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Objective: The current vector velocity measurement techniques are typically limited by probe apertures, which restrict their use to superficial vessels. This work introduces a high frame rate method based on the use of multiple array probes transmitting defocused beams and shows that such a method permits accurate and precise measurements of blood velocities in deep and large regions/volumes of interest.
Methods: Multiple probes are positioned to investigate a common region of interest and activated in a sequence. The corresponding phase shifts are estimated and combined through a least square error approach to derive the velocity vector. The method's performance has been quantitatively evaluated in both 2-D and 3-D scenarios through simulations based on either two linear- or three matrix sparsearray probes.
Results: Good estimations of both module (average rmse(v) 11.9% of vmax) and direction (average rmse(α) 2.3° and rmse(β) 8.0°) of the velocity vectors have been obtained. The corresponding vector velocity frames cover wide areas or volumes even around depths as high as 80 mm.
Conclusions: This simulation work demonstrates that multi-probe configurations can be exploited to measure the 2-D or 3-D flow velocity accurately and precisely in deep and large regions of interest.
Significance: In a clinical scenario, for example, the method could be exploited for velocity estimation in the abdominal region where large and deep vessels, such as the aorta, are located.
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http://dx.doi.org/10.1109/TBME.2025.3583943 | 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.