Meeting Banner
Abstract #2801

Denoising very low field magnetic resonance images using native noise modeling and deep learning

Tonny Ssentamu1, Ronald Omoding2, Atamba Edgar 1, Pius kabanda Mukwaya 1, Jjuuko George William1, Alvin Bagetuuma Kimbowa 2, and Sairam Geethanath3
1Department of physiology, Makerere University, Kampala, Uganda, 2Department of Electrical and Computer Engineering, Makerere University, Kampala, Uganda, 3Accessible MR Laboratory, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Low-Field MRI, Denoising, Native noise

Motivation: Low-field MRI (LF-MRI) can increase accessibility in low-income countries where high-field MRI is not available due to cost, power and siting requirements. However, noise significantly affects LF-MR image quality.

Goal(s): This study aims to enhance the signal-to-noise ratio (SNR) in very LF-MRI (0.05T) images using native noise modeling and deep learning.

Approach: We extracted noise from 0.05T phantom MRI images, modeled it, added it to high-field brain MRI (1.5T & 3T), trained two deep-learning algorithms, and evaluated them on in vivo brain MRI images.

Results: Our approach improves the SNR of in-vivo LF images by a factor of approximately two.

Impact: Using native noise while developing deep-learning denoising algorithms for LF-MRI images is better than using synthetic random noise. As a result, the developed algorithms are more explainable and follow domain knowledge on noise in LF-MRI improving trust in the models.

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

noisefieldmodelsphantomlearningnative