Multi-contrast MRI synthesis with channel-exchanging-network

Date
2022-08-29
Advisor
Instructor
Source Title
Signal Processing and Communications Applications Conference (SIU)
Print ISSN
2165-0608
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
[1] - [4]
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

Magnetic resonance imaging (MRI) is used in many diagnostic applications as it has a high soft-tissue contrast and is a non-invasive medical imaging method. MR signal levels differs according to the parameters T1, T2 and PD that change with respect to the chemical structure of the tissues. However, long scan times might limit acquiring images from multiple contrasts or if the multi-contrasts images are acquired, the contrasts are noisy. To overcome this limitation of MRI, multi-contrast synthesis can be utilized. In this paper, we propose a deep learning method based on Channel-Exchanging-Network (CEN) for multi-contrast image synthesis. Demonstrations are provided on IXI dataset. The proposed model based on CEN is compared against alternative methods based on CNNs and GANs. Our results show that the proposed model achieves superior performance to the competing methods.

Course
Other identifiers
Book Title
Keywords
Multimodal fusion, Channel-exchanging-network, Multi-contrast image synthesis, Deep learning
Citation
Published Version (Please cite this version)