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Abstract #2273

Deep Learning Accelerated 3D SPACE DIXON for Improved Fat Suppression in Musculoskeletal MRI

Jan Vosshenrich1,2, Mahesh Keerthivasan3, Marcel Dominik Nickel4, Gregor Koerzdorfer3, Mary Bruno1, and Jan Fritz1
1Department of Radiology, NYU Langone School of Medicine, New York, NY, United States, 2Department of Radiology, University Hospital Basel, Basel, Switzerland, 3MR R&D Collaborations, Siemens Medical Solutions USA, Malvern, PA, United States, 4Siemens Healthcare GmbH, MR Application Predevelopment, Erlangen, Germany

Synopsis

Keywords: Whole Joint, Joints

Motivation: The application of 3D SPACE-DIXON techniques for musculoskeletal MRI has advantages in terms of fat suppression capacities, but drawbacks with regard to substantially longer acquisition times compared with SPAIR fat suppressed 3D SPACE.

Goal(s): To reduce the acquisition time of 3D SPACE-DIXON to that of 3D SPACE-SPAIR while maintaining superior fat suppression capabilities.

Approach: Six-fold CAIPIRINHA acceleration was combined with a deep learning-based image reconstruction algorithm to decrease the acquisition time and maintain image quality.

Results: Image quality parameters in volunteer scans were rated equivalent to the conventional 3D SPACE-DIXON sequence and superior to the 3D SPACE-SPAIR sequence.

Impact: We present a 3D SPACE-DIXON technique with improved fat suppression performance and similar acquisition time compared with 3D SPACE-SPAIR for musculoskeletal 3D MRI. Preliminary in vivo results indicate the clinical utility of this technique for proton density-weighted 3D MRI.

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