基于图像矩的低剂量CT图像重建系统开发

发布时间：2018-05-17 10:25

本文选题：低剂量CT图像重建 + 有限角度投影数据　；参考：《南京邮电大学》2017年硕士论文

【摘要】：X射线计算机断层成像技术(X-ray Computerized Tomography,CT)能够为临床医生的诊断提供丰富的人体器官组织解剖信息,是临床诊断和治疗的有效工具之一。但是一次完整的CT扫描会对人体产生较大的电离辐射伤害,如果长时间暴露在高剂量的电离辐射下容易引发癌症等疾病。虽然减少CT投影数据能够有效降低X射线剂量、减少电离辐射伤害,但是同时也会造成CT重建图像质量下降,从而影响临床医生的诊断。因此,如何在低剂量X射线条件下重建出符合临床诊断要求、高质量的二维或三维CT图像具有重要的临床实用价值。为了在部分平行束投影数据缺失的情况下提高二维CT重建图像质量,本文提出了基于Krawtchouk离散正交矩的有限角度二维C T图像重建算法。该算法利用Krawtchouk离散正交矩和二维Radon变换的性质建立了Krawtchouk离散正交矩和二维Radon变换之间的关系,然后通过此关系先从已知角度投影数据中获得Krawtchouk离散正交矩,然后再通过已获得的Krawtchouk离散正交矩来估计未知角度投影数据,从而达到补全投影数据、提高重建图像质量的目的。实验结果表明,本文方法能够在投影数据缺失的情况下有效提高二维重建图像质量。其次,为了在部分锥形束CT投影数据缺失的情况下提高三维CT重建图像质量,本文提出了基于几何矩的有限角度锥形束CT图像重建算法。该算法利用Grangeat公式将获得的已知锥形束投影数据转化为已知三维Radon变换数据,然后建立了三维Radon变换与三维几何图像矩之间的关系,并通过此关系从已知三维Radon变换数据中计算出三维几何图像矩,再从已获得的三维几何图像矩估计出未知三维Radon变换数据,最后直接使用三维Radon逆变换重建出图像。从实验结果可以看出,在部分锥形束CT投影数据缺失时,该方法仍能获得质量较好的三维CT图像。
[Abstract]:X-ray Computerized Tomography can provide abundant anatomical information of human organs and tissues for the diagnosis of clinicians. It is one of the effective tools for clinical diagnosis and treatment. However, a complete CT scan can cause more ionizing radiation damage to human body. If exposed to high dose of ionizing radiation for a long time, cancer and other diseases can be easily caused. Although the reduction of CT projection data can effectively reduce the dose of X-ray and reduce the injury of ionizing radiation, it will also cause the quality of CT reconstruction image to decline, thus affecting the diagnosis of clinicians. Therefore, how to reconstruct 2D or 3D CT images with high quality under the condition of low dose X-ray is of great clinical value. In order to improve the quality of 2D CT reconstruction image without some parallel beam projection data, a finite angle 2D CT image reconstruction algorithm based on Krawtchouk discrete orthogonal moments is proposed in this paper. Based on the properties of Krawtchouk discrete orthogonal moment and two-dimensional Radon transform, the relationship between Krawtchouk discrete orthogonal moment and two-dimensional Radon transform is established, and then the Krawtchouk discrete orthogonal moment is obtained from the known angle projection data. Then the unknown angle projection data are estimated by the obtained Krawtchouk discrete orthogonal moments, which can complement the total projection data and improve the image quality. The experimental results show that the proposed method can effectively improve the quality of 2D reconstructed images without projection data. Secondly, in order to improve the image quality of 3D CT reconstruction, a finite angle cone-beam CT image reconstruction algorithm based on geometric moments is proposed in this paper. In this algorithm, the known conical beam projection data are transformed into known 3D Radon transform data by Grangeat formula, and the relationship between 3D Radon transform and 3D geometric image moments is established. The moment of 3D geometric image is calculated from the known 3D Radon transform data, and then the unknown 3D Radon transform data is estimated from the obtained 3D geometric image moment. Finally, the image is reconstructed directly by using 3D Radon inverse transform. From the experimental results, it can be seen that the method can still obtain good quality 3D CT images when part of the conical beam CT projection data is missing.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R814.42;TP391.41

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