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A coupled computational method for recovering tissue velocity vector fields from high-frame-rate echocardiography is described. Conventional transthoracic echocardiography provides limited temporal resolution, which may prevent accurate estimation of the 2-D myocardial velocity field dynamics. High-frame-rate compound echocardiography using diverging waves with integrated motion compensation has been shown to provide concurrent high-resolution B-mode and tissue Doppler imaging (TDI). In this paper, we propose a regularized least-squares method to provide accurate myocardial velocities at high frame rates. The velocity vector field was formulated as the minimizer of a cost function that is a weighted sum of: 1) the ${\ell }^{{2}}$ -norm of the material derivative of the B-mode images (optical flow); 2) the ${\ell }^{{2}}$ -norm of the tissue-Doppler residuals; and 3) a quadratic regularizer that imposes spatial smoothness and well-posedness. A finite difference discretization of the continuous problem was adopted, leading to a sparse linear system. The proposed framework was validated in vitro on a rotating disk with speeds up to 20 cm/s, and compared with speckle tracking echocardiography (STE) by block matching. It was also validated in vivo against TDI and STE in a cross-validation strategy involving parasternal long axis and apical three-chamber views. The proposed method based on the combination of optical flow and tissue Doppler led to more accurate time-resolved velocity vector fields.
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http://dx.doi.org/10.1109/TMI.2018.2811483 | DOI Listing |
NPJ Biomed Innov
September 2025
Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA USA.
Glioblastoma is characterized by aggressive infiltration into surrounding brain tissue, hindering complete surgical resection and contributing to poor patient outcomes. Identifying tumor-specific invasion patterns is essential for advancing our understanding of glioblastoma progression and improving surgical and radiotherapeutic strategies. Here, we leverage in vivo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively quantify interstitial fluid velocity, direction, and diffusion within and around glioblastomas.
View Article and Find Full Text PDFTraffic Inj Prev
September 2025
Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia.
Objective: Multiple studies have demonstrated an increased risk of lower extremity injuries for females in frontal crashes. This study aimed to investigate whether sex-based anatomical differences, as measured on computed tomography (CT) scans of the abdomen and pelvis, contribute to lower extremity injury risk.
Methods: The Crash Injury Research and Engineering Network (CIREN) database (2017-2023) was queried for frontal collisions.
Sci Rep
September 2025
Department of Earth and Planetary Sciences, ETH Zürich, Zürich, 8092, Switzerland.
The occurrence of tectonic plate reorganization events is evident throughout the geologic record and appears to be associated with the cessation of mature and/or initiation of new subduction. Subduction initiation that produced the bend in the Hawaii-Emperor seamount chain resulted in the most recent upheaval of plate motion and engendered dramatic changes in plate velocities. Here, applying a method for identifying plate boundaries in a numerical global mantle convection model, we calculate Euler vector time series of self-consistently generated plates over a period of approximately 144 Myr.
View Article and Find Full Text PDFProc IEEE Int Symp Appl Ferroelectr
September 2024
Department of Biomedical Engineering, New York City, USA.
Arterial stiffness is a key predictor of cardiovascular mortality. This study utilizes ultrasound-based Pulse Wave Imaging (PWI) and Vector Flow Imaging (VFI) to track vessel wall displacement caused by arterial pulse wave propagation and blood flow velocity at a high frame rate (3.3 kHz) to estimate localized arterial wall stiffness through an Inverse problem setting.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Department of Ultrasonography, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang, China.
Background: Non-Valvular Atrial fibrillation (NVAF) and atrial flutter are significant contributors to left atrial appendage thrombus (LAAT) formation. This study explores the potential of machine learning (ML) models integrating transthoracic echocardiography (TTE) and clinical data for non-invasive LAAT detection and risk assessment.
Methods: A total of 698 patients with NVAF was recruited from Luoyang Central Hospital between January 2021 and May 2024, including 558 patients for retrospective analysis and 140 for prospective validation.