Using Reinforcement Learning To Personalize AI-accelerated MRI Scans
What the research is: A method leveraging reinforcement learning to improve AI-accelerated magnetic resonance imaging (MRI) scans. Experiments using the fastMRI data set created by NYU Langone show that our models significantly reduce reconstruction errors by dynamically adjusting the sequence of k-space measurements, a process known as active MRI acquisition. These k-space measurements are the building blocks of an MRI scan, the raw data from which images are reconstructed. The fastMRI project recently demonstrated that AI can reconstruct diagnostically useful scans from undersampled k-space data, and that these scans are interchangeable with fully sampled scans. That work used a predetermined sequence of these…
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