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Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline

Dietlmeier, Julia orcid logoORCID: 0000-0001-9980-0910, Garcia-Cabrera, Carles orcid logoORCID: 0000-0001-8139-9647, Hashmi, Anam, Curran, Kathleen M. orcid logoORCID: 0000-0003-0095-9337 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2023) Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline. In: MICCAI STACOM CMRxRecon challenge workshop, 12 Oct 2023, Vancouver, Canada. (In Press)

Abstract
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
Subjects:Computer Science > Artificial intelligence
Computer Science > Image processing
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:Springer
Copyright Information:© 2023
Funders:SFI 18/CRT/6183, SFI 12/RC/2289_P2
ID Code:29095
Deposited On:12 Oct 2023 14:04 by Julia Dietlmeier . Last Modified 12 Oct 2023 14:04
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