Supervised learning is widely used for deep learning based image quality enhancement for improved clinical diagnosis. However, the difficulties to acquire a large number of high-quality reference image for different MR applications can limit its generalization performance. An unsupervised domain adaptation (DA) approach is proposed and incorporated into the deep learning based image enhancement framework, which improves the performance of trained network on new datasets. Preliminary evaluation on point spread function enhanced turbo spin echo imaging has showed that the unsupervised DA approach can provide more stabilized image sharpness improvement without severe amplified noise.
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