Multi-modality Large Deformation Diffeomorphic Metric Mapping Driven by Single-modality Images

Wu，Jiong1; Zhou，Shuang2; Yang，Qi3; Zhang，Yue4,5; Tang，Xiaoying4

10.1109/EMBC46164.2021.9630617

2021

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

1557-170X

1558-4615

978-1-7281-1180-3

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

2610-2613

NOV 01-05, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Multi-modality magnetic resonance image (MRI) registration is an essential step in various MRI analysis tasks. However, it is challenging to have all required modalities in clinical practice, and thus the application of multi-modality registration is limited. This paper tackles such problem by proposing a novel unsupervised deep learning based multi-modality large deformation diffeomorphic metric mapping (LDDMM) framework which is capable of performing multi-modality registration only using single-modality MRIs. Specifically, an unsupervised image-to-image translation model is trained and used to synthesize the missing modality MRIs from the available ones. Multi-modality LDDMM is then performed in a multi-channel manner. Experimental results obtained on one publicly- accessible datasets confirm the superior performance of the proposed approach.Clinical relevance - This work provides a tool for multi-modality MRI registration with solely single-modality images, which addresses the very common issue of missing modalities in clinical practice.

Measurement Deep learning Magnetic resonance imaging Magnetic resonance Tools Biology Task analysis

通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[62071210];

Engineering

Engineering, Biomedical ; Engineering, Electrical & Electronic

WOS:000760910502118

20220811670348

2-s2.0-85122491941

Scopus

1.Hunan University of Arts and Science,School of Computer and Electrical Engineering,Hunan,China
2.Hunan University of Arts and Science,Furong College,Hunan,China
3.Sun Yat-sen University,School of Electronics and Information Technology,Guangzhou,China
4.Southern University of Science and Technology,Department of Electrical and Electronic Engineering,Shenzhen,China
5.Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong,Hong Kong

Wu，Jiong,Zhou，Shuang,Yang，Qi,et al. Multi-modality Large Deformation Diffeomorphic Metric Mapping Driven by Single-modality Images[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:2610-2613.