MR Image Reconstruction Using a Combination of Compressed Sensing and Partial Fourier Acquisition: ESPReSSo

作者：Kuestner T; Wuerslin C; Gatidis S; Martirosian P; Nikolaou K; Schwenzer Nf; Schick F; Yang B; Schmidt H

来源：IEEE Transactions on Medical Imaging, 2016, 35(11): 2447-2458.

DOI：10.1109/TMI.2016.2577642

• Magnetic resonance imaging
• Compressive sensing
• Image acquisition
• Image enhancement/restoration

A Cartesian subsampling scheme is proposed incorporating the idea of PF acquisition and variable-density Poisson Disc (vdPD) subsampling by redistributing the sampling space onto a smaller region aiming to increase k-space sampling density for a given acceleration factor. Especially the normally sparse sampled high-frequency components benefit from this sampling redistribution, leading to improved edge delineation. The prospective subsampled and compacted k-space can be reconstructed by a seamless combination of a CS-algorithm with a Hermitian symmetry constraint accounting for the missing part of the k-space. This subsampling and reconstruction scheme is called Compressed Sensing Partial Subsampling (ESPReSSo) and was tested on in-vivo abdominal MRI datasets. Different reconstruction methods and regularizations are investigated and analyzed via global (intensity-based) and local (region-of-interest and line evaluation) image metrics, to conclude a clinical feasible setup. Results substantiate that ESPReSSo can provide improved edge delineation and regional homogeneity for multidimensional and multi-coil MRI datasets and is therefore useful in applications depending on well-defined tissue boundaries, such as image registration and segmentation or detection of small lesions in clinical diagnostics.

• 出版日期2016-11