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Abstract #2770

Motion-Guided Deep Image Prior for 3D Real-Time Cine (M-DIP-3D)

Chong Chen1, Marc Vornehm1,2,3, Muhammad Ahmad Sultan1, Syed Murtaza Arshad1, Yuchi Han4, and Rizwan Ahmad1
1Biomedical Engineering, The Ohio State University, Columbus, OH, United States, 2Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 3Siemens Healthineers AG, Erlangen, Germany, 4Internal Medicine, The Ohio State University, Columbus, OH, United States

Synopsis

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

Motivation: Three-dimensional real-time (3D-RT) cardiovascular magnetic resonance imaging (CMR) requires extremely high acceleration rates, which are not feasible using existing methods.

Goal(s): Develop an unsupervised reconstruction method for 3D-RT cine to capture beat-to-beat variations in the cardiac function.

Approach: Our method, termed M-DIP-3D, builds upon the deep image prior (DIP) framework and incorporates manifold learning to model motion and content variation.

Results: For a 3D cine MRXCAT phantom with both regular and irregular beats, M-DIP-3D effectively captures the beat dynamics. We also demonstrate the feasibility of M-DIP-3D for ferumoxytol-enhanced 3D-RT cine imaging in a healthy subject and a patient.

Impact: The proposed method, M-DIP-3D, facilitates 3D real-time cine imaging from highly undersampled data. By directly learning the underlying motion and content variation manifold, M-DIP-3D produces images with minimal motion blur and real-time dynamics.

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Keywords