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相关概念视频

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.
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this principle...
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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...

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相关实验视频

Updated: Jun 7, 2026

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

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在扩散相关谱中量化血液流量指数,使用强大的深度学习方法.

Quan Wang1, Mingliang Pan1, Zhenya Zang1

  • 1University of Strathclyde, Department of Biomedical Engineering, Faculty of Engineering, Glasgow, United Kingdom.

Journal of biomedical optics
|January 29, 2024
PubMed
概括
此摘要是机器生成的。

一种新的深度学习方法,DCS-NET,快速而可靠地分析分散相关谱 (DCS) 数据以估计血流指数 (BFi). 与传统的装配方法相比,这种AI方法显著提高了准确性和速度.

关键词:
血液的流动,血液的流动.深度学习是一种深度学习.扩散相关性光谱学扩散相关性光谱学

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相关实验视频

Last Updated: Jun 7, 2026

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
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科学领域:

  • 生物医学光学 生物医学光学
  • 医疗成像医学成像
  • 光学光谱学是指光学光谱学.

背景情况:

  • 扩散相关谱学 (DCS) 是一种用于测量血液流量的非侵入性光学技术.
  • 计算血流指数 (BFi) 的传统方法依赖于自相关函数 (ACF) 的计算密集的非线性最小平方拟合.
  • 这些传统的装配方法容易受到噪音和光学特性和组织层厚度的变化的影响.

研究的目的:

  • 开发一个数据驱动的深度学习方法,用于快速和可靠地分析DCS时间ACF.
  • 为了使BFi估计在源-探测器距离的范围,克服固定距离方法的局限性.
  • 在各种生理条件下,将深度学习方法的性能与传统的拟合模型进行比较.

主要方法:

  • 一个深度学习架构,DCS神经网络 (DCS-NET),利用1D卷积神经网络被开发用于BFi和连贯因子估计.
  • 基于三层大脑模型的模拟DCS数据被用于训练和验证DCS-NET.
  • 该研究量化了光学特性,层厚度,噪声和源探测器距离对DCS-NET和传统安装模型的影响.

主要成果:

  • DCS-NET显示了显著更快的分析速度 (分别比三层和半无限模型快17000倍和32倍).
  • 深度学习方法显示了对深层组织的提高敏感性和出色的抗噪声能力.
  • 使用DCS-NET的相对BFi (rBFi) 提取与传统方法相比 (43.76%和19.66%) 的误差很低 (8.35%).

结论:

  • DCS-NET在扩展的源探测器距离上稳定量化了血液流量测量,从而实现了更深入的组织分析.
  • 该方法显示了硬件实现的潜力,促进了持续的实时血流监测.
  • 深度学习为克服传统DCS分析的局限性提供了一个有希望的途径.