Deep Learning-Based High Frequency Constrained Fast Image Reconstruction for 4D Cardiac MRI

Motivation: Online image reconstructions for prospectively undersampled cardiac MRI data are noisy as the naïve approach fails to remove undersampling artifacts. Compressed sensing (CS) reconstruction reduces artifacts but is time and memory intensive, making it an offline reconstruction option only. Goal(s): Our aim was to address these constraints by training a Deep Learning (DL) model to obtain high resolution online reconstructions. Approach: We achieved this by implementing a spatiotemporal UNET with a weighted high frequency loss. Results: We found the results of the DL model comparable to the CS reconstruction in image quality, with lower computational cost, making it suitable for online reconstructions. Impact: Our image denoising Deep Learning (DL) model showed similar results to the time consuming, more computationally expensive compressed sensing (CS) reconstruction (gold standard), thus demonstrating its potential for online reconstruction of prospectively undersampled cardiac MRI data.