BAYESIAN COMPRESSED SENSING ALGORITHM FOR CONTRAST-ENHANCED LOW-DOSE CT IMAGE WITH IODINE BASED ON CARBON NANOTUBE X-RAY TUBE

A Bayesian compressed sensing algorithm for low-dose CT images based on carbon nanotube X-ray tube is proposed to improve their reconstruction quality. The field emission theory of the nanotube X-ray tube is analyzed. It is also possible to increase the quality of CT scan images by using contrast, along with nanotubes. According to the emission theory, a static image scanning system is constructed to scan low-dose CT images with contrast-enhanced (Iodine). Further, an improved noise variance estimation algorithm is constructed, and the CT image denoising is realized using Anscombe variance stable transform and inverse transform. Bayesian compressed sensing algorithm is used to compress the high-frequency coefficients in three directions of CT images after denoising. After obtaining the sampling results, a hierarchical Bayesian model is constructed to fully use the correlation of wavelet coefficients in three directions to realize image reconstruction and complete compressed sensing. Experimental results show that the algorithm can effectively scan low-dose CT images with low redundancy and high reconstruction quality after compressed sensing.