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Efficient interpolated compressed sensing reconstruction scheme for 3D MRI
Author(s) -
Datta Sumit,
Deka Bhabesh
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5473
Subject(s) - undersampling , compressed sensing , interpolation (computer graphics) , computer science , real time mri , artificial intelligence , magnetic resonance imaging , iterative reconstruction , computer vision , multislice , reconstruction algorithm , algorithm , pattern recognition (psychology) , nuclear magnetic resonance , image (mathematics) , physics , radiology , medicine
3D magnetic resonance imaging (3D MRI) is one of the most preferred medical imaging modalities for the analysis of anatomical structures where acquisition of multiple slices along the slice select gradient direction is very common. In 2D multi‐slice acquisition, adjacent slices are highly correlated because of very narrow inter‐slice gaps. Application of compressed sensing (CS) in MRI significantly reduces traditional MRI scan time due to random undersampling. The authors first propose a fast interpolation technique to estimate missing samples in the k‐space of a highly undersampled slice (H‐slice) from k‐space (s) of neighbouring lightly undersampled slice/s (L‐slice). Subsequently, an efficient multislice CS‐MRI reconstruction technique based on weighted wavelet forest sparsity, and joint total variation regularisation norms is applied simultaneously on both interpolated H and non‐interpolated L‐slices. Simulation results show that the proposed CS reconstruction for 3D MRI is not only computationally faster but significant improvements in terms of visual quality and quantitative performance metrics are also achieved compared to the existing methods.

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