Computer Science ›› 2017, Vol. 44 ›› Issue (9): 308-314.doi: 10.11896/j.issn.1002-137X.2017.09.058

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Compressed Sensing Recovery Algorithm for Region of Interests of MRI/MRA Images Based on NLTV and NESTA

ZHAO Yang, WANG Wei, DONG Rong, WANG Jing-shi and TANG Min   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Compressed sensing (CS) is a novel developed theoretical framework for information acquisition and proces-sing.Taking advantages of the inherent sparsity or compressibility in real world signals,CS can collect compressed data at the sampling rate much lower than that needed in Shannon’s theorem.CS is used to medical imaging techniques to accelerate the scanning speed of MRI/MRA,improve the scanning efficiency and alleviate the patients’ suffering.The flow chart of our NLTV-ROI-NESTA algorithm is as follows.The nonlocal total variation (NLTV) is applied to overcome the disadvantages of the traditional TV for its edge blurring and stepstairs effects.An improved NESTA algorithm is used to reconstruct the region of interests (ROIs) for MRI/MRA images accurately and fast to maintain or enhance the details of the low-contrast vessels.Three indices:peak signal to noise rate (PSNR),structural similarity index (SSIM) and relative l2 norm error (RLNE) are adopted to compare the reconstruction performances and clinical value of the ordinary CS-MRI algorithms and our ROI-CS-MRI algorithm qualitatively and quantitatively.Experimental results demonstrate that the proposed NLTV-ROI-NESTA algorithm is superior in reconstruction accuracy and detail features when the undersampled ratio changed from 10% to 50%,which can be extended and widely used in rapid medical imaging technology.

Key words: Compressed sensing,Image recovery,Nonlocal total variation,NESTA algorithm,Region of interests

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