Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.

PURPOSE: The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations. MATERIALS AND METHODS: Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio. RESULTS: Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850-0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810-0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668-0.760) and of 0.723 (95% CI: 0.668-0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set. CONCLUSION: This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.

Motion Estimation by Deep Learning in 2D Echocardiography: Synthetic Dataset and Validation.

Motion estimation in echocardiography plays an important role in the characterization of cardiac function, allowing the computation of myocardial deformation indices. However, there exist limitations in clinical practice, particularly with regard to the accuracy and robustness of measurements extracted from images. We therefore propose a novel deep learning solution for motion estimation in echocardiography. Our network corresponds to a modified version of PWC-Net which achieves high performance on ultrasound sequences. In parallel, we designed a novel simulation pipeline allowing the generation of a large amount of realistic B-mode sequences. These synthetic data, together with strategies during training and inference, were used to improve the performance of our deep learning solution, which achieved an average endpoint error of 0.07 ± 0.06 mm per frame and 1.20 ± 0.67 mm between ED and ES on our simulated dataset. The performance of our method was further investigated on 30 patients from a publicly available clinical dataset acquired from a GE system. The method showed promise by achieving a mean absolute error of the global longitudinal strain of 2.5 ± 2.1% and a correlation of 0.77 compared to GLS derived from manual segmentation, much better than one of the most efficient methods in the state-of-the-art (namely the FFT-Xcorr block-matching method). We finally evaluated our method on an auxiliary dataset including 30 patients from another center and acquired with a different system. Comparable results were achieved, illustrating the ability of our method to maintain high performance regardless of the echocardiographic data processed.

Exercise Capacity in Young Adults Born Small for Gestational Age.

Importance: Being born small for gestational age (SGA), approximately 10% of all births, is associated with increased risk of cardiovascular mortality in adulthood, but mechanistic pathways are unclear. Cardiac remodeling and dysfunction occur in fetuses SGA and children born SGA, but it is uncertain whether and how these changes persist into adulthood. Objective: To evaluate baseline cardiac function and structure and exercise capacity in young adults born SGA. Design, Setting, and Participants: This cohort study conducted from January 2015 to January 2018 assessed a perinatal cohort born at a tertiary university hospital in Spain between 1975 and 1995. Participants included 158 randomly selected young adults aged 20 to 40 years born SGA (birth weight below the 10th centile) or with intrauterine growth within standard reference ranges (controls). Participants provided their medical history, filled out questionnaires regarding smoking and physical activity habits, and underwent incremental cardiopulmonary exercise stress testing, cardiac magnetic resonance imaging, and a physical examination, with blood pressure, glucose level, and lipid profile data collected. Exposure: Being born SGA. Main Outcomes and Measures: Cardiac structure and function assessed by cardiac magnetic resonance imaging, including biventricular end-diastolic shape analysis. Exercise capacity assessed by incremental exercise stress testing. Results: This cohort study included 81 adults born SGA (median age at study, 34.4 years [IQR, 30.8-36.7 years]; 43 women [53%]) and 77 control participants (median age at study, 33.7 years [interquartile range (IQR), 31.0-37.1 years]; 33 women [43%]). All participants were of White race/ethnicity and underwent imaging, whereas 127 participants (80% of the cohort; 66 control participants and 61 adults born SGA) completed the exercise test. Cardiac shape analysis showed minor changes at rest in right ventricular geometry (DeLong test z, 2.2098; P = .02) with preserved cardiac function in individuals born SGA. However, compared with controls, adults born SGA had lower exercise capacity, with decreased maximal workload (mean [SD], 180 [62] W vs 214 [60] W; P = .006) and oxygen consumption (median, 26.0 mL/min/kg [IQR, 21.5-33.5 mL/min/kg vs 29.5 mL/min/kg [IQR, 24.0-36.0 mL/min/kg]; P = .02). Exercise capacity was significantly correlated with left ventricular mass (ρ = 0.7934; P < .001). Conclusions and Relevance: This cohort of young adults born SGA had markedly reduced exercise capacity. These results support further research to clarify the causes of impaired exercise capacity and the potential association with increased cardiovascular mortality among adults born SGA.

Volumetric parcellation of the cardiac right ventricle for regional geometric and functional assessment.

3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks, especially within the ventricular cavity. We developed a technique for regionally assessing the volume of 3 relevant RV volumetric regions: apical, inlet and outflow. The proposed parcellation method is based on the geodesic distances to anatomical landmarks that are easily identifiable in the images: the apex and the tricuspid and pulmonary valves, each associated to a region. Based on these distances, we define a partition in the endocardium at end-diastole (ED). This partition is then interpolated to the blood cavity using the Laplace equation, which allows to compute regional volumes. For obtaining an end-systole (ES) partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking software, and then the interpolation process is repeated. We assessed the intra- and inter-observer reproducibility using a 10-subjects dataset containing repeated quantifications of the same images, obtaining intra- and inter- observer errors (7-12% and 10-23% respectively). Finally, we propose a novel synthetic mesh generation algorithm that deforms a template mesh imposing a user-defined strain to a template mesh. We used this method to create a new dataset for involving distinct types of remodelling that were used to assess the sensitivity of the parcellation method to identify volume changes affecting different parts. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodelling, which are the most clinically relevant cases.

