Sparse-view computed tomography image reconstruction via a combination of L(1) and SL(0) regularization.

Abstract:

:Low-dose computed tomography reconstruction is an important issue in the medical imaging domain. Sparse-view has been widely studied as a potential strategy. Compressed sensing (CS) method has shown great potential to reconstruct high-quality CT images from sparse-view projection data. Nonetheless, low-contrast structures tend to be blurred by the total variation (TV, L1-norm of the gradient image) regularization. Moreover, TV will produce blocky effects on smooth and edge regions. To overcome this limitation, this study has proposed an iterative image reconstruction algorithm by combining L1 regularization and smoothed L0 (SL0) regularization. SL0 is a smooth approximation of L0 norm and can solve the problem of L0 norm being sensitive to noise. To evaluate the proposed method, both qualitative and quantitative studies were conducted on a digital Shepp-Logan phantom and a real head phantom. Experimental comparative results have indicated that the proposed L1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.

journal_name

Biomed Mater Eng

authors

Qi H,Chen Z,Guo J,Zhou L

doi

10.3233/BME-151437

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

S1389-98

eissn

0959-2989

issn

1878-3619

pii

BME1437

journal_volume

26 Suppl 1

pub_type

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