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

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 spa...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2016-11, Vol.35 (11), p.2447-2458
Main Authors: Kustner, T., Wurslin, C., Gatidis, S., Martirosian, P., Nikolaou, K., Schwenzer, Nf, Schick, F., Yang, B., Schmidt, H.
Format: Article
Language:English
Subjects:
Abdomen - diagnostic imaging
Acceleration
Adult
Algorithms
Coffee
Compressed sensing
Compressive sensing
Electronic mail
Female
Fourier Analysis
Homogeneity
Humans
Image acquisition
Image edge detection
Image enhancement/restoration
Image processing
Image Processing, Computer-Assisted - methods
Image reconstruction
Image segmentation
Imaging
Lesions
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Radiology
Sampled data
Sampling
Young Adult
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Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2016.2577642