Handling confounding variables in statistical shape analysis - application to cardiac remodelling.

Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.

A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images.

In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ultrasound images. To date, most of this research has focused on segmentation and view recognition. This article benchmarks different convolutional neural network algorithms for motion estimation in ultrasound imaging. We evaluated and compared several networks derived from FlowNet2, one of the most efficient architectures in computer vision. The networks were tested with and without transfer learning, and the best configuration was compared against the particle imaging velocimetry method, a popular state-of-the-art block-matching algorithm. Rotations are known to be difficult to track from ultrasound images due to a significant speckle decorrelation. We thus focused on the images of rotating disks, which could be tracked through speckle features only. Our database consisted of synthetic and in vitro B-mode images after log compression and covered a large range of rotational speeds. One of the FlowNet2 subnetworks, FlowNet2SD, produced competitive results with a motion field error smaller than 1 pixel on real data after transfer learning based on the simulated data. These errors remain small for a large velocity range without the need for hyperparameter tuning, which indicates the high potential and adaptability of DL solutions to motion estimation in ultrasound imaging.

Analysis of nonstandardized stress echocardiography sequences using multiview dimensionality reduction.

Alternative stress echocardiography protocols such as handgrip exercise are potentially more favorable towards large-scale screening scenarios than those currently adopted in clinical practice. However, these are still underexplored because the maximal exercise levels are not easily quantified and regulated, requiring the analysis of the complete data sequences (thousands of images), which represents a challenging task for the clinician. We propose a framework for the analysis of these complex datasets, and illustrate it on a handgrip exercise dataset including complete acquisitions of 10 healthy controls and 5 ANT1 mutation patients (1377 cardiac cycles). The framework is based on an unsupervised formulation of multiple kernel learning, which is used to integrate information coming from myocardial velocity traces and heart rate to obtain a lower-dimensional representation of the data. Such simplified representation is then explored to discriminate groups of response and understand the underlying pathophysiological mechanisms. The analysis pipeline involves the reconstruction of population-specific signatures using multiscale kernel regression, and the clustering of subjects based on the trajectories defined by their projected sequences. The results confirm that the proposed framework is able to detect distinctive clusters of response and to provide insight regarding the underlying pathophysiology.

Breaking the state of the heart: meshless model for cardiac mechanics.

Cardiac modeling has recently emerged as a promising tool to study pathophysiology mechanisms and to predict treatment outcomes for personalized clinical decision support. Nevertheless, achieving convergence under large deformation and defining a robust meshing for realistic heart geometries remain challenging, especially when maintaining the computational cost reasonable. Smoothed particle hydrodynamics (SPH) appears to be a promising alternative to the finite element method (FEM) since it removes the burden of mesh generation. A point cloud is used where each point (particle) contains all the physical properties that are updated throughout the simulation. SPH was evaluated for solid mechanics applications in the last decade but its capacity to address the challenge of simulating the mechanics of the heart has never been evaluated. In this paper, a total Lagrangian formulation of a corrected SPH was used to solve three solid mechanics problems designed to test important features that a cardiac mechanics solver should have. SPH results, in terms of ventricle displacements and strains, were compared to results obtained with 11 different FEM-based solvers, by using synthetic cardiac data from a benchmark study. In particular, passive dilation and active contraction were simulated in an ellipsoidal left ventricle with the exponential anisotropic constitutive law of Guccione following the direction of fibers. The proposed meshless method is able to reproduce the results of three benchmark problems for cardiac mechanics. Hyperelastic material with fiber orientation and high Poisson ratio allows wall thickening/thinning when large deformation is present.

Regional Multi-View Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients.

OBJECTIVE: The aim of this paper is to describe an automated diagnostic pipeline that uses as input only ultrasound (US) data, but is at the same time informed by a training database of multimodal magnetic resonance (MR) and US image data. METHODS: We create a multimodal cardiac motion atlas from three-dimensional (3-D) MR and 3-D US data followed by multi-view machine learning algorithms to combine and extract the most meaningful cardiac descriptors for classification of dilated cardiomyopathy (DCM) patients using US data only. More specifically, we propose two algorithms based on multi-view linear discriminant analysis and multi-view Laplacian support vector machines (MvLapSVMs). Furthermore, a novel regional multi-view approach is proposed to exploit the regional relationships between the two modalities. RESULTS: We evaluate our pipeline on the classification task of discriminating between normals and DCM patients. Results show that the use of multi-view classifiers together with a cardiac motion atlas results in a statistically significant improvement in accuracy compared to classification without the multimodal atlas. MvLapSVM was able to achieve the highest accuracy for both the global approach (92.71%) and the regional approach (94.32%). CONCLUSION: Our work represents an important contribution to the understanding of cardiac motion, which is an important aid in the quantification of the contractility and function of the left ventricular myocardium. SIGNIFICANCE: The intended workflow of the developed pipeline is to make use of the prior knowledge from the multimodal atlas to enable robust extraction of indicators from 3-D US images for detecting DCM patients.

