Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, implicit neural representation
Current parallel imaging techniques for MRI acceleration can still not reliably reconstruct a high-quality image from highly reduced k-space measurements with fewer calibration data. In this study, we applied the new insight of implicit neural representation (INR) to parallel MRI reconstruction. The underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates. These functions were simultaneously learned from the sparsely-acquired k-space itself without additional training data. Thanks to the continuous representation and joint estimation scheme, the proposed method outperforms the existing scan-specific methods, demonstrating its potential for further accelerating the MRI acquisition.
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