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Abstract #4051

Zero-Shot Self-Supervised Learning for 2D T2-shuffling MRI Reconstruction

Molin Zhang1, Junshen Xu1, Yamin Arefeen1, and Elfar Adalsteinsson1,2,3
1EECS, MIT, Cambridge, MA, United States, 2Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, 3Institute for Medical Engineering and Science, MIT, Cambridge, MA, United States


Resolving a time series of T2-weighted images from a fast-spin-echo (FSE) sequence with traditional techniques requires long acquisitions, but T2-shuffling enables clinically feasible scan times by combining subspace models, which reduce degrees-of-freedom, with random spatial and temporal undersampling. Supervised machine learning achieves impressive reconstruction, but lack of labeled training data preclude its use in reconstructing signal dynamics. Recent zero-shot-self-supervised-learning (ZSSS) techniques enable high quality structural MRI reconstruction without training data. In this work, we combine ZSSS with the subspace model to further accelerate 2D T2-shuffling acquisitions. Our ZSSS-subspace models show significant reconstruction improvement in comparison to standard T2-shuffling in simulation.

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