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Abstract #4500

DL based reconstruction method for an undersampled PROPELLER MRI data

Florintina C1, Sudhanya Chatterjee1, Rohan Patil1, Sajith Rajamani1, and Suresh Emmanuel Joel1
1GE HealthCare, Bangalore, India

Synopsis

Keywords: AI/ML Image Reconstruction, Image Reconstruction, PROPELLER

Motivation: Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) is a popular MRI acquisition scheme used for clinical and research MRI data acquisition due to its robustness to motion. However, it is known to have long scan times.

Goal(s): Reduce scan time for PROPELLER scans to make it feasible for usage in regular clinical settings.

Approach: An unrolled algorithm based deep learning reconstruction method for PROPELLER scans has been proposed, which performs reconstruction at the blade level.

Results: Proposed method has been demonstrated to perform good reconstruction on single coil data for multiple anatomies and contrasts.

Impact: This method has the potential to reduce PROPELLER scan times and make it a popular choice for acquisition in clinical settings.

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