Mitigating undersampling errors in MR fingerprinting by sequence optimization

Purpose: To develop a method for MR Fingerprinting (MRF) sequence optimization that takes both the applied undersampling pattern and a realistic reference map into account. Methods: A predictive model for the undersampling error leveraging on perturbation theory was exploited to optimize the MRF flip angle sequence for improved robustness...

Recurrent Inference Machines for accelerated MRI acquisition trained using simulated undersampled k-space data

Recently, many advancements have been made in accelerated MRI reconstruction with the use of neural networks. Such deep learning methods learn a suitable MRI prior distribution from large sets of training data. For MRI images acquired with an uncommon scanning sequence, large datasets required for training are not available. Additionally, deep...