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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
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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相关实验视频

Updated: May 24, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

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无监督动态MRI重建的时空空间隐含神经表示.

Jie Feng, Ruimin Feng, Qing Wu

    IEEE transactions on medical imaging
    |March 3, 2025
    PubMed
    概括

    隐式神经表示 (INR) 为动态MRI提供无监督,数据效率高的重建. 这种新的方法可以提高图像质量和时空分辨率,而不需要外部训练数据.

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    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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    Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
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    Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

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    相关实验视频

    Last Updated: May 24, 2025

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

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    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

    Published on: May 10, 2012

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    Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
    05:07

    Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

    Published on: September 6, 2024

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    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 监督深度学习 (DL) 在动态MRI重建方面表现出色,但需要广泛的基准真实数据,限制了概括.
    • 隐式神经表示 (INR) 提供了一个无监督的方法来将信号模型为连续函数,解决数据限制.

    研究的目的:

    • 开发一种基于INR的方法,以从高度低采样的k空间数据中改进动态MRI重建.
    • 消除对外部培训数据集的需求或在动态MRI重建中转移学习.

    主要方法:

    • 提出了一种基于INR的方法,将动态MRI编码为隐性神经网络功能.
    • 从稀疏获取的 (k,t) 空间数据直接学习网络权重.
    • 集成INR的隐含连续性与明确的低级和稀疏性规范化.

    主要成果:

    • 在跨越各种加速度因子的动态MRI重建中实现了最先进的性能.
    • 在高加速度 (高达40.8x) 的心脏影视数据集上显示出显著的改进 (0.6-2.0 dB PSNR).
    • 该方法不需要外部训练数据,克服了概括问题.

    结论:

    • 提出的基于INR的方法有效地从低样本数据中重建动态MRI.
    • INR固有的连续性和规则化能力提高了图像质量,并有可能提高时空分辨率.
    • 这种无监督的方法为动态MRI中的监督DL方法提供了一个有希望的替代方案.