurate, and reproducible measurements of LV volumes, superior to conventional 2DE methods.