Efficient interpolated compressed sensing reconstruction scheme for 3D MRI. Issue 11 (12th September 2018)

• Record Type: Journal Article
• Authors: Datta, Sumit; Deka, Bhabesh
• Is Part Of: IET image processing. Volume 12:Issue 11(2018)
• Page Start: 2119
• Page End: 2127
• Publication Date: 2018-09-12
• Subjects: medical image processing -- biomedical MRI -- interpolation -- compressed sensing -- image reconstruction -- wavelet transforms -- gradient methods; efficient interpolated compressed sensing reconstruction scheme -- 3D MRI -- 3D magnetic resonance imaging -- anatomical structure analysis -- medical imaging modalities -- multiple slice acquisition -- slice select gradient direction -- 2D multislice acquisition -- MRI scan time -- random undersampling -- fast interpolation technique -- missing sample estimation -- highly undersampled slice -- lightly undersampled slice -- multislice CS‐MRI reconstruction technique -- weighted wavelet forest sparsity -- joint total variation regularisation norms -- visual quality performance metric -- visual quantitative performance metric; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5473
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.