Compressive sensing magnetic resonance imaging reconstruction based on nonlocal autoregressive modeling

Xi Wu; Jin Wang; Wei Xu; Qing Zhu

doi:10.1117/12.2503050

9 August 2018 Compressive sensing magnetic resonance imaging reconstruction based on nonlocal autoregressive modeling

Xi Wu, Jin Wang, Wei Xu, Qing Zhu

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108063F (2018) https://doi.org/10.1117/12.2503050
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Magnetic resonance imaging (MRI) is a revolutionary tool in medical imaging, which plays an important role in clinical diagnosis. Compressive sensing (CS) has shown great potential in significantly reducing the acquisition time of MRI scanning. However, how to improve the reconstruction quality with limited k-space data is still a challenge. MRI images are featured with large area of smooth regions, sharp edges and rich textures. Motivated by these facts, we propose a nonlocal autoregressive model (NAM) for CS MRI reconstruction. Nonlocal similarity between image patches is exploited as a regularization term to constrain the nonlocal feature in MRI images, which is very helpful in preserving edge sharpness. While an autoregressive regularization term is employed to describe the linear correlation between neighboring pixels, which preserves more spatial details. Different from previous work, we reconstruct an MRI image patch utilizing correlations both among patches and among neighboring pixels. Extensive experimental results demonstrate that our method outperforms mainstream methods in MRI reconstruction in terms of both subjective quality and objective quality.

Citation Download Citation

Xi Wu, Jin Wang, Wei Xu, and Qing Zhu "Compressive sensing magnetic resonance imaging reconstruction based on nonlocal autoregressive modeling", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108063F (9 August 2018); https://doi.org/10.1117/12.2503050

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KEYWORDS

Magnetic resonance imaging

Autoregressive models

Compressed sensing

Neuroimaging

Reconstruction algorithms

Detection theory

Medical imaging

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