Keywords: Signal Representations, Cardiovascular, Cardiac binning, respiratory binning, motion identification, variational autoencoder (VAE), multidimensional quantitative imaging
Motivation: Respiratory and cardiac motion identification is challenging with changing contrast weightings for self-gated multidimensional techniques like MR multitasking.
Goal(s): To guide VAE latent vector constraints design for representing relaxation and motion.
Approach: We evaluated VAE representational fidelity for 16 combinations of constraints on T1/T2 relaxation, cardiac, and respiratory latent dimensions.
Results: The results demonstrate that nonlinear T1/T2 relaxation representations and cardiac phase representations improve VAE performance.
Impact: Latent space design is important for VAE learning in multidimensional cardiac imaging, suggesting avenues for better self-gated cardiac and respiratory binning.
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