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To increase the flexibility and scalability of deep neural networks for imagereconstruction, a framework is proposed based on bandpass filtering. For manyapplications, sensing measurements are performed indirectly. For example, inmagnetic resonance imaging, data are sampled in the frequency domain. Theintroduction of bandpass filtering enables leveraging known imaging physicswhile ensuring that the final reconstruction is consistent with actualmeasurements to maintain reconstruction accuracy. We demonstrate this flexiblearchitecture for reconstructing subsampled datasets of MRI scans. The resultinghigh subsampling rates increase the speed of MRI acquisitions and enable thevisualization rapid hemodynamics.

Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering