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

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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Updated: Jul 6, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

使用深度约束球形解卷用于扩散权重磁共振成像的坚固纤维定向分布函数估计.

Tianyuan Yao¹, Francois Rheault², Leon Y Cai³

¹Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.

Journal of medical imaging (Bellingham, Wash.)

|January 8, 2024

概括

此摘要是机器生成的。

这项研究引入了一种新的深度受约束球形解卷 (CSD) 方法,以提高扩散权重MRI (DW-MRI) 数据的可靠性. 该方法减少了大脑微观结构建模的变异性,增强了跨不同站点和扫描的通道图和连接性分析.

关键词:

深度学习是一种深度学习.扩散磁共振成像技术的使用.建模建模模型建模模型

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Last Updated: Jul 6, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

科学领域:

神经成像是一种神经成像.
生物物理学的生物物理.
医学物理医学物理

背景情况:

扩散权重磁共振成像 (DW-MRI) 对于绘制大脑微观结构的图像至关重要.
来自DW-MRI的纤维方向分布函数 (fODF) 对于曲谱和连接分析至关重要.
现有的方法在多站点和纵向DW-MRI研究中难以衡量测量变异性.

研究的目的:

开发一个数据驱动的深度受约束球形解卷 (CSD) 方法,明确考虑扫描-重新扫描变量.
从重复的DW-MRI扫描中增强大脑微观结构建模的可复制性和稳定性.
提高多站点和纵向神经成像研究中fODF估计的性能.

主要方法:

提出了一种深度的CSD方法,包括一个3D体积扫描仪不变规范化方案.
使用对比损失与扫描/重新扫描数据用于fODF估计.
在人类结合体项目 (HCP) 测试复试数据集,MASIVar数据集和巴尔的摩老龄化纵向研究数据集上验证了该方法.

主要成果:

与现有基准相比,拟议的方法在重复的fODF估计中表现优越.
通过使用对比性损失实现了更高的一致性和角度相关系数与CSD建模.
在下游连接分析中表现更好,用于区分具有不同生物标志物的受试者.

结论:

开发了一种深度CSD方法,以有效地减少可复制大脑微观结构建模的扫描-重新扫描变异性.
拟议的方法提供了一个适用于神经成像中更广泛的数据协调挑战的插入式解决方案.