Keywords: System Imperfections, Gradients
Motivation: Gradient imperfections from eddy currents and trajectory error cause image artifacts. NMR field probes can accurately measure these imperfections to achieve high-quality imaging, but require additional hardware and cost.
Goal(s): Develop an alternative imaging-based approach to estimate gradient imperfections by leverage the encoding capability of modern multi-channel receivers and neural networks for implicit Fourier phase representation.
Approach: Simulated spiral imaging acquisition with eddy current and low-gradient sequences without eddy effects. Trained MLPs to convolutionally represent gradient phase imperfections in k-space as a function of time.
Results: MLP estimates spatio-temporal phase to high accuracy, showing promise for high-order phase estimation without NMR field probes.
Impact: We propose an algorithmic imaging-based alternative approach to field probe for gradient characterization. This enables enhanced image reconstruction for high-slew MRI without external hardware, potentially revolutionizing fast acquisition MRI techniques and broadening their application.
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