GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: Acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both 4x and 8x acceleration.

    Original languageEnglish (US)
    Article number9157643
    Pages (from-to)14303-14310
    Number of pages8
    JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    DOIs
    StatePublished - 2020
    Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
    Duration: Jun 14 2020Jun 19 2020

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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