Meeting Banner
Abstract #4926

Convolutional Neural Network based Stack-of-Star Imaging with Noise and Artifacts Removal

Xinzeng Wang1, Yedaun Lee2,3, Joonsung Lee4, Sagar Mandava5, Ty A. Cashen6, Xucheng Zhu7, and Arnaud Guidon8
1GE Healthcare, Houston, TX, United States, 2Department of Radiology, Haeundae Paik Hospital, Busan, Korea, Republic of, 3Inje University College of Medicine, Busan, Korea, Republic of, 4GE Healthcare, Seoul, Korea, Republic of, 5GE Healthcare, Atlanta, GA, United States, 6GE Healthcare, Madison, WI, United States, 7GE Healthcare, Menlo Park, CA, United States, 8GE Healthcare, Boston, MA, United States

Synopsis

Keywords: Cancer, Image Reconstruction, Liver, PancreasStack of Star acquisition is one of the most frequently used non-Cartesian k-space sampling methods due to its fast speed and robustness to motion. To further reduce the scan time or increase the temporal resolution, Stack of Star is often down-sampled with fewer spokes and advanced sampling patterns, such as golden angle acquisition. However, this makes Stack of Star prone to noise and streak artifacts, limiting the in-plane resolution and degrading diagnostic quality. In this work, we evaluated a deep-learning based stack-of-star method for free-breathing abdominal imaging and it shows improved diagnostic quality.

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.

Click here for more information on becoming a member.

Keywords