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磁共振成像 2024年7月第15卷第7期 Chin J Magn Reson Imaging, Jul, 2024, Vol. 15, No. 7 技术研究｜｜Technical Article

基于解剖MRI的大脑皮质表面影像组学

重建方法研究

*
张之凡，王训恒 ，厉力华

作者单位 杭州电子科技大学自动化学院，杭州 310018
* 通信作者 王训恒，E-mail: xhwang@hdu.edu.cn
中图分类号 R445.2；R745.1 文献标识码 A DOI 10.12015/issn.1674-8034.2024.07.024
本文引用格式 张之凡, 王训恒, 厉力华 . 基于解剖 MRI 的大脑皮质表面影像组学重建方法研究[J]. 磁共振成像, 2024, 15(7):
143-150.
[摘要] 目的 设计一种脑皮质表面影像组学计算方法，为脑影像研究提供丰富的、可靠的脑区局部特征。材料与方法 基于
21 组重复测量健康被试与 222 例多动症相关被试的大脑 T1WI 磁共振数据集提取皮层厚度、灰质体积、平均曲率与皮层表面积
四种表面形态指数。使用 Desikan-Killiany（DK） 脑图谱和球面局部投影，实现三维皮层表面脑区的二维平面化。利用
Pyradiomics 对四个形态指数分别提取 968 个二维影像组学特征。结合重复测量数据集与组内相关系数（intra-class correlation
coefficient, ICC），以 ICC 信度值作为影像组学特征评估的标准，综合评价不同形态指数、不同影像组学特征类型与不同脑区间
的复测信度差异。结合多动症数据集，预测患者的注意力缺陷指数、过动指数两种症状指标。结果 对于不同形态指标，灰质
体积、皮层表面积的影像组学特征可重复性较好，与皮层厚度与平均曲率组差异具有统计学意义（P<0.05）。对于不同类型影像
组学特征，基于皮层厚度的一阶特征和灰度共生矩阵特征与其他类型特征差异具有统计学意义（P<0.05）。对于不同脑区，左右
脑内嗅皮层、左右脑颞极与右脑额极提取的特征相较其他区域复测性降低（P<0.05）。总体而言本研究提出的表面重建方法所提
取的脑影像组学特征均具有较高的可重测性（ICC 均值>0.76）。在对多动症两种症状指标的预测中发现，左脑海马旁回、额上
回与颞上回与多动症症状显著相关（|r|=0.33~0.52，P<0.05）。结论 基于 DK 脑图谱与表面形态学指数构建脑影像组学特征是
可行的，所提取的新型特征具有良好的可重复性，并在注意力预测等研究中具有一定的临床价值。
[关键词] 多动症；注意力预测；表面形态指数；球面局部投影；影像组学特征；磁共振成像

Research on radiomics reconstruction from cerebral cortex surface based on anatomical magnetic
resonance imaging
*
ZHANG Zhifan, WANG Xunheng , LI Lihua
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
* Correspondence to WANG X H, E-mail: xhwang@hdu.edu.cn
Received 1 Apr 2024, Accepted 5 Jul 2024; DOI 10.12015/issn.1674-8034.2024.07.024
ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 62071158).
Cite this article as ZHANG Z F, WANG X H, LI L H. Research on radiomics reconstruction from cerebral cortex surface based on
anatomical magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(7): 143-150.

Abstract Objective: To design a computational method of cortical surface radiomics, to provide rich and reliable local features of brain
regions for brain imaging research. Materials and Methods: Based on the T1WI magnetic resonance data sets of 21 groups of repeated
measurements of healthy subjects and 222 attention deficit hyperactivity disorder (ADHD)-related subjects, four surface morphological
indices including cortical thickness, gray matter volume, mean curvature and cortical surface area were extracted. Using the
Desikan-Killiany (DK) brain atlas and spherical local projection, the brain area is flattened from the three-dimensional cortical surface to
two-dimensional. Pyradiomics was used to extract 968 two-dimensional radiomics features for each of the four morphological indices.
Combining repeated measurement data set and intra-class correlation coefficients (ICC), the ICC value was used as the standard for
evaluating radiomics features to comprehensively evaluate the differences in test-retest reliability among different morphological indices,
different radiomics feature types and different brain regions. And based on the ADHD dataset, we predict the patient's attention deficit
index and hyperactivity index. Results: For different morphological indicators, the radiomics features of gray matter volume and cortical
surface area have better reproducibility, and are significantly different from the cortical thickness and average curvature groups (P<0.05).
For different types of radiomics features, the first-order features and gray-level co-occurrence matrix features based on cortical thickness
showed significant differences from other types of features (P<0.05). For different brain regions, the features extracted from the left and
right entorhinal cortex, the left and right temporal poles, and the right frontal pole have lower retest retestability than other regions (P<
0.05). However, in general, the brain radiomics features extracted by the surface reconstruction method proposed in this study have high
reproducibility (mean ICC>0.76). In the prediction tasks of the two symptom indicators of attention deficit hyperactivity disorder
(ADHD), it was found that the left hippocampal gyrus, superior frontal gyrus and superior temporal gyrus were significantly correlated
with ADHD symptoms (|r|=0.33-0.52, P<0.05). Conclusions: It is feasible to construct brain radiomics features based on DK brain atlas
and surface morphology index. The extracted new features have good repeatability and have certain clinical value in attention prediction
and other studies.
Key words attention deficit hyperactivity disorder (ADHD); attention prediction; surface morphological index; spherical local
projection; radiomics features; magnetic resonance imaging

收稿日期 2024-04-01 接受日期 2024-07-05
基金项目 国家自然科学基金项目（编号：62071158）

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