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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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相关实验视频

Updated: Jul 26, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

用预先学习的子空间进行B₁映射,用于定量脑成像.

Tianxiao Zhang¹, Yibo Zhao^2,3, Wen Jin^2,3

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Magnetic resonance in medicine

|June 22, 2023

概括

此摘要是机器生成的。

本研究介绍了一种机器学习方法,用于估计发射器和接收器B1场,以纠正脑成像中的B1不均性. 这种方法可以提高幻影,健康受试者和脑瘤患者的定量成像准确度.

关键词:

B1 在同质性方面.机器学习是机器学习.定量脑磁力共振 (MRI) 是一种脑磁力共振的方法.下空间建模的模型.

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Basics of Multivariate Analysis in Neuroimaging Data

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Published on: September 25, 2019

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Last Updated: Jul 26, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Basics of Multivariate Analysis in Neuroimaging Data

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Published on: July 24, 2010

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

科学领域:

医疗成像医学成像
机器学习机器学习
定量脑成像技术脑成像技术

背景情况:

B1 场的不均性是定量脑成像的一个重大挑战,影响准确性.
对发射器 (B1t) 和接收器 (B1r) 场的准确估计对于有效的B1不均性纠正至关重要.

研究的目的:

开发一种基于机器学习的新方法,用于估计发射器和接收器B1字段.
改善B1不均质效应在定量脑成像中的纠正.

主要方法:

基于子空间模型的机器学习方法被用来估计B1t和B1r字段.
概率子空间模型捕获了取决于扫描的B1场变化,从预先扫描的数据中学习.
新实验数据 B1 场估计是通过先前约束的线性优化进行的.

主要成果:

该方法在幽灵和健康受试者中生成了高质量的B1地图.
B1校正从SPGR数据显著改善了T1和质子密度 (PD) 地图.
在脑瘤患者中,该方法比传统技术更准确,更强大的B1估计和校正.
对瘤患者的MRSI数据的应用产生了改善的神经代谢物地图的结果,具有更好的组织分化.

结论:

开发了一种使用概率子空间模型的新型机器学习方法,用于B1场估计.
这种方法在实际的定量脑成像应用中为纠正B1不均性提供了更强大的解决方案.