人脑肿瘤质子光谱成像数据的独立成分分析

Independent component analysis to proton spectroscopic imaging data of human brain tumours.

作者信息

Pulkkinen J, Häkkinen A-M, Lundbom N, Paetau A, Kauppinen R A, Hiltunen Y

机构信息

Department of Biomedical NMR, A.I. Virtanen Institute, University of Kuopio, Finland.

出版信息

Eur J Radiol. 2005 Nov;56(2):160-4. doi: 10.1016/j.ejrad.2005.03.018.

DOI:10.1016/j.ejrad.2005.03.018

PMID:16233889

Abstract

In proton magnetic resonance spectroscopic imaging (1H MRSI), the recorded spectra are often linear combinations of spectra from different cell and tissue types within the voxel. This produces problems for data analysis and interpretation. A sophisticated approach is proposed here to handle the complexity of tissue heterogeneity in MRSI data. The independent component analysis (ICA) method was applied without prior knowledge to decompose the proton spectral components that relate to the heterogeneous cell populations with different proliferation and metabolism that are present in gliomas. The ability to classify brain tumours based on IC decomposite spectra was studied by grouping the components with histopathology. To this end, 10 controls and 34 patients with primary brain tumours were studied. The results indicate that ICA may reveal useful information from metabolic profiling for clinical purposes using long echo time MRSI of gliomas.

摘要

在质子磁共振波谱成像（1H MRSI）中，记录的谱图通常是体素内不同细胞和组织类型谱图的线性组合。这给数据分析和解释带来了问题。本文提出了一种复杂的方法来处理MRSI数据中组织异质性的复杂性。在没有先验知识的情况下应用独立成分分析（ICA）方法来分解与胶质瘤中存在的具有不同增殖和代谢的异质细胞群体相关的质子光谱成分。通过将成分与组织病理学分组，研究了基于IC分解谱对脑肿瘤进行分类的能力。为此，对10名对照者和34名原发性脑肿瘤患者进行了研究。结果表明，ICA可以通过胶质瘤的长回波时间MRSI从代谢谱中揭示有用信息用于临床目的。