Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: High-quality MRI acquisitions often require long scan times causing motion artifacts and image distortions. Accelerated scan times are essential to improve efficiency and reduce image distortions.
Goal(s): The goal of this work is to interpolate intermediate unacquired slices from an accelerated acquisition of acquired slices.
Approach: Our proposed method was pretrained on video frames, fine-tuned on 221 randomly selected healthy subjects from the OASIS-1 Dataset, and tested on a cohort of 10 subjects.
Results: The proposed model demonstrates the ability to interpolate intermediate MRI slices with relatively high similarity to the ground-truth.
Impact: This work serves as a proof of concept, in which our optical flow-based deep learning technique has the potential to reduce MRI acquisition times, overcome issues of image distortions, and enhance overall image 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.
Keywords