This work introduces a physics-based deep learning reconstruction that can enable real-time (free-breathing and ungated) spiral 2D bSSFP functional cardiac MRI on a 0.55T scanner. The proposed method extends the concept of a deep image prior and does not require prior training on reference data. In addition, GROG is used to reduce the computational complexity of the forward model calculation by shifting spiral k-space data to their nearest Cartesian grid points, allowing use of FFT rather than NUFFT operations. Results are demonstrated in healthy subjects with functional measurements compared to gold standard acquisitions at 1.5T.
This abstract and the presentation materials are available to members only; a login is required.