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正常化 FLAIR MR 成像在儿童和青少年中的脑成熟模式。

Brain Maturation Patterns on Normalized FLAIR MR Imaging in Children and Adolescents.

机构信息

From the Department of Electrical, Computer and Biomedical Engineering (K.C., A.G., D.R., A.K.), Toronto Metropolitan University, Toronto, Ontario, Canada

Institute for Biomedical Engineering, Science Tech (iBEST) (K.C., A.G., D.R., A.K.), a Partnership between St. Michael's Hospital and Toronto Metropolitan University, Toronto, Ontario, Canada.

出版信息

AJNR Am J Neuroradiol. 2023 Sep;44(9):1077-1083. doi: 10.3174/ajnr.A7966. Epub 2023 Aug 17.

Abstract

BACKGROUND AND PURPOSE

Signal analysis of FLAIR sequences is gaining momentum for studying neurodevelopment and brain maturation, but FLAIR intensity varies across scanners and needs to be normalized. This study aimed to establish normative values for standardized FLAIR intensity in the pediatric brain.

MATERIALS AND METHODS

A new automated algorithm for signal normalization was used to standardize FLAIR intensity across scanners and subjects. Mean intensity was extracted from GM, WM, deep GM, and cortical GM regions. Regression curves were fitted across the pediatric age range, and ANOVA was used to investigate intensity differences across age groups. Correlations between intensity and regional volume were also examined.

RESULTS

We analyzed 429 pediatric FLAIR sequences in children 2-19 years of age with a median age of 11.2 years, including 199 males and 230 females. WM intensity had a parabolic relationship with age, with significant differences between various age groups ( < .05). GM and cortical GM intensity increased over the pediatric age range, with significant differences between early childhood and adolescence ( < .05). There were no significant relationships between volume and intensity in early childhood, while there were significant positive and negative correlations ( < .05) in WM and GM, respectively, for increasing age groups. Only the oldest age group showed significant differences between males and females ( < .05).

CONCLUSIONS

This work presents a FLAIR intensity standardization algorithm to normalize intensity across large data sets, which allows FLAIR intensity to be used to compare regions and individuals as a surrogate measure of the developing pediatric brain.

摘要

背景与目的

FLAIR 序列信号分析在研究神经发育和大脑成熟方面越来越受到关注,但 FLAIR 强度因扫描仪而异,需要进行标准化。本研究旨在建立儿科大脑标准化 FLAIR 强度的正常值。

材料与方法

使用新的自动信号归一化算法对扫描仪和受试者的 FLAIR 强度进行标准化。从 GM、WM、深部 GM 和皮质 GM 区域提取平均强度。在儿科年龄范围内拟合回归曲线,并使用 ANOVA 研究年龄组之间的强度差异。还检查了强度与区域体积之间的相关性。

结果

我们分析了 429 名 2-19 岁儿童的 FLAIR 序列,中位数年龄为 11.2 岁,包括 199 名男性和 230 名女性。WM 强度与年龄呈抛物线关系,各年龄组之间存在显著差异(<0.05)。GM 和皮质 GM 强度在儿科年龄范围内增加,幼儿期和青春期之间存在显著差异(<0.05)。在幼儿期,体积与强度之间没有显著关系,而在年龄较大的组中,WM 和 GM 分别存在显著的正相关和负相关(<0.05)。只有年龄最大的组显示出男性和女性之间的显著差异(<0.05)。

结论

这项工作提出了一种 FLAIR 强度标准化算法,可对大数据集进行强度标准化,从而使 FLAIR 强度可用于比较区域和个体,作为发育中儿科大脑的替代测量指标。

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本文引用的文献

1
Alzheimer's and vascular disease classification using regional texture biomarkers in FLAIR MRI.
Neuroimage Clin. 2023;38:103385. doi: 10.1016/j.nicl.2023.103385. Epub 2023 Mar 24.
2
FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition.
Neuroimage Clin. 2022;34:102955. doi: 10.1016/j.nicl.2022.102955. Epub 2022 Feb 8.
4
Neurodevelopmental Disorders: From Genetics to Functional Pathways.
Trends Neurosci. 2020 Aug;43(8):608-621. doi: 10.1016/j.tins.2020.05.004. Epub 2020 Jun 5.
5
Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets.
Magn Reson Imaging. 2019 Oct;62:59-69. doi: 10.1016/j.mri.2019.05.001. Epub 2019 May 16.
6
The development of brain white matter microstructure.
Neuroimage. 2018 Nov 15;182:207-218. doi: 10.1016/j.neuroimage.2017.12.097. Epub 2018 Jan 3.
7
Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress.
Dev Cogn Neurosci. 2018 Oct;33:161-175. doi: 10.1016/j.dcn.2017.12.002. Epub 2017 Dec 7.
8
Structural brain development: A review of methodological approaches and best practices.
Dev Cogn Neurosci. 2018 Oct;33:129-148. doi: 10.1016/j.dcn.2017.11.008. Epub 2017 Nov 22.
9
Whole brain myelin mapping using T1- and T2-weighted MR imaging data.
Front Hum Neurosci. 2014 Sep 2;8:671. doi: 10.3389/fnhum.2014.00671. eCollection 2014.
10
FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities.
AJNR Am J Neuroradiol. 2013 Jan;34(1):54-61. doi: 10.3174/ajnr.A3146. Epub 2012 Jun 14.

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