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Thin slice thickness reconstructions from helical Multi Detector-row CT (MDCT) scanning may suffer from windmill artifacts because of the under-sampling of the data in the z- or detector-row direction (which is essentially a Nyquist sampling issue). There are two strategies for windmill artifacts reduction: one is focusing on the CT system hardware design such as flying focal spot (FFS), the other is committed to correction using algorithms. Recently, numerous algorithms have been proposed to address this issue. One method aims to recover high-resolution images from thick-slice low-resolution images which are without windmill artifacts. Another method is an image domain post-processing method which can suppress windmill artifacts by using prior information such as total variation (TV). However, both two methods blur sharp edges and are unable to recover fine details. In this work, a super-resolution (SR) reconstruction method is developed by combining low rank and TV regularization (LRTV) to improve the z-axis resolution of MDCT in the post processing step. Hence, the SR reconstruction is formulated as an optimization problem which is solved effectively via alternating direction method of multipliers (ADMM). Thereafter, combining the high-resolution image with original reconstructed image, which is affected by windmill artifacts, can obtain a more accurate image. We evaluated our algorithm on Anke 16-slice helical CT scanner. The results demonstrate that the proposed method can achieve better windmill artifacts removal performance than the competing methods and simultaneously preserve fine details.
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