An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
This work addresses faster and more accurate MRI reconstruction for medical imaging, though it appears incremental as it builds on existing compressed sensing and deep learning methods.
The paper tackled the problem of accelerating MRI acquisition by reconstructing images from undersampled k-space data, achieving top rankings in the fastMRI challenge with #1 on 8x multi-coil, shared #1 on 4x multi-coil, and #3 on 4x single-coil tracks.
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 adopt deep neural networks 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 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil track. This demonstrates the superior performance and wide applicability of the method.