Keywords: Image Reconstruction, Image Reconstruction
Motivation: Estimating cardiac and respiratory inter-bin deformation fields from 5D motion-resolved free-running data is particularly challenging due to a high level of undersampling.
Goal(s): To address this challenge through an innovative multi-resolution approach to estimate the deformation fields and reconstruct 5D motion-resolved images.
Approach: The approach consists of a sequence of compressed-sensing image reconstructions that iteratively progresses from low to high spatial resolutions, where one lower-resolution iteration’s output is exploited as input for the next higher resolution until target resolution is reached.
Results: Using optimized regularization weights, the proposed approach achieved left-ventricular ejection fraction within a 4% error margin compared the 2D cine.
Impact: This study presents a multi-resolution framework for estimating cardiac and respiratory inter-bin deformation fields aimed at improved motion-resolved whole-heart 5D-imaging. This multi-resolution compressed sensing framework has the potential to accurately estimate deformation fields and reduce compression artefacts.
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