Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound

Automated cardiac vortex ring identification and characterization based on Recurrent All-Pairs Field Transforms and Lagrangian Averaged Vorticity Deviation

Automated identification of cardiac vortices is a formidable task due to the complex nature of blood flow within the heart chambers. This study proposes a novel approach that algorithmically characterizes the identification criteria of these cardiac vortices based on Lagrangian Averaged Vorticity Deviation (LAVD). For this purpose, the Recurrent All-Pairs Field Transforms (RAFT) is employed to assess the optical flow over the Phase Contrast Magnetic Resonance Imaging (PC-MRI), and to construct a continuous blood flow velocity field and reduce errors that arise from the integral process of LAVD. Additionally, Generalized Hough Transform (GHT) is applied for automated depiction of the structure of cardiac vortices. The effectiveness of this method is demonstrated and validated by the computation of the acquired cardiac flow data. The results of this comprehensive visual and analytical study show that the evolution of cardiac vortices can be effectively described and displayed, and the RAFT framework for optical flow can synthesize the in-between PC-MRIs with high accuracy. This allows cardiologists to acquire a deeper understanding of intracardiac hemodynamics and its impact on cardiac functional performance.

Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry

Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity field information of a blood flows by localizing and tracking speckle signals of red blood cells (RBCs) without using any contrast agents. However, DL-SRU can localize only a small part of the speckle signals of blood flow owing to ambiguity problems encountered in the classification of blood flow signals from ultrasound B-mode images and the building up of suitable datasets required for training artificial neural networks, as well as the structural limitations of the neural network itself. An artificial intelligence-based speckle featurization and localization (AI-SFL) framework is proposed in this study. It includes a machine learning-based algorithm for classifying blood flow signals from ultrasound B-mode images, dimensionality reduction for featurizing speckle patterns of the classified blood flow signals by approximating them with quantitative values. A novel and robust neural network (ResSU-net) is trained using the online data generation (ODG) method and the extracted speckle features. The super-resolution performance of the proposed AI-SFL and ODG method is evaluated and compared with the results of previous U-net and conventional data augmentation methods under in silico conditions. The predicted locations of RBCs by the AI-SFL and DL-SRU for speckle patterns of blood flow are applied to a PTV algorithm to measure quantitative velocity fields of the flow. Finally, the feasibility of the proposed AI-SFL framework for measuring real blood flows is verified under in vivo conditions.

Vortex Formation Time is a Novel Measure for Early Detection of Diastolic Abnormalities in Adolescents with Hypertension

Pediatric hypertension (HTN) has demonstrated an upward trend in recent years. Adolescent HTN has been linked to adult HTN, cardiovascular disease, and other health conditions. Thus, it is essential that HTN and its associated cardiac abnormalities be diagnosed and treated early to minimize lifelong adverse effects. In this study, we evaluated whether vortex formation time (VFT), a validated echocardiogram measure of left ventricular diastolic dysfunction, correlated with ambulatory blood pressure monitoring (ABPM) and HTN in adolescents. Echocardiogram data including systolic and diastolic function indices and ABPM data from 2015 to 2022 in adolescents age 13-21 years were analyzed retrospectively. We found that VFT was significantly lower in adolescents with HTN compared to those without HTN (3.69 ± 1.39 vs. 4.50 ± 1.73, p = 0.02). Standard echocardiographic indices of systolic and diastolic function were similar between the two groups, except indexed left atrial volume. Higher overall systolic blood pressure (SBP) (β = - 0.01, CI - 0.02, - 2.2 × 10^-3, p = 0.02), mean wake SBP (β = - 0.01, CI - 0.02, - 9.4 × 10^-4, p = 0.03), and mean sleep SBP (β = - 0.01, CI - 0.02, - 1.2 × 10^-3, p = 0.03) were significantly associated with lower VFT. This study demonstrates that VFT correlates to ABPM data and can be used a novel diagnostic measure in adolescents with HTN.

Full-volume three-component intraventricular vector flow mapping by triplane color Doppler

Objective. Intraventricular vector flow mapping (iVFM) is a velocimetric technique for retrieving two-dimensional velocity vector fields of blood flow in the left ventricular cavity. This method is based on conventional color Doppler imaging, which makes iVFM compatible with the clinical setting. We have generalized the iVFM for a three-dimensional reconstruction (3D-iVFM). Approach. 3D-iVFM is able to recover three-component velocity vector fields in a full intraventricular volume by using a clinical echocardiographic triplane mode. The 3D-iVFM problem was written in the spherical (radial, polar, azimuthal) coordinate system associated to the six half-planes produced by the triplane mode. As with the 2D version, the method is based on the mass conservation, and free-slip boundary conditions on the endocardial wall. These mechanical constraints were imposed in a least-squares minimization problem that was solved through the method of Lagrange multipliers. We validated 3D-iVFM in silico in a patient-specific CFD (computational fluid dynamics) model of cardiac flow and tested its clinical feasibility in vivo in patients and in one volunteer. Main results. The radial and polar components of the velocity were recovered satisfactorily in the CFD setup (correlation coefficients, r  = 0.99 and 0.78). The azimuthal components were estimated with larger errors ( r  = 0.57) as only six samples were available in this direction. In both in silico and in vivo investigations, the dynamics of the intraventricular vortex that forms during diastole was deciphered by 3D-iVFM. In particular, the CFD results showed that the mean vorticity can be estimated accurately by 3D-iVFM. Significance. Our results tend to indicate that 3D-iVFM could provide full-volume echocardiographic information on left intraventricular hemodynamics from the clinical modality of triplane color Doppler.

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion

BACKGROUND AND OBJECTIVES

Dynamic Magnetic Resonance Imaging (MRI) may capture temporal anatomical changes in soft tissue organs with high-contrast but the obtained sequences usually suffer from limited volume coverage which makes the high-resolution reconstruction of organ shape trajectories a major challenge in temporal studies. Because of the variability of abdominal organ shapes across time and subjects, the objective of the present study is to go towards 3D dense velocity measurements to fully cover the entire surface and to extract meaningful features characterizing the observed organ deformations and enabling clinical action or decision.

METHODS

We present a pipeline for characterization of bladder surface dynamics during deep respiratory movements. For a compact shape representation, the reconstructed temporal volumes were first used to establish subject-specific dynamical 4D mesh sequences using the large deformation diffeomorphic metric mapping (LDDMM) framework. Then, we performed a statistical characterization of organ dynamics from mechanical parameters such as mesh elongations and distortions. Since we refer to organs as non-flat surfaces, we have also used the mean curvature change as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization (i.e. the mesh resolution). To cope with this dependency, we employed a non-parametric method for surface deformation analysis. Independent of parameterization and minimizing the length of the geodesic curves, it stretches smoothly the surface curves towards a sphere by minimizing a Dirichlet energy. An Eulerian PDE approach is used to derive a shape descriptor from the curve-shortening flow. Intercorrelations between individuals' motion patterns are computed using the Laplace-Beltrami Operator (LBO) eigenfunctions for spherical mapping.

RESULTS

Application to extracting characterization correlation curves for locally-controlled simulated shape trajectories demonstrates the stability of the proposed shape descriptor. Its usability was shown on MRI acquired for seven healthy participants for which the bladder was highly deformed by maximum of inspiration. As expected, the study showed that deformations occured essentially on the top lateral regions.

CONCLUSION

Promising results were obtained, showing the organ in its 3D complexity during deformation due to strain conditions. Smooth genus-0 manifold reconstruction from sparse dynamic MRI data is employed to perform a statistical shape analysis for the determination of bladder deformation.