Keywords: AI/ML Image Reconstruction, DSC & DCE Perfusion, DISCO-Star, DL Stack-of-stars
Motivation: Free breathing DCE imaging utilizes stack-of-stars sampling, which can lead to streak artifacts and noise reduction when too few spokes are used.
Goal(s): Our goal was to validate application of deep learning to 3D DISCO-Star DCE imaging in the abdomen via image quality assessment and noise characterization.
Approach: DL and conventionally reconstructed images were assessed by two radiologists across different IQ attributes. Noise characteristics were evaluated by calculation of total variation. AUC was also calculated.
Results: The radiologists preferred DL across many of the IQ attributes, with noticeably lowered noise and decreased streaks in DL images. AUC was similar between the two reconstructions.
Impact: The application of DL to DISCO-Star DCE imaging provides enhanced diagnostic quality, with reduced streaking, higher SNR, and better in-plane resolution. This has the potential to improve care for abdominal patients who have trouble holding their breath.
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