【作者】 张华；

【导师】 陈武凡； 马建华；

【作者基本信息】 南方医科大学 ， 生物医学工程， 2014， 博士

【摘要】 X射线计算机断层成像(CT)以其高的时间分辨率