Research On Calibration Method And Interior Tomography Of High Resolution CT

High resolution CT is one kind of imageing system of which the spatial resolution is higher than 10 micrometers and even can reach to tens of nanometers.High resolution CT plays an important role in the field of disease modeling,drug testing,tumor monitoring,bone parameters measurement and many other fields due to its nature of non-destructive,non-intrusive and high resolution.Because of the high resolution characteristic,the hardware system and imaging algorithm in high resolution CT demands higher precision than those in conventional Micro CT.Our laboratory developed a high resolution CT system with a spatial resolution of sub-micron level.This paper mainly focuses on the study of system calibration and interior reconstruction algorithm in this high resolution CT platform.The high resolution CT system has a high demand for the geometric calibration precision,and the traditional methods can not meet the demand.In this paper,a geometric calibration algorithm for high precision CT system based on single point phantom is proposed.The high-precision calibration method in this dissertation is based on optimization theory with a simple spherical phantom.The involved cost function associates the misalignment parameters with the convergence degree of the lines back-projected from all projections of the phantom.In order to avoid being trapped in the local minimum due to noise,the simplex-simulated annealing algorithm is employed to minimize the cost function for solving the optimal parameters.Results on the simulated data and the real data from high-resolution CT prove that the proposed calibration method can get all geometric parameters with higher precision and stronger robustness.The multi-lens detector conversion device in the high resolution CT platform allows the system to switch lens detectors with different resolution and FOV.In order to effectively perform image registration among the lens detectors,this paper realizes the image registration algorithm based on mutual information optimization.Experiments show that the optimization algorithm can effectively meet the the registration requirements in high resolution CT platform.In this paper,the relationship between geometric calibration and image registration of high resolution CT is further analyzed.A joint optimization algorithm for the geometric calibration and image registration of multi-detector is proposed which the solving process of the two problems are integrated into the same optimization.The results of high resolution CT multi-detector experiment and Micro CT detector translation experiment demonstrate that the algorithm can effectively perform high-precision geometric calibration and image registration of multi-detector CT system.The calibration process is simple and only one optimization process can complete the system calibration work.In high resolution CT applications,the diameter of the field of view(FOV)should cover the specimen to allow a complete reconstruction.Otherwise,projections truncation occurs and results in truncation artifacts in reconstruction.Multi-resolution acquisition based methods can be applied to solve this problem by using a low resolution scan to fill up the incomplete information in truncated high-resolution projections.This study proposes a low resolution image constrained reconstruction algorithm(LRICR)for high resolution CT interior tomography.In the proposed method,data of global and interior scans are collected for different resolutions.The reconstructed low resolution image is incorporated as prior knowledge into the high resolution interior reconstruction process in the LRICR algorithm.Two phantoms and a bamboo sample were used in experiment to evaluate the proposed algorithm.Compared with the traditional TV minimization algorithm and the multi-resolution 'Scout-reconstruction' algorithm,the proposed method shows significant improvements in reducing truncation artifacts and provide ROI reconstruction in higher quality.