Noises, artifacts, and loss of information caused by the MR reconstruction may compromise the final performance of the downstream applications such as image segmentation. In this study, we develop a re-weighted multi-task deep learning method to learn prior knowledge from the existing big dataset and then utilize them to assist simultaneous MR reconstruction and segmentation from under-sampled k-space data. It integrates the reconstruction with segmentation and produces both promising reconstructed images and accurate segmentation results. This work shows a new way for the direct image analysis from k-space data with deep learning.
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