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Abstract #4626

Robust Plug-and-Play Methods for highly Accelerated Non-Cartesian MRI Reconstruction: A reproducible benchmark.

Pierre-Antoine Comby1, Matthieu Terris2, Alexandre Vignaud1, and Philippe Ciuciu1
1CEA/Neurospin, Paris, France, 2Inria Saclay, Paris, France

Synopsis

Keywords: Image Reconstruction, AI/ML Image Reconstruction, Plug-and-Play Denoising

Motivation: Non-Cartesian MRI scans allow for faster scan time and high-resolution, but requires expensive reconstruction. Traditional algorithms delivers suboptimal results and deep-learning methods have improved image quality but are expensive to train and prone to overfitting.

Goal(s): Propose a novel algorithm for multicoil, non-Cartesian MRI reconstruction by leveraging Plug-and-Play (PnP) methods and Half-Quadratic Splitting (HQS), with better reconstruction quality, stability, and generalization.

Approach: 1)Boost the learned denoiser by preprocessing fastMRI dataset.
2) Generalized preconditioned PGD algorithm to HQS scheme.

Results: Preconditioning improves image quality, with HQS providing the best. At 16x acceleration factor, all PnP methods outperform NCPDNet and variational approaches without further finetuning.

Impact: The proposed algorithm's improved image quality and accelerated reconstruction times at a minimal cost, and generalizes to any sampling pattern and acceleration factor.

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Keywords