Semi-PROPELLER Compressed Sensing Image Reconstruction with Enhanced Resolution in MRI


  • Krzysztof Malczewski Poznan University of Technology


Magnetic Resonance Imaging (MRI) reconstruction algorithm using semi-PROPELLER compressed sensing is pre- sented in this paper. It is exhibited that introduced algorithm for estimating data shifts is feasible when super- resolution is applied. The offered approach utilizes compressively sensed MRI PROPELLER sequences and improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Compressed sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. It is shown that the presented approach improves MR spatial resolution in cases when Compressed Sensing (CS) sequences are used. The application of CS in medical modalities has the potential for significant scan time reductions, with visible benefits for patients and health care economics. These methods emphasize on maximizing image sparsity on known sparse transform do- main and minimizing fidelity. This diagnostic modality struggles with an inherently slow data acquisition process. The use of CS to MRI leads to substantial scan time reductions and visible benefits for patients and economic factors. In this report the objective is to combine Super-Resolution image enhancement algorithm with both PROPELLER sequence and CS framework. All the techniques emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The motion estimation algorithm being a part of super resolution reconstruction (SRR) estimates shifts for all blades jointly, emphasizing blade-pair correlations that are both strong and more robust to noise. 

Author Biography

Krzysztof Malczewski, Poznan University of Technology

Faculty of Electronics and Telecommunications


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Image Processing