Machine Learning Approaches for Myocardial Motion and Deformation Analysis.

Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.

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.

Realistic Vendor-Specific Synthetic Ultrasound Data for Quality Assurance of 2-D Speckle Tracking Echocardiography: Simulation Pipeline and Open Access Database.

Two-dimensional (2-D) echocardiography is the modality of choice in the clinic for the diagnosis of cardiac disease. Hereto, speckle tracking (ST) packages complement visual assessment by the cardiologist by providing quantitative diagnostic markers of global and regional cardiac function (e.g., displacement, strain, and strain-rate). Yet, the reported high vendor-dependence between the outputs of different ST packages raises clinical concern and hampers the widespread dissemination of the ST technology. In part, this is due to the lack of a solid commonly accepted quality assurance pipeline for ST packages. Recently, we have developed a framework to benchmark ST algorithms for 3-D echocardiography by using realistic simulated volumetric echocardiographic recordings. Yet, 3-D echocardiography remains an emerging technology, whereas the compelling clinical concern is, so far, directed to the standardization of 2-D ST only. Therefore, by building upon our previous work, we present in this paper a pipeline to generate realistic synthetic sequences for 2-D ST algorithms. Hereto, the synthetic cardiac motion is obtained from a complex electromechanical heart model, whereas realistic vendor-specific texture is obtained by sampling a real clinical ultrasound recording. By modifying the parameters in our pipeline, we generated an open-access library of 105 synthetic sequences encompassing: 1) healthy and ischemic motion patterns; 2) the most common apical probe orientations; and 3) vendor-specific image quality from seven different systems. Ground truth deformation is also provided to allow performance analysis. The application of the provided data set is also demonstrated in the benchmarking of a recent academic ST algorithm.

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.

Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings.

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

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.

Multiview diffeomorphic registration: application to motion and strain estimation from 3D echocardiography.

This paper presents a new registration framework for quantifying myocardial motion and strain from the combination of multiple 3D ultrasound (US) sequences. The originality of our approach lies in the estimation of the transformation directly from the input multiple views rather than from a single view or a reconstructed compounded sequence. This allows us to exploit all spatiotemporal information available in the input views avoiding occlusions and image fusion errors that could lead to some inconsistencies in the motion quantification result. We propose a multiview diffeomorphic registration strategy that enforces smoothness and consistency in the spatiotemporal domain by modeling the 4D velocity field continuously in space and time. This 4D continuous representation considers 3D US sequences as a whole, therefore allowing to robustly cope with variations in heart rate resulting in different number of images acquired per cardiac cycle for different views. This contributes to the robustness gained by solving for a single transformation from all input sequences. The similarity metric takes into account the physics of US images and uses a weighting scheme to balance the contribution of the different views. It includes a comparison both between consecutive images and between a reference and each of the following images. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement fields. Registration and strain accuracy were evaluated on synthetic 3D US sequences with known ground truth. Experiments were also conducted on multiview 3D datasets of 8 volunteers and 1 patient treated by cardiac resynchronization therapy. Strain curves obtained from our multiview approach were compared to the single-view case, as well as with other multiview approaches. For healthy cases, the inclusion of several views improved the consistency of the strain curves and reduced the number of segments where a non-physiological strain pattern was observed. For the patient, the improvement (pacing ON vs. OFF) in synchrony of regional strain correlated with clinician blind assessment and could be seen more clearly when using the multiview approach.

Myocardial motion estimation combining tissue doppler and B-mode echocardiographic images.

We present a registration framework that combines both tissue Doppler and B-mode echocardiographic sequences. The estimated spatiotemporal transform is diffeomorphic, and calculated by modeling its corresponding velocity field using continuous B-splines. A new cost function using both B-mode image voxel intensities and Doppler velocities is also proposed. Registration accuracy was evaluated on synthetic data with known ground truth. Results showed that our method allows quantifying wall motion with higher accuracy than when using a single modality. On patient data, both displacement and velocity curves were compared with the ones obtained from widely used commercial software using either B-mode images or TDI. Our method demonstrated to be more robust to image noise while being independent from the beam angle.

A high-resolution atlas and statistical model of the human heart from multislice CT.

Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously.