Keywords: AI/ML Image Reconstruction, Image Reconstruction
Motivation: State-of-the-art motion-resolved 4D MRI techniques lack sufficient spatial resolution and efficient acquisition and reconstruction for application in clinical practice.
Goal(s): To develop HD-Movienet, a deep learning-based method to efficiently acquire and reconstruct 4D MRI with approximately 1mm isotropic resolution using 3D radial acquisitions.
Approach: HD-Movienet uses accelerated half-spoke (UTE) and full-spoke (T1-weighted) 3D radial kooshball acquisition and image-time-coil deep learning 4D reconstruction without k-space data consistency.
Results: HD-Movienet can enable 4D MRI with isotropic 1.1mm resolution, 4 minutes of scan time, and reconstruction of less than 7 seconds to image patients with lung tumors.
Impact: Deep learning-based HD-Movienet reconstruction enables motion-resolved 4D MRI technique with isotropic 1.1mm resolution, 4 minutes of scan time, and reconstruction of less than 7 seconds for robust radiation-free imaging of patients with mobile tumors.
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