Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, AI/ML Super-resolution
Motivation: Faster MRI is needed for MRI-guided interventional procedures, MRI-guided radiation therapy, and to reduce the time and economic cost-burden of MRI on health systems.
Goal(s): To test the limits of accelerated MRI acquisition by compounding multiple techniques simultaneously in a method we term ‘ultra-acceleration’.
Approach: We developed an ultra-acceleration model based on AI to simultaneously reconstruct and apply super-resolution at 50× and 70× ultra-acceleration.
Results: The normalised mean-square-error (mean ± standard deviation) was 0.056 ± 0.009 and 0.065 ± 0.011 for 50× and 70× ultra-acceleration respectively. Ultra-acceleration produced images with high visual quality and minimal aliasing.
Impact: Our work demonstrates compounding acceleration techniques can shorten the acquisition time of MRI by over 50 times. This finding is a step towards real-time volumetric MRI for interventional guidance.
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