Speed is often claimed as a key advantage of deep learning (DL) for undersampled parallel MRI reconstruction. However, leading DL methods require repeated application of the MR acquisition model and its adjoint, just as in conventional iterative methods. This work investigates the feasibility and robustness of model-free DL reconstruction, which has the potential to be much faster. Results in varied patient cases of increasing pathological rarity demonstrate that while model-free DL can reasonably reconstruct anatomies similar to those seen in training, performance can degrade drastically in more challenging situations.
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