Keywords: AI/ML Image Reconstruction, Quantitative Imaging
Motivation: Recent advancements in deep learning hold great promise for expediting MRI scans, particularly in the time-consuming domain of quantitative MRI. However, the inherent "black-box" nature of deep learning models introduces an element of unknown risk when confronted with unseen data. This is a critical concern in qMRI, where the utmost reliability is imperative.
Goal(s): To develop a qMRI reconstruction framework which could quantifies model uncertainty for guiding clinical decisions.
Approach: We presented a conditional Wasserstein GAN for qMRI reconstruction, enabling uncertainty assessment through posterior sampling.
Results: The proposed method achieved comparable performance to the current method while offering valuable pixel-wise uncertainty maps.
Impact: The study offers clinicians and researchers a reliable qMRI reconstruction method with pixel-wise uncertainty assessment. This could spark further investigations into model reliability and potentially facilitate the practical application of deep learning-based qMRI methods.
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