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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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Updated: Sep 9, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

TransUNET-DDPM:用于特定主体大脑网络生成和分类的变压器增强扩散模型

Meenu Ajith¹, Vince D Calhoun¹

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, 30303, GA, USA.

Computers in biology and medicine

|August 29, 2025

概括

本研究介绍了TransUNET-DDPM,这是一个创建高质量的内在连接网络 (ICN) 的新型生成AI框架. 它通过生成现实的合成数据来增强神经影像分析和辅助精神分裂症分类.

关键词:

数据增强扩散模型本质连接网络精神分裂症的分类变压器架构

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Last Updated: Sep 9, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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科学领域:

人工智能
神经成像
计算机视觉

背景情况:

生成性人工智能,特别是扩散模型, 具有先进的图像合成.
现有的内在连接网络 (ICN) 方法存在局限性.

研究的目的:

介绍TransUNET-DDPM,这是一个融合变压器和无噪声扩散概率模型 (DDPM) 的新框架.
创建高质量的二维和三维内在连接网络 (ICNs).
增强神经影像分析和帮助数据增强用于分类任务.

主要方法:

使用基于变压器的架构与DDPM进行非线性建模.
采用静态fMRI (rs-fMRI) 的特定对象3DICN的图像条件变体.
实施转移学习以提供高效的培训,并为数据增强提供课堂化版本.

主要成果:

通过TransUNET-DDPM生成具有解剖学和功能意义的ICNs,其性能优于现有的模型.
这种有图像条件的变种有效地产生了对特定主体的3D ICNs.
类条件生成提高了对精神分裂症检测的分类器稳定性,特别是在数据稀缺的情况下.

结论:

TransUNET-DDPM提供了一个强大的新方法来生成高保真性ICN.
该框架在推进神经成像研究和临床应用方面具有重大潜力.
在外部数据集中验证了通用性,证实了其强大的性能.