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相关概念视频

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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Fast Fourier Transform01:10

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
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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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The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
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相关实验视频

Updated: Jul 17, 2025

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
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通过Multi-Scale Fourier变压器进行频率学习,用于MRI重建.

Qiaosi Yi, Faming Fang, Guixu Zhang

    IEEE journal of biomedical and health informatics
    |September 1, 2023
    PubMed
    概括

    这项研究介绍了FMTNet,这是一种用于更快的磁共振成像 (MRI) 重建的新方法. FMTNet有效地修复图像频率信息和非局部相似性,显著提高加速MRI扫描中的结构清晰度.

    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:

    • 磁共振成像 (MRI) 的获取是耗时的.
    • 现有的加速方法往往忽略了关键的频率和非局部信息,导致图像结构差.
    • 需要先进的重建技术,在加速核磁共振时保持图像质量.

    研究的目的:

    • 提出一个新的深度学习框架,FMTNet,用于加速MRI重建.
    • 专注于修复低频和高频信息,以提高图像清晰度.
    • 开发一个高效的变压器模块,能够学习全球和多层次信息.

    主要方法:

    • 通过Multi-scale Fourier变压器进行MRI重建 (FMTNet) 框架进行频率学习.
    • 双分支架构:高频学习分支 (HFLB) 和低频学习分支 (LFLB).
    • 多尺度里叶变压器 (MFT) 模块利用里叶卷积来实现高效的全球信息学习和跨尺度融合.

    主要成果:

    • 与MRI重建中最先进的方法相比,FMTNet表现出卓越的性能.
    • 在各种加速率和采样模式下进行的实验验证实了该方法的有效性.
    • 拟议的MFT模块有效地学习非本地信息,并减少计算资源.

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    结论:

    • 通过有效地修复频率和非局部信息,FMTNet成功地通过清晰的结构重建MRI图像.
    • 多尺度里埃变压器 (MFT) 为学习全球图像特征提供了标准自我注意的有效替代方案.
    • 拟议的方法代表了加速MRI重建的重大进步,提高了速度和图像质量.