Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
This work addresses faster MRI acquisition for medical imaging, presenting an incremental improvement in compressive sensing methods.
The paper tackled MR image reconstruction from under-sampled k-space data by proposing a method that learns priors using undecimated wavelet transform and a denoising autoencoder, integrated via proximal gradient algorithm, achieving validated improvements in experiments across various sampling conditions.
Compressive sensing is an impressive approach for fast MRI. It aims at reconstructing MR image using only a few under-sampled data in k-space, enhancing the efficiency of the data acquisition. In this study, we propose to learn priors based on undecimated wavelet transform and an iterative image reconstruction algorithm. At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE). The highly redundant and multi-scale input enables the correlation of feature images at different channels, which allows a robust network-driven prior. At the iterative reconstruction, the transformed DAE prior is incorporated into the classical iterative procedure by the means of proximal gradient algorithm. Experimental comparisons on different sampling trajectories and ratios validated the great potential of the presented algorithm.