1. MR Image Reconstruction Using a Combination of Compressed Sensing and Partial Fourier Acquisition: ESPReSSo
- Author
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Fritz Schick, Petros Martirosian, Thomas Küstner, Sergios Gatidis, Nf. Schwenzer, Holger Schmidt, Bin Yang, Christian Würslin, and Konstantin Nikolaou
- Subjects
Adult ,Male ,Computer science ,Image registration ,Image processing ,02 engineering and technology ,Iterative reconstruction ,030218 nuclear medicine & medical imaging ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Abdomen ,Hermitian function ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Computer vision ,Electrical and Electronic Engineering ,Fourier Analysis ,Radiological and Ultrasound Technology ,business.industry ,Sampling (statistics) ,Magnetic Resonance Imaging ,Computer Science Applications ,Compressed sensing ,Female ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Algorithms ,Software - Abstract
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.
- Published
- 2016