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Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks

  • Xueli Liu
  • Dongsheng Jiang
  • Manning Wang
  • Zhijian Song
Original Article
  • 40 Downloads

Abstract

Multi-modal image registration has significant meanings in clinical diagnosis, treatment planning, and image-guided surgery. Since different modalities exhibit different characteristics, finding a fast and accurate correspondence between images of different modalities is still a challenge. In this paper, we propose an image synthesis-based multi-modal registration framework. Image synthesis is performed by a ten-layer fully convolutional network (FCN). The network is composed of 10 convolutional layers combined with batch normalization (BN) and rectified linear unit (ReLU), which can be trained to learn an end-to-end mapping from one modality to the other. After the cross-modality image synthesis, multi-modal registration can be transformed into mono-modal registration. The mono-modal registration can be solved by methods with lower computational complexity, such as sum of squared differences (SSD). We tested our method in T1-weighted vs T2-weighted, T1-weighted vs PD, and T2-weighted vs PD image registrations with BrainWeb phantom data and IXI real patients’ data. The result shows that our framework can achieve higher registration accuracy than the state-of-the-art multi-modal image registration methods, such as local mutual information (LMI) and α-mutual information (α-MI). The average registration errors of our method in experiment with IXI real patients’ data were 1.19, 2.23, and 1.57 compared to 1.53, 2.60, and 2.36 of LMI and 1.34, 2.39, and 1.76 of α-MI in T2-weighted vs PD, T1-weighted vs PD, and T1-weighted vs T2-weighted image registration, respectively. In this paper, we propose an image synthesis-based multi-modal image registration framework. A deep FCN model is developed to perform image synthesis for this framework, which can capture the complex nonlinear relationship between different modalities and discover complex structural representations automatically by a large number of trainable mapping and parameters and perform accurate image synthesis. The framework combined with the deep FCN model and mono-modal registration methods (SSD) can achieve fast and robust results in multi-modal medical image registration.

Graphical abstract

The workflow of proposed multi-modal image registration framework

Keywords

Multi-modal registration Image synthesis Convolutional neural network 

Notes

Authors’ contributions

Xueli Liu and Dongsheng Jiang developed the algorithm, performed the experiments, analyzed the data, and drafted the manuscript. Manning Wang and Zhijian Song provided suggestions and helped to draft the manuscript. All authors have read and approved the final manuscript.

Funding

This study has been supported by the National Key Research and Development Program of China (2017YFC0110700) and the National Natural Science Foundation of China (grants 81471758 and 81701795). This research has also been partially supported by the Program of Shanghai Academic/Technology Research Leaders (16XD1424900).

Compliance with ethical standards

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Digital Medical Research Center, School of Basic Medical SciencesFudan UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted InterventionShanghaiChina

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