Meeting Banner
Abstract #3859

Simultaneous reduction of noise and motion artifacts in brain MRI using deep learning.

Isao Muro1,2, Shuhei Shibukawa2, and Keisuke Usui2
1MRI, AIC YAESU CLINIC, Tokyo, Japan, 2Faculty of Health Science, Juntendo University, Tokyo, Japan

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: The aim is to contribute to diagnosis by simultaneously reducing motion artifacts and noise in head MRI images using deep learning.

Goal(s): The goal is to achieve high motion artifact and noise reduction in T1W, T2W, and FLAIR images, independent of artifact and noise levels.

Approach: Simulation was used to create an image containing motion artifacts and noise, and deep learning was used to evaluate the removal effect.

Results: The average SSIM between the ground troth and the input image was 0.72, and the SSIM between the ground troth and the output image using this method was 0.95, showing a high improvement effect.

Impact: By 36,000 pairs of training data, we were able to increase the accuracy of the learning process. The advantage of this method is that it is post-processing and can be used regardless of the equipment or imaging method.

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