Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: Spatial resolution, signal-to-noise ratio, and motion artifacts critically matter in any MRI practices. Current methods focus on a single source of known degradation of imaging. A unified framework is desired, which allows for high-quality reconstruction in the face of multiple unknown sources of degradation.
Goal(s): We reconstruct high-quality brain MRI against degradations by motion, noise, and low resolution, with an image-to-image translation-based deep neural framework.
Approach: We developed a prompt-based learning approach and assessed it on a public brain MRI dataset.
Results: Our method offered remarkably improved reconstructions (PSNR=30.96dB, SSIM=0.9133), as compared to two other state-of-the-art methods.
Impact: We developed a new methodology that enables high-quality MRI reconstruction from scans corrupted by a mixture of multiple unknown sources of degradations, which commonly happen in clinical and research MRI studies, with a unified reconstruction framework.
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