Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder
This addresses the need for faster reconstruction in medical imaging, though it is incremental as it builds on existing autoencoder techniques for a known bottleneck.
The paper tackles real-time dynamic MRI reconstruction by proposing a stacked denoising autoencoder (SDAE) to learn a non-linear mapping from aliased to clean images, achieving reconstruction faster than the data acquisition rate with quality comparable to prior compressed sensing methods.
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.