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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

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The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
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Autoregulation of Blood Flow01:17

Autoregulation of Blood Flow

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Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation....
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

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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...
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Plane Potential Flows01:23

Plane Potential Flows

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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
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Applications of Integration to Find Blood Flow01:27

Applications of Integration to Find Blood Flow

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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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Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
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Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

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物理指导的神经网络用于静脉向量流量映射.

Hang Jung Ling, Salome Bru, Julia Puig

    IEEE transactions on ultrasonics, ferroelectrics, and frequency control
    |June 10, 2024
    PubMed
    概括

    使用物理信息神经网络和nnU-Net的新方法增强了用于心脏成像的静脉内向量流映射 (iVFM). 这些方法的性能与传统的iVFM相提并论,nnU-Net显示出优越的稳定性和实时能力.

    科学领域:

    • 心血管成像 - 心血管成像
    • 计算流体动力学的流体动力学.
    • 人工智能在医学中的应用

    背景情况:

    • 静脉内载体流量映射 (iVFM) 对于使用彩色多普勒来量化心脏血液流量至关重要.
    • 传统的iVFM优化方法在性能和通用性方面存在局限性.

    研究的目的:

    • 开发和评估传统iVFM优化方案的新型替代方案.
    • 利用物理信息的神经网络 (PINNs) 和以物理为导向的nnU-Net方法来改进 iVFM.

    主要方法:

    • 使用双阶段优化和预先优化的权重实现PINNs.
    • 开发一个以物理为导向的nnU-Net监督学习模型.
    • 从患者特定的CFD模型和体内多普勒采集中对模拟的多普勒图像进行评估.

    主要成果:

    • 无论是PINNs还是nnU-Net,都表现出与原始iVFM算法相比的可比重建性能.
    • nnU-Net在稀疏/截断的多普勒数据上表现出卓越的概括性,实时能力和稳定性.
    • 通过优化培训策略,PINNs显示提高了效率.

    结论:

    • PINNs和nnU-Net是重建心室内载体血流的有效替代方案.

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  • nnU-Net在心脏成像方面的稳定性和实时应用方面具有优势.
  • 在超快速多普勒成像和从血液流量数据中推导心血管疾病生物标志物的PINNs潜力.