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Related Concept Videos

Blood Flow01:29

Blood Flow

Blood is pumped by the heart into the aorta, the largest artery in the body, and then into increasingly smaller arteries, arterioles, and capillaries. The velocity of blood flow decreases with increased cross-sectional blood vessel area. As blood returns to the heart through venules and veins, its velocity increases. The movement of blood is encouraged by smooth muscle in the vessel walls, the movement of skeletal muscle surrounding the vessels, and one-way valves that prevent backflow.
Applications of Integration to Find Blood Flow01:27

Applications of Integration to Find Blood Flow

Blood flow through a cylindrical blood vessel can be mathematically described using the principles of laminar flow, a regime in which fluid moves smoothly in parallel layers. In this model, the velocity of the blood is not uniform across the cross-section of the vessel; rather, it varies with the radial distance from the center. The maximum velocity occurs along the central axis, decreasing progressively toward the vessel walls, where it reaches zero due to viscous drag.Approximating Blood...
Development of Blood Vessels01:07

Development of Blood Vessels

The development of the vascular system in a fetus is a complex and intricate process that begins as early as 15 to 16 days post-conception. This process starts outside the embryo, specifically in the mesoderm of the yolk sac, chorion, and connecting stalk. Approximately two days later, the formation of blood vessels occurs within the embryo itself.
The initial formation of this system is facilitated by the small amount of yolk present in the ovum and yolk sac. Blood vessels originate from...

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Related Experiment Video

Updated: Jun 4, 2026

Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Real-time blood flow visualization using the graphics processing unit.

Owen Yang1, David Cuccia, Bernard Choi

  • 1University of California, Irvine, Beckman Laser Institute and Medical Clinic, Department of Biomedical Engineering, 1002 Health Sciences Road, Irvine, California 92612, USA. yango@uci.edu

Journal of Biomedical Optics
|February 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for laser speckle imaging (LSI) using graphics processing units (GPUs) for faster blood flow analysis. The developed system achieves real-time speckle flow index (SFI) imaging at approximately 10 frames per second.

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Last Updated: Jun 4, 2026

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05:57

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Published on: October 14, 2020

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09:39

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Published on: November 18, 2019

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows
07:53

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Published on: April 25, 2013

Area of Science:

  • Biomedical optics
  • Medical imaging
  • Computational imaging

Background:

  • Laser speckle imaging (LSI) analyzes movement of optical scatterers to assess blood flow.
  • Current LSI processing can be computationally intensive, limiting real-time applications.
  • Speckle flow index (SFI) maps provide relative blood flow quantification.

Purpose of the Study:

  • To develop and implement a novel, high-speed algorithm for laser speckle image processing.
  • To leverage the NVIDIA Compute Unified Device Architecture (CUDA) for graphics processing unit (GPU) acceleration.
  • To achieve real-time processing and display of speckle flow index (SFI) maps.

Main Methods:

  • Developed a C-based algorithm integrated with CUDA for GPU acceleration.
  • Integrated the CUDA-accelerated algorithm into a LabVIEW Virtual Instrument (VI).
  • Interfaced the VI with a monochrome CCD camera for high-resolution image acquisition at ~10 fps.

Main Results:

  • Achieved real-time processing and display of SFI images at approximately 10 frames per second.
  • Demonstrated the system's capability in real-time flow imaging during various scenarios.
  • Successfully imaged blood flow during a reactive hyperemia maneuver, through an in vitro phantom, and during laser surgery.

Conclusions:

  • The CUDA-accelerated LSI algorithm significantly enhances processing speed for real-time blood flow imaging.
  • The integrated LabVIEW system provides a robust platform for high-frame-rate SFI map generation.
  • This advancement enables practical, real-time LSI applications in clinical and research settings.