Abstract #3767

DeepDenoiseNet: a convolutional neural network trained with synthesized images from inversion-recovery maps for SASHA denoising

Xiaofeng Qian¹, Ancong Wang¹, Yingwei Fan¹, Yafeng Li², Bowei Liu³, Yongsheng Jin⁴, Haiyan Ding³, and Rui Guo¹

¹Shool of Medical Technology, Beijing Institute of Technology, Beijing, China, ²China Electronics Harvest Technology Co.,Ltd, Beijing, China, ³Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, ⁴Department of Infectious Diseases, The Affiliated Hospital of Yan’an University, Yan’an, Shanxi, China

Synopsis

Keywords: Myocardium, Myocardium, SASHA

Motivation: SASHA T₁has high accuracy but low precision due to the low SNR of T₁-weighted images. Convolutional neural network has the potential to improve SASHA T₁ precision by using spatio-temporal correlations.

Goal(s): The aim of this study is to develope a convolutional neural network for improving SASHA T₁ precision.

Approach: We implemented a convolutional neural network (DeepDenoiseNet) and trained it using synthesized SASHA images from co-registered high-quality T₁, T₂, and M₀ images. Different-level noise was added to simulate low SNR SASHA images.

Results: DeepDenoiseNet could reduce the impaction from noise and improve SASHA T₁precision.

Impact: The deep convolutional neural network trained with synthesized images and simulated noise could improve SASHA T₁ precision.

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.