access icon free Efficient interpolated compressed sensing reconstruction scheme for 3D MRI

© The Institution of Engineering and Technology
Received 31/01/2018, Accepted 26/07/2018, Revised 09/05/2018, Published 06/08/2018

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.

Inspec keywords: interpolation; medical image processing; biomedical MRI; image reconstruction; gradient methods; wavelet transforms; compressed sensing

Other keywords: joint total variation regularisation norms; weighted wavelet forest sparsity; lightly undersampled slice; 3D MRI; visual quantitative performance metric; slice select gradient direction; highly undersampled slice; anatomical structure analysis; random undersampling; efficient interpolated compressed sensing reconstruction scheme; 2D multislice acquisition; multislice CS-MRI reconstruction technique; missing sample estimation; medical imaging modalities; 3D magnetic resonance imaging; fast interpolation technique; visual quality performance metric; multiple slice acquisition; MRI scan time

Subjects: Computer vision and image processing techniques; Medical magnetic resonance imaging and spectroscopy; Integral transforms in numerical analysis; Biology and medical computing; Biomedical magnetic resonance imaging and spectroscopy; Interpolation and function approximation (numerical analysis); Patient diagnostic methods and instrumentation; Interpolation and function approximation (numerical analysis); Numerical approximation and analysis; Integral transforms in numerical analysis; Optical, image and video signal processing

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