Exploiting similarity in adjacent slices for compressed sensing MRI (original) (raw)
2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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Region of Interest Compressed Sensing MRI
Journal of the Indian Institute of Science, 2014
Compressed Sensing (CS) based Magnetic Resonance Imaging (MRI) reconstruction relies on data sparsity. Region of Interest Compressed Sensing (ROICS) is based on the hypothesis that superior CS performance can be obtained by limiting the sparsity objective and data consistency in CS to a Region of Interest (ROI). This relaxation is justified in most applications where the anatomy of interest such as the heart, has surrounding structures, typically not used for further analyses. ROICS has been proposed as an extension of CS that is ROI weighted CS. Current work demonstrates the implementation of ROICS for the first time on MR cardiac and brain data. Reconstructed images and performance evaluation metrics show that ROICS technique performs better than conventional CS technique. CS and Parallel Imaging (PI) are widely used to reduce MRI scan time and their combination yields better performance than used individually. The proposed method also implements the combination of ROICS and SENSitivity Encoding (SENSE), which applies weighted CS to a particular ROI, and the resulting output is then reconstructed using SENSE for arbitrary k-space. Proposed ROICS-PI performs better as compared to PI and CS + PI.
Compressed Sensing MRI: A Review
Critical Reviews in Biomedical Engineering, 2013
Compressed sensing (CS) is a mathematical framework that reconstructs data from highly undersampled measurements. To gain acceleration in acquisition time, CS has been applied to MRI and has been demonstrated on diverse MRI methods. This review discusses the important requirements to qualify MRI to become an optimal application of CS, namely, sparsity, pseudo-random undersampling, and nonlinear reconstruction. By utilizing conapplication of CS. In this paper, Section I introduces the fundamentals of CS and the idea of CS as applied to MRI. The requirements for application of CS to MRI is discussed in Section II, while the various acquisition techniques, reconstruction techniques, the advantages of combining CS and parallel imaging, and sampling mask design problems are discussed in Section III. Numerous applications of CS in MRI due to its ability to improve imaging speed are reviewed in section IV. Clinical evaluations of some of the CS applications recently published are discussed in Section V. Section VI provides information on available open source software that could be used for CS implementations.
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