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Abstract #5150

K-space Interpolation using Deep Koopman Autoencoders.

Wassim Ben Salah1,2, Sarah McElroy1,2,3, Jonathan Shapey2, Sebastien Ourselin2, Christos Bergeles2, and Radhouene Neji1,2
1Imaging physics and engineering research department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Surgical & Interventional Engineering research department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom

Synopsis

Keywords: AI/ML Image Reconstruction, Parallel Imaging, Deep Koopman, k-space interpolation, accelerated imaging, nonlinear GRAPPA

Motivation: Reconstruction of accelerated MRI acquisitions is crucial, particularly for parallel imaging, which is widely used in the clinic.

Goal(s): To develop an interpretable neural network for the reconstruction of accelerated MRI data with reduced noise and improved image quality.

Approach: A Deep Koopman autoencoder was developed with two non-linear layers for the encoder/decoder and a linear layer for interpolation in the latent space, and compared to GRAPPA and RAKI in two brain scans.

Results: The approach results in improved qualitative and quantitative reconstruction results compared to GRAPPA. Compared to RAKI, visual inspection shows improved sharpness, albeit with a slightly higher residual noise.

Impact: This work introduces an interpretable neural network for k-space interpolation, enabling good reconstruction quality and offering avenues for extensions to enable autoencoder-based scan-specific denoising.

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