The dataset contains about 17424 volumes; and multiple slices in each volume. In total we have used about 100,000 images for training the SDAE's...Unfortunately, for these datasets, the fully sampled k-space scans are not available. Therefore the reconstruction results obtained from  is taken as the basis images for comparison.
Using time dependent images reconstructed from some unknown algorithm and comparing it with the temporal reconstruction capability of Deep Neural Networks is a first step and it may set the comparison with a CS reconstruction in an unfair light. But the next step ought to be obvious :-) Thank you Angshul for this provocative preprint (provocative because deep neural nets have very little theory associated with them whereas there is more of it for compressive sensing and MRI)
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder by Angshul Majumdar
In this work we address the problem of real-time dynamic MRI reconstruction. There are a handful of studies on this topic; these techniques are either based on compressed sensing or employ Kalman Filtering. These techniques cannot achieve the reconstruction speed necessary for real-time reconstruction. In this work, we propose a new approach to MRI reconstruction. We learn a non-linear mapping from the unstructured aliased images to the corresponding clean images using a stacked denoising autoencoder (SDAE). The training for SDAE is slow, but the reconstruction is very fast - only requiring a few matrix vector multiplications. In this work, we have shown that using SDAE one can reconstruct the MRI frame faster than the data acquisition rate, thereby achieving real-time reconstruction. The quality of reconstruction is of the same order as a previous compressed sensing based online reconstruction technique.
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