Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Denoising, Unsupervised Learning
Motivation: Traditional MRI scans, necessary for high SNR and clear images, are time-consuming and discomfort for patients. Shorter scans, meant to improve the patient experience, often compromise image quality and SNR. New deep learning techniques provide a solution to denoise MRI scans, even with limited data availability.
Goal(s): We aim to create an unsupervised MRI denoising method for real-world clinical settings, eliminating the need for clean or paired noisy images ensuring versatility and practicality.
Approach: We use an unsupervised diffusion-based denoising approach to denoise MRI scans.
Results: We achieve unsupervised denoising for MRI scans, outperforming previous methods and reducing time to 6 seconds.
Impact: Our approach denoises general MRI scans without extra clean or noisy data. It's suitable for real-world clinics, reducing patient MRI time. It enhances imaging quality, ensuring accurate diagnoses and faster clinical practices for patients and doctors.
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