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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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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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预测未来的解剖学:纵向大脑Mri-to-Mri预测

Ali Farki, Elaheh Moradi, Deepika Koundal

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    此摘要是机器生成的。

    深度学习模型现在可以高准确地预测未来的大脑MRI,有助于早期检测和个性化预后的神经退行性疾病,如阿尔茨海默病.

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    科学领域:

    • 神经成像是一种神经成像.
    • 人工智能的人工智能
    • 医学诊断 医学诊断 医学诊断

    背景情况:

    • 从MRI预测未来的大脑状态对于理解神经退行性疾病,如阿尔茨海默病 (AD) 至关重要.
    • 当前的方法往往侧重于认知得分,而不是直接预测图像.
    • 纵向MRI分析是建模疾病进展的关键.

    研究的目的:

    • 研究用于纵向MRI图像对图像预测的深度学习.
    • 在未来几年内预测整个大脑的MRI.
    • 为了建模复杂的,空间分布的神经退行模式.

    主要方法:

    • 实施和评估了五个深度学习架构:UNet,U2-Net,UNETR,时间嵌入UNet和ODE-UNet.
    • 利用两个纵向队列:阿尔茨海默病神经成像计划 (ADNI) 和澳大利亚成像生物标志物和生活方式 (AIBL).
    • 将预测的随访MRI与使用全球相似性和局部差异指标的实际扫描进行了比较.

    主要成果:

    • 性能最好的深度学习模型实现了高准确度的MRI预测.
    • 所有评估的模型都显示了对独立数据集的强有力的跨队列概括.
    • 深度学习可靠地预测了参与者特定的大脑MRI在voxel水平.

    结论:

    • 深度学习能够准确地预测未来大脑MRI的声音水平.
    • 这种方法为神经退行性疾病的个性化预后提供了一种新的方法.
    • 未来的研究可以利用这些技术来加强疾病监测和治疗策略.