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基于T1加权图像的白质纤维束分割方法

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白质纤维束分割方法通过识别连接不同脑区的白质通路,为脑连接分析提供了重要的神经通路参考信息.然而,传统的白质纤维束分割方法主要依赖于弥散磁共振图像(Diffusion magnetic resonance imaging,dMRI),由于获取弥散磁共振图像比较耗时,这极大地限制了其在临床中的应用.为解决此问题,提出了一种基于T1加权图像的白质纤维束分割方法,通过计算T1加权图像的结构张量来提示可能的纤维走向,进而提高白质纤维束的分割精度.此外,本文在模型训练期间引入弥散磁共振图像的特权信息来指导模型学习,从而提升白质束分割模型性能,具有挑战性的束分割效果提升明显,其中左穹窿(Left fornix,FX_left)的Dice得分提高了5%,右穹窿(Right fornix,FX_right)的Dice得分提高了6%.本研究弥补了在缺少弥散磁共振图像的场景下无法进行神经通路分析的不足,扩展了神经通路分析的应用场景.
White Matter Fiber Tract Segmentation Method Based on T1-Weighted Imaging
White matter fiber tract segmentation methods provide crucial neural pathway reference information for brain connectivity analysis by identifying white matter tracts connecting distinct brain regions.Traditional segmentation methods predominantly depend on diffusion magnetic resonance imaging(dMRI),but the lengthy acquisition time of dMRI severely restricts its clinical applicability.To address this limitation,this paper introduces a white matter fiber tract segmentation approach based on T1-weighted imaging.This method leverages the structural tensor of T1-weighted images to infer potential fiber orientations,thereby enhancing the segmentation accuracy of white matter tracts.Moreover,the proposed method incorporates privileged information from dMRI during model training to guide the learning process,thus improving the performance of the white matter tract segmentation model,and the segmentation of challenging tracts is improved significantly,with a 5%improvement in Dice score for the left fornix(FX_left)and a 6%improvement in Dice score for the right fornix(FX_right).This approach mitigates the limitations of conducting neural pathway analysis in the absence of dMRI,broadening the application scope of neural pathway analysis.

medical image segmentationwhite matter fiber tractsprivileged informationT1-weighted imagingdiffusion magnetic resonance imaging(dMRI)

焦瑞柯、张小凤、叶初阳

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北京理工大学集成电路与电子学院,北京 100081

北京理工大学(珠海)大湾区创新研究院,珠海 519088

医学图像分割 白质纤维束 特权信息 T1加权图像 弥散磁共振图像

北京市自然科学基金中央高校基本科研业务费专项广东省"天临空地海"复杂环境智能探测重点实验室研究基金

72422732024CX060402022KSYS016

2024

10.16337/j.1004-9037.2024.04.007

数据采集与处理
中国电子学会 中国仪器仪表学会信号处理学会　中国仪器仪表学会中国物理学会微弱信号检测学会　南京航空航天大学

数据采集与处理

CSTPCD北大核心
影响因子：0.679
ISSN：1004-9037
年,卷(期)：2024.39(4)