An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

IEEE Access, vol. 8, pp. 204825–204838

Abstract

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.

Authors 12

1. Nicola Pezzotti

   Philips (Netherlands)

   Affiliation as printed

   Philips Research, Eindhoven, The Netherlands

   Philips,

2. Sahar Yousefi Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, ZA, The Netherlands

3. Mohamed S. Elmahdy Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, ZA, The Netherlands

4. Jeroen Hendrikus Fransiscus Van Gemert

   Philips (Netherlands)

   Affiliation as printed

   Philips Healthcare, Best, The Netherlands

5. Christophe Schülke

   Philips (Germany)

   Affiliation as printed

   Philips Research, Hamburg, Germany

6. Mariya Doneva

   Philips (Germany)

   Affiliation as printed

   Philips Research, Hamburg, Germany

7. Tim Tolker‐Nielsen

   Philips (Germany)

   Affiliation as printed

   Philips Research, Hamburg, Germany

8. Sergey Kastryulin
   Affiliation as printed

   Philips Research, Moscow, Russia

9. Boudewijn P. F. Lelieveldt Aachen

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, The Netherlands

   Leiden University Medical Center, Leiden, ZA, The Netherlands

10. Matthias J.P. van Osch Aachen

    Leiden University Medical Center

    Affiliation as printed

    Leiden University Medical Center, Leiden, ZA, The Netherlands

11. Elwin de Weerdt

    Philips (Netherlands)

    Affiliation as printed

    Philips Healthcare, Best, The Netherlands

12. Marius Staring Aachen

    Leiden University Medical Center · Delft University of Technology

    Affiliation as printed

    Delft University of Technology, Delft, The Netherlands

    Leiden University Medical Center, Leiden, ZA, The Netherlands