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End-to-End Variational Networks for Accelerated MRI Reconstruction

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arxiv 2004.06688 v2 pith:EEBW5VZX submitted 2020-04-14 eess.IV cs.CV

Anuroop Sriram , Jure Zbontar , Tullie Murrell , Aaron Defazio , C. Lawrence Zitnick , Nafissa Yakubova , Florian Knoll , Patricia Johnson This is my paper · ORCID
classification eess.IVcs.CV
keywords methodsacquiringend-to-endimagingproblemreconstructionvariationalaccelerated
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The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). While the combination of these methods has the potential to allow much faster scan times, reconstruction from such undersampled multi-coil data has remained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset for both brain and knee MRIs.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction

    eess.IV 2024-11 conditional novelty 6.0 of 10

    ADOBI combines a pretrained diffusion bridge with adaptive coil sensitivity calibration, delivering measurement-consistent blind parallel MRI reconstruction in 5 to 10 steps.

  2. Enhancing and Accelerating Brain MRI through Deep Learning Reconstruction Using Prior Subject-Specific Imaging

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A deep learning framework using deep registration and a transformer enhancer improves prior-informed brain MRI reconstruction quality and speed.

  3. Fast MRI of bones in the knee -- An AI-driven reconstruction approach for adiabatic inversion recovery prepared ultra-short echo time sequences

    physics.med-ph 2025-06 conditional novelty 4.0 of 10

    An AI denoising network embedded in iterative reconstruction reduces 3D IR-UTE knee MRI scan time from 30 minutes to 2.5-10 minutes with preserved image quality.

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