基于非局部权值先验和GPU加速的3D低剂量CT成像

Three-dimensional low-dose CT volume reconstruction based on non-local weights optimization and GPU acceleration

在肿瘤监测和放疗计划制定中,需要多次CT扫描,其使用的X射线辐射剂量已受到广泛关注。为了获得高质量的低剂量CT图像,本文提出一种基于非局部权值先验和GPU加速的3D低剂量CT成像新方法。源于非局部均值滤波(NLM)思想,本文方法采用先前标准剂量CT扫描图像构建用于低剂量CT图像重建的全新非局部均值滤波。具体而言,本文方法首先将3D标准剂量图像与低剂量图像进行配准以减少两图像数据间解剖结构的不一致性,接着利用两配准后的图像构建NLM权重先验,最后采用全新的非局部平均实现高质量的低剂量CT成像。为了增加本文方法的执行效率,GPU硬件加速技术被采用。实验结果表明,本文方法较传统NLM滤波在低剂量图像的噪声消除和细节信息保持两方面均有优势显著且执行效率大幅提升。

Concerns have been raised over x-ray radiation dose associated with repeated computed tomography（CT） scans for tumor surveillance and radiotherapy planning.In this paper,we present a low-dose CT image reconstruction method for improving low-dose CT image quality.The method proposed exploited rich redundancy information from previous normal-dose scan image for optimizing the non-local weights construction in the original non-local means（NLM）-based low-dose image reconstruction.The objective 3D low-dose volume and the previous 3D normal-dose volume were first registered to reduce the anatomic structural dissimilarity between the two datasets,and the optimized non-local weights were constructed based on the registered normal-dose volume.To increase the efficiency of this method,GPU was utilized to accelerate the implementation.The experimental results showed that this method obviously improved the image quality,as compared with the original NLM method,by suppressing the noise-induced artifacts and preserving the edge information.