In this work, we approach the problem of cardiac Magnetic Resonance Imaging (MRI) image reconstruction from undersampled k-space. This is an inherently ill-posed problem leading to a variety of noise and aliasing artifacts if not appropriately addressed. We propose a two-step double-stream processing pipeline that first reconstructs a noisy sample from the undersampled k-space (frequency domain) using the inverse Fourier transform. Second, in the spatial domain we train a denoising GNA-UNET (enhanced by Group Normalization and Attention layers) on the noisy aliased and fully sampled image data using the Mean Square Error loss function. We achieve competitive results on the leaderboard and show that the algorithmic combination proposed is effective in high-quality MRI reconstruction from undersampled cardiac long-axis and short-axis complex k-space data.
Metadata
Item Type:
Conference or Workshop Item (Poster)
Event Type:
Workshop
Refereed:
Yes
Additional Information:
As part of the 26th International Conference on Medical Image
Computing and Computer Assisted Intervention
Uncontrolled Keywords:
Cardiac MRI; Undersampled k-space; Deep Learning; Denoising UNET