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Abstract #5131

Up to four times accelerated musculoskeletal MRI at 0.4T using the CIRIM-network

Daisy van den Berg1, Rosario Varriale2, Fabrizio Ferrando2, Paolo Traverso2, Luca Balbi2, Gustav Strijkers1, and Matthan Caan 1
1Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, Netherlands, 2MRI R&D, Esaote S.p.A, Genoa, Italy

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Low-Field MRI

Motivation: Low-field MRI scanners are gaining attention for being cost-effective, increasing the accessibility of MRI worldwide. Still, the scan times of low-field MRI are high, necessitating acceleration techniques.

Goal(s): To accelerate 0.4T knee and spine MRI using deep learning reconstruction.

Approach: A neural network (the CIRIM) was trained to reconstruct undersampled data. The undersampling pattern and loss function of the CIRIM were optimized, and different acceleration factors were explored.

Results: The CIRIM successfully reconstructed accelerated knee and spine data. For 2D and 3D images, some minor blurring was seen beyond an acceleration of 3 and 4, respectively.

Impact: Low-field MRI is cost-effective and can therefore increase the worldwide accessibility of MRI. By accelerating imaging using deep learning reconstruction on undersampled data, we realize time-efficient scanning.

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