Hankel-based data preparation method for radial MRI artifact removal from undersampled zero-filled images

Motivation: Hankel-based reconstruction distorts the image's center less than its periphery. This prompts us to examine Hankel-based reconstruction for neural network training data preparation. Goal(s): To train the model-agnostic neural network on Hankel-based reconstruction data to improve image center reconstruction. Approach: A neural network trained on Hankel-based reconstruction data was compared to an equivalent network trained on NUFFT-based reconstruction data. Results: In the context of radial dynamic imaging, where the ROI can be placed in the center of the image, our approach achieved better results than when using NUFFT-based data preparation for reconstruction of undersampled radial data. Impact: This study might influence dynamic radial-MRI reconstruction. Our data preparation for training and testing the network improved cardiac-MRI qualitative outcomes, especially in the heart region. The radial-MRI society may find the proposed solution appealing when paired with DL-based approaches.