Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, synthetic data, compressed sensing, accelerated imagingThis study demonstrates using generative deep learning for transforming magnitude-only images into synthetic raw data for deep-learning-base image reconstruction. Using relatively few raw datasets, a set of neural networks was trained to generate the missing phase and coil sensitivity information in magnitude-only images. These maps are then recombined into synthetic raw data. We trained end-to-end variational networks for 4-fold accelerated compressed sensing reconstruction on the FastMRI dataset, with increasing training set size. Synthetic raw data showed similar improvements as real raw data with increasing data. This shows promise for applying deep-learning-based image reconstruction when raw data is scarce.
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