基于双字典自适应学习算法的低采样率CT重建

Low Sampling Rate CT Reconstruction Based on Dual Dictionary Adaptive Learning Algorithm

在医疗诊断中,稀疏采样能减少CT扫描过程中辐射对患者的伤害.但直接对稀疏采样后的投影数据进行重建,会使CT重建后的图像出现失真、伪影等问题.为保证低采样率下重建图像的质量,提出了双字典自适应学习算法,参照Sparse-Land模型的双字典学习框架,将K-SVD算法与双字典学习算法框架相结合得到补全投影数据,利用FBP算法进行重建得到高质量的重建图像.实验结果表明,在低采样率下使用所提方法进行CT重建的图像质量优于COMP双字典学习算法和MOD双字典学习算法,并且此方法有效提高了CT图像重建在低采样率时的性能.

In medical diagnosis, sparse sampling can reduce radiation damage to patients during CT scanning. However, direct reconstruction of sparse sampling projection data will cause distortion and artifacts in the reconstructed CT images. In order to ensure the quality of reconstructed images at low sampling rate, a dual dictionary adaptive learning algorithm is proposed, referring to the dual dictionary learning framework under the Sparse-Land model. K-SVD algorithm is combined with the dual dictionary learning algorithm framework to obtain patched projection data and FBP(filter back projection) algorithm is used to reconstruct high-quality reconstructed images. Experimental results show that the proposed method is superior to COMP double dictionary learning algorithm and MOD double dictionary learning algorithm in CT reconstruction at low sampling rate, and this method effectively improves the performance of CT image reconstruction at low sampling rate.