Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction
This work addresses real-time medical imaging for cardiac diagnostics, representing an incremental improvement by incorporating a-priori knowledge into existing compressive sensing frameworks.
The paper tackles dynamic 3D MRI reconstruction by decomposing matrices into low-rank and sparse components using a-priori knowledge, achieving superior reconstruction quality for 4D cardiac MR image sequences from highly under-sampled data compared to state-of-the-art methods.
It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-rank and a Sparse component from compressive measurements. When applied to the problem of construction of 4D Cardiac MR image sequences in real-time from highly under-sampled $k-$space data, our proposed method achieves superior reconstruction quality compared to the other state-of-the-art methods.