Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Denoising, model based
Motivation: The most prominent approach for MRI denoising has been the direct DL denoising methods. Robustness of such methods are not guaranteed while denoising MR images with unseen noise levels.
Goal(s): Propose and evaluate a model-based DL denoising method for MR image.
Approach: In this work, we adapt the model-based DL methods for MRI reconstruction to perform MR image denoising.
Results: The proposed method was evaluated on in-vivo MRI data. The evaluation dataset consisted of MRI data of several contrasts for multiple anatomies. A qualitative analysis shows that the denoised images have fine structures well preserved.
Impact: A model-based approach for MRI denoising provides guarantees against contrast and fine structure alterations during denoising. By virtue of the proposed method, we shall have robust and generalizable MRI denoisers.
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.
Keywords