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In this work we address the problem of real-time dynamic medical MRI and XRay CT image reconstruction from parsimonious samples Fourier frequency spacefor MRI and sinogram tomographic projections for CT. Today the de factostandard for such reconstruction is compressed sensing. CS produces highquality images (with minimal perceptual loss, but such reconstructions are timeconsuming, requiring solving a complex optimization problem. In this work wepropose to learn the reconstruction from training samples using an autoencoder.Our work is based on the universal function approximation capacity of neuralnetworks. The training time for the autoencoder is large, but is offline andhence does not affect performance during operation. During testing oroperation, our method requires only a few matrix vector products and hence issignificantly faster than CS based methods. In fact, it is fast enough forreal-time reconstruction the images are reconstructed as fast as they areacquired with only slight degradation of image quality. However, in order tomake the autoencoder suitable for our problem, we depart from the standardEuclidean norm cost function of autoencoders and use a robust l1-norm instead.The ensuing problem is solved using the Split Bregman method.

RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction