Functional magnetic resonance imaging progressive deformable registration based on a cascaded convolutional neural network

Quantitative Imaging in Medicine and Surgery

article

Qiaoyun Zhu¹  Guoye Lin¹  Yuhang Sun¹  Yi Wu¹  Yujia Zhou¹  Qianjin Feng¹ 

[1] School of Biomedical Engineering, Southern Medical University;Guangdong Provincial Key Laboratory of Medical Image Processing;Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University

关键词: Functional magnetic resonance imaging (fMRI);    deformable image registration;    Multi-resolution network (MR-Net);    cascaded network;   

DOI  :  10.21037/qims-20-1289

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Intersubject registration of functional magnetic resonance imaging (fMRI) is necessary for group analysis. Accurate image registration can significantly improve the results of statistical analysis. Traditional methods are achieved by using high-resolution structural images or manually extracting functional information. However, structural alignment does not necessarily lead to functional alignment, and manually extracting functional features is complicated and time-consuming. Recent studies have shown that deep learning-based methods can be used for deformable image registration. Methods: We proposed a deep learning framework with a three-cascaded multi-resolution network (MR-Net) to achieve deformable image registration. MR-Net separately extracts the features of moving and fixed images via a two-stream path, predicts a sub-deformation field, and is cascaded three times. The moving and fixed images’ deformation field is composed of all sub-deformation fields predicted by the MR-Net. We imposed large smoothness constraints on all sub-deformation fields to ensure their smoothness. Our proposed architecture can complete the progressive registration process to ensure the topology of the deformation field. Results: We implemented our method on the 1000 Functional Connectomes Project (FCP) and Eyes Open Eyes Closed fMRI datasets. Our method increased the peak t values in six brain functional networks to 19.8, 17.8, 15.0, 16.4, 17.0, and 13.2. Compared with traditional methods [i.e., FMRIB Software Library (FSL) and Statistical Parametric Mapping (SPM)] and deep learning networks [i.e., VoxelMorph (VM) and Volume Tweening Network (VTN)], our method improved 47.58%, 11.88%, 18.60%, and 15.16%, respectively. Conclusions: Our three-cascaded MR-Net can achieve statistically significant improvement in functional consistency across subjects.

【 授权许可】

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