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Abstract #0121

Generative MR Multitasking: Bridging real-time and gated imaging with complex-valued cardiac encoding and neural subspace representations

Xinguo Fang1,2 and Anthony G Christodoulou1,2
1Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States, 2Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Signal Representations, AI/ML Image Reconstruction

Motivation: To improve representation of cardiac and respiratory motion for multidimensional imaging and bridge the divide between gating and real-time imaging.

Goal(s): To develop continuous representations of respiratory/cardiac motion, and extend the standard cardiac phase paradigm to a complex-harmonic inspired cardiac phase (timing) and “amplitude” (beat-to-beat variability) paradigm.

Approach: We propose a generative-subspace MR Multitasking framework using a conditional variational autoencoder (CVAE) for an implicit neural representation of continuous-time multidimensional temporal subspaces.

Results: The CVAE subspace produced higher SNR, continuous motion, and eliminated eddy current artifacts. The complex cardiac representation simultaneous allowed both “real-time” beat-by-beat imaging and average-cycle “gated” imaging.

Impact: This framework bridges traditional gating and real-time imaging approaches, and provides a flexible, scalable framework for multidimensional imaging. The complex-harmonic cardiac representation preserves both cardiac timing (phase) and beat-to-beat variability (amplitude), which may especially have future impact for arrhythmia patients.

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Keywords