An adaptive intelligence algorithm for undersampled knee MRI reconstruction

Nicola Pezzotti*, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, Boudewijn P.F. Lelieveldt, Marius Staring, More Authors

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

62 Citations (Scopus)
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Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8× accelerated multi-coil, the 4× multi-coil, and the 4× single-coil tracks. This demonstrates the superior performance and wide applicability of the method.

Original languageEnglish
Pages (from-to)204825-204838
Number of pages14
JournalIEEE Access
Publication statusPublished - 2020


  • Deep learning
  • FastMRI challenge
  • Image reconstruction
  • ISTA
  • MRI


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