Keywords: Lung, Lung, PREFUL, U-Net, GAN, Sequence
Motivation: Pulmonary proton MRI in free-breathing typically utilizes either spoiled or balanced steady-state free precession sequence types (SPGRE/bSSFP), which leads to sequence-dependent results variability.
Goal(s): Since deep learning networks are known to be able to translate between different image types, image-mapping from SPGRE to bSSFP is hypothesized to yield more similar results.
Approach: After training a U-Net, alone and part of a generative adversarial network pair (GAN), similarity of results was assessed, including SSIM, MSE, image sharpness and perfusion defect percentage.
Results: Results showed less image sharpness and were significantly more similar after mapping. Both network architectures performed on par.
Impact: As sequence homogenization is limited by vendor standards and hardware-limits the demonstrated sequence-mapping approach via deep learning is viable alternative. It could be specifically used to decrease the variability of perfusion-weighted maps acquired with bSSFP and SPGRE in multicenter settings.
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