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相关概念视频

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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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...
Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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MPM-DCE:早期和晚期DCE前列腺MRI合成的多阶段渐进机制

Siva Singireddi, Sriprabha R, Kishore Kumar M

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    概括

    MPM-DCE是一种新的多阶段框架,通过合成动态对比增强MRI (DCE-MRI) 图像来增强前列腺癌诊断. 这种方法完善了 perfusion 的特性,提高了诊断准确度,同时减少了对比剂的依赖.

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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
    • 在瘤学瘤学.

    背景情况:

    • 动态对比增强型MRI (DCE-MRI) 对前列腺癌的诊断至关重要.
    • 目前用于DCE-MRI合成的深度学习方法使用单阶段架构,限制了功能改进.
    • 保存输液细节对于准确的诊断信息至关重要.

    研究的目的:

    • 开发一个基于生成对抗网络 (GAN) 的多阶段框架 (MPM-DCE) 来合成早期和晚期响应的DCE-MRI图像.
    • 改进DCE-MRI合成中的 perfusion 特性逐步的精细化.
    • 通过减少对对比剂的依赖来提高前列腺癌诊断.

    主要方法:

    • 拟议的MPM-DCE是一个多阶段的GAN框架,具有编码器-解码器生成器.
    • 使用自适应级联特征融合 (AICF) 块,用于阶段之间的顺序和横向信息交换.
    • 使用多式输入来合成DCE-MRI图像.

    主要成果:

    • 在 ProstateX 数据集上,MPM-DCE 实现了最先进的性能.
    • 与DCE-Former相比,表现出优异的结果,在PSNR,SSIM和MAE的早期和晚期反应方面都有显著的改进.
    • 验证了多阶段渐进特征改进和跨阶段关注的重要性.

    结论:

    • MPM-DCE显著提高了DCE-MRI合成质量,保留了关键的 perfusion 细节.
    • 该方法减少了对加多对比剂的需求,可能提高前列腺癌诊断.
    • 从统计学上显著的改善表明,临床相关性改善了诊断准确度和减少不必要的活检.