摘要
CT扫描中潜在的辐射伤害已越来越受到人们的重视,然而降低扫描剂量会导致成像质量退化,从而影响诊断结果。针对上述问题,提出一种联合卷积稀疏编码与梯度L_(0)范数的三维重建算法。该算法通过频率分解的重建形式对高频成分进行无监督的多尺度在线卷积稀疏编码约束,对低频成分进行梯度L_(0)范数约束,从而实现低剂量CT图像中噪声伪影的抑制与组织细节的保持。此外,卷积稀疏编码中使用三种不同尺度的三维滤波器,可有效适应不同尺度下的特征信息,提高编码能力。腹部CT仿真数据和真实扫描数据的实验结果表明,所提算法在25%常规剂量的重建过程中可以获得噪声伪影少、结构细节对比度高和质量更好的成像效果。
The potential radiation damage in CT scans have been receiving increasing attention.However,reducing the scan dose will degrade the image quality and affect the diagnosis results.Aiming at addressing the above problems,a three-dimensional(3D)reconstruction algorithm combining convolutional sparse coding and gradient L_(0)-norm is proposed herein.The proposed algorithm uses the frequency decomposition reconstruction form to perform unsupervised multiscale online convolution sparse-coding constraints on high-frequency components,and gradient L_(0)-norm constraints on low-frequency components to achieve the suppression and organization of noise artifacts in low-dose CT imaging keep the details.Moreover,three different scales of 3D filter sets are used in convolutional sparse coding,which can effectively adapt to the feature information at different scales and improve the coding ability.The experimental results of abdominal CT simulation data and real-time scan data show that the proposed algorithm can obtain fewer noise artifacts,high contrast in structural details,and better imaging results in the reconstruction process of 25% conventional dose.
作者
亢艳芹
刘进
王勇
强俊
顾云波
陈阳
Kang Yanqin;Liu Jin;Wang Yong;Qiang Jun;Gu Yunbo;Chen Yang(College of Computer and Information,Anhui Polytechnic University,Wuhu,Anhui 241000,China;Key Laboratory of Computer Network and Information Integration,Ministry of Education,Southeast University,Nanjing,Jiangsu 210096,China;Laboratory of Image Science and Technology,Southeast University,Nanjing,Jiangsu 210096,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2021年第9期96-107,共12页
Acta Optica Sinica
基金
国家自然科学基金(61801003)
安徽高校协同创新项目(GXXT-2019-008)
安徽工程大学校级科研项目(Xjky072019B02)。
