A Divide-and-Conquer Approach to Compressed Sensing MRI
This work addresses image quality issues in MRI reconstruction for medical imaging, though it is incremental as it builds on existing CS-MRI methods with a novel decomposition approach.
The paper tackles the problem of compressed sensing MRI reconstruction by addressing the non-uniform energy distribution in k-space, which often leads to loss of high-frequency details. The proposed divide-and-conquer framework decomposes k-space into subspaces, reconstructs them individually, and fuses the results, achieving competitive quantitative performance and qualitative improvements over state-of-the-art methods.
Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In traditional CS-MRI inversion methods, the fact that the energy within the Fourier measurement domain is distributed non-uniformly is often neglected during reconstruction. As a result, more densely sampled low-frequency information tends to dominate penalization schemes for reconstructing MRI at the expense of high-frequency details. In this paper, we propose a new framework for CS-MRI inversion in which we decompose the observed k-space data into "subspaces" via sets of filters in a lossless way, and reconstruct the images in these various spaces individually using off-the-shelf algorithms. We then fuse the results to obtain the final reconstruction. In this way we are able to focus reconstruction on frequency information within the entire k-space more equally, preserving both high and low frequency details. We demonstrate that the proposed framework is competitive with state-of-the-art methods in CS-MRI in terms of quantitative performance, and often improves an algorithm's results qualitatively compared with it's direct application to k-space.