Meeting Banner
Abstract #4345

A deep learning network for low-field MRI denoising using group sparsity information and a Noise2Noise method

Yuan Lian1, Xinyu Ye1, Hai Luo2, Ziyue Wu2, and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Marvel Stone Healthcare Co., Ltd., Wuxi, China

Synopsis

Owing to hardware advancements, interest in low-field MRI system has increased recently. However, the imaging quality of low-field MRI is limited due to intrinsic low signal to noise ratio (SNR). Here we propose a deep-learning model to jointly denoise multi-contrast images using Noise2Noise training strategy. Our method can promote the SNRs of multi-contrast low-field images, and experiments show the effectiveness of the proposed strategy.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords