Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, k-space, motion, artifacts, quality metric
Motivation: Motion correction in MRI predominantly relies on image-based methods and continues to be a challenge. Innovative approaches could harness better motion information latent in k-space (i.e., the measurement space).
Goal(s): Developing a reference-less motion correction pipeline in k-space using deep learning.
Approach: Our k-space motion correction pipeline combines deep learning for motion parameter estimation with model-based image reconstruction. Large datasets were generated through physics-based simulations on 2D brain MRI acquisitions to enhance model training and performance.
Results: Our deep-learning model performs well in motion parameter estimation, even for successive motion events, effectively removing substantial motion artifacts when combined with model-based reconstruction.
Impact: SISMIK, our deep learning model successfully estimates motion parameters in the acquisition space of multi-slice 2D brain MRI. It allows substantial motion artifact removal through a model-based reconstruction approach, which is, by design, free of hallucination artifacts.
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