Abstract #1136

Deep Learning-Optimized GRASP-Pro Reconstruction for Highly-Accelerated DCE-MRI

Haoyang Pei^1,2,3 and Li Feng^1,2

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ³Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, United States

Synopsis

Keywords: Sparse & Low-Rank Models, Image Reconstruction, DCE-MRI

Motivation: Even though GRASP-Pro has been proven to be effective in various applicatioins, it presents a significant challenge at high acceleration/frame rates as the performance of standard GRASP-Pro reconstruction can be significantly compromised under this condition.

Goal(s): This work aim to develop self-supervised learning-optimized GRASP-Pro reconstruction approach to improve 4D dynamic MRI reconstruction at high acceleration rates.

Approach: This approach employs self-supervised learning to first reconstruct high-quality low-resolution images to estimate a accurate temporal basis. Subsequently, self-supervised learning is applied again to reconstruct full-resolution 4D dynamic images using a low-rank subspace-assisted network training.

Results: This approach demostrates improved reconstruction quality for highly-accelerated 4D DCE-MRI.

Impact: The proposed self-supervised learning-optimized GRASP-Pro enables efficient and reliable 4D MRI reconstruction. This improves reconstruction quality for highly-accelerated 4D dynamic MRI, which is useful in various applications such as DCE-MRI.

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.