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大脑MRI的未配对体积协调与有条件的潜在扩散

Mengqi Wu¹, Minhui Yu¹, Shuaiming Jing¹

¹Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, 27599, NC, USA.

Medical image analysis

|November 5, 2025

概括

这项研究引入了一种新的3DMRI协调方法 (HCLD),以消除神经成像数据中的特定位置变异. HCLD在体积上有效协调大脑MRI,保留生物特征,以便更好地进行多地点研究.

科学领域:

神经成像是一种神经成像.
医学图像分析医学图像分析
人工智能的人工智能

背景情况:

多站点结构性MRI研究对于不同的队列至关重要,但面临着与站点相关变化的挑战.
当前的回顾性协调方法经常使用手工制作的特征或2D切片,限制了它们的有效性和适用性.
现有的方法无法充分利用体积信息,导致协调结果不够理想.

研究的目的:

开发一种新的3DMRI协调框架,即通过条件隐性扩散 (HCLD) 进行协调,以解决与位置相关的变化.
通过明确考虑图像风格和大脑解剖学,使体积水平的协调成为可能.
创建一种不需要配对图像进行训练的方法.

主要方法:

一个可泛化的3D自编码器将MRI编码和解码到4D潜伏空间.
一个有条件的潜伏扩散模型学习潜伏分布,并产生协调的MRI.
该框架要求对目标图像风格进行协调,同时保留源MRI的解剖信息.

主要成果:

在三个数据集中,HCLD接受了4158个T1加权脑MRI的训练和评估.
该方法在消除与地点相关的变异,同时保持基本的生物特征方面表现出有效性.
定性和定量实验表明,HCLD的性能超过了几种最先进的协调技术.

结论:

HCLD通过潜在风格翻译提供了高效的体积级MRI协调解决方案.
拟议的框架有效地解决了现有的二维和基于特征的协调方法的局限性.
HCLD在提高多地点神经成像研究的质量和可靠性方面显示出显著的前景.

关键词:

自动编码器自动编码器大脑MRI 脑部MRI 脑部统一化统一化统一化潜在扩散模型的潜伏扩散模型.

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Magnetic Resonance Imaging

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