An adaptive intelligence algorithm for undersampled knee MRI reconstruction

An adaptive intelligence algorithm for undersampled knee MRI reconstruction

Pezzotti, Nicola (Philips Research)
Yousefi, Sahar (Leiden University Medical Center)
Elmahdy, Mohamed S. (Leiden University Medical Center)
van Gemert, Jeroen Hendrikus Fransiscus (Philips Healthcare Nederland)
Schuelke, Christophe (Philips Innovation Services)
Doneva, Mariya (Philips Innovation Services)
Nielsen, Tim (Philips Innovation Services)
Lelieveldt, B.P.F. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Staring, M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)

2020

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.

Deep learning
FastMRI challenge
Image reconstruction
ISTA
MRI

http://resolver.tudelft.nl/uuid:e42bf1dd-ea66-4ae1-b93f-c7d23a6ec9a0

https://doi.org/10.1109/ACCESS.2020.3034287

2169-3536

IEEE Access, 8, 204825-204838

Institutional Repository

journal article

© 2020 Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, B.P.F. Lelieveldt, M. Staring, More Authors