Permuted cubes wavelet thresholding for mask-sensed MRI

In this work we present a novel inpainting algorithm to gain reduced acquisition time and high quality data reconstruction for MRI applications. We analyzed MRI recordings of two synthetically generated and of one real measured data set. On the basis of the proposed mask-based sampling trajectories, patients only have to spend a fraction of the recording time in the MRI. Especially, in the range of high k-space coefficient reduction, the reconstruction quality of our Permuted Cubes Wavelet Thresholding (PCWT) approach can compete with standard data compression-focused methods like JPEG2000 or MPEG-4. In all simulations, the proposed algorithm also outperforms state-of-the-art techniques such as BM3D-MRI with respect to accuracy after data reconstruction. Regarding the quality of the approximated MRI data, we mainly focus on the clear recovery of sharp edges without undesirable artifacts and the identification of tumors within MRI frames.