Keywords: AI/ML Image Reconstruction, Acquisition Methods
Motivation: Fourier transform based quantitative MRI (qMRI) relies on decoupled spatial and physical encoding, which limits scan efficiency, thus impedes clinical adoption of qMRI.
Goal(s): A new framework was explored to learn optimally efficient spatial-physical encoding purely from data.
Approach: An end-to-end model was developed for contemporary spatial-physical acquisition and reconstruction. The model was learnt with large-scale pretraining using self-supervised objective and unlimited natural image data.
Results: Results demonstrated such reconstruction model is capable of untangling mixed spatial-physical encoding, and robust to zero-shot transfer from natural image to medical data. With joint learning framework, reconstruction automatically guides the discovery of optimal acquisition.
Impact: Large-scale pretrained end-to-end model can be an alternative to long standing Fourier Transform foundation for qMRI. The new capability of contemporary spatial-physical encoding opens up new possibility to push current speed limit of qMRI.
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