摘要
低剂量CT(Computed Tomography)因其大大降低了辐射剂量而广泛用于现代医疗中。然而,随着辐射剂量的减少,扫描过程中投影数据受到随机噪声的污染,导致重建图像中存在明显的条形伪影。为解决上述问题,该文提出一种基于局部模糊熵的自适应恢复算法。该算法在基于统计信息的各向异性滤波器的基础上,利用局部模糊熵来判断边缘和平滑区域。新的扩散模型能有效地控制扩散程度,大大提高了扩散速度,达到快速恢复投影数据的目的。仿真实验和实际数据试验结果表明,基于局部模糊熵的自适应恢复方法能够得到高信噪比的重建图像,且与传统算法相比,缩短了对投影数据的处理时间,从而减轻了辐射对患者的危害。
Low-dose Computed Tomography （CT） is widely used in modern medical practice for its advantage on reducing the radiation dose to patients. However, excessive quantum noise is present in low dose X-ray imaging along with the decrease of the radiation dose; thus, there are obvious streak-like artifacts in reconstructed images. For this problem, an adaptive restoration algorithm based on local fuzzy entropy is proposed in this paper. This new algorithm modifies the statistical information based anisotropic filter, distinguishing edges and smooth areas by a local fuzzy entropy. The new diffusion model can effectively control the diffusion degree, thus improve greatly the diffusion rate to achieve the purpose of rapid recovery of the projection data, Simulation results show that higher signal-to-noise ratio reconstructed images can be obtained by the new adaptive diffusion algorithm. In addition, compared with conventional algorithm, the proposed algorithm shortens processing time in projection domain and thereby reduces the hazards of radiation to patients.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第6期1421-1427,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61071192
61271357)
山西省自然科学基金(2009011020-2)
山西省省研究生优秀创新项目(20123098)
山西省高等学校优秀青年学术带头人支持计划资助课题
