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Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma Grading



. 2022 May 10;2022:7315665.


doi: 10.1155/2022/7315665.


eCollection 2022.

Affiliations

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Peiying Guo et al.


J Healthc Eng.


.

Abstract

Accurate preoperative glioma grading is essential for clinical decision-making and prognostic evaluation. Multiparametric magnetic resonance imaging (mpMRI) serves as an important diagnostic tool for glioma patients due to its superior performance in describing noninvasively the contextual information in tumor tissues. Previous studies achieved promising glioma grading results with mpMRI data utilizing a convolutional neural network (CNN)-based method. However, these studies have not fully exploited and effectively fused the rich tumor contextual information provided in the magnetic resonance (MR) images acquired with different imaging parameters. In this paper, a novel graph convolutional network (GCN)-based mpMRI information fusion module (named MMIF-GCN) is proposed to comprehensively fuse the tumor grading relevant information in mpMRI. Specifically, a graph is constructed according to the characteristics of mpMRI data. The vertices are defined as the glioma grading features of different slices extracted by the CNN, and the edges reflect the distances between the slices in a 3D volume. The proposed method updates the information in each vertex considering the interaction between adjacent vertices. The final glioma grading is conducted by combining the fused information in all vertices. The proposed MMIF-GCN module can introduce an additional nonlinear representation learning step in the process of mpMRI information fusion while maintaining the positional relationship between adjacent slices. Experiments were conducted on two datasets, that is, a public dataset (named BraTS2020) and a private one (named GliomaHPPH2018). The results indicate that the proposed method can effectively fuse the grading information provided in mpMRI data for better glioma grading performance.

Conflict of interest statement

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Figures



Figure 1

Overview of the proposed framework.


Figure 2


Figure 2

The process of graph convolution operation.


Figure 3


Figure 3

Image examples of 4-sequence MRI from BraTS2020 and GliomaHPPH2018 datasets.


Figure 4


Figure 4

Methodology for realizing mpMRI context and multiparameter information fusion simultaneously. (a) N(1). (b) N(2).


Figure 5


Figure 5

Classification accuracy of the validation set under different CNN models and vertex feature dimensionality of GCN. (a) BraTS2020 dataset. (b) GliomaHPPH2018 dataset.


Figure 6


Figure 6

Classification accuracy of the validation set under different CNN models and GCN iterations. (a) BraTS2020 dataset. (b) GliomaHPPH2018 dataset.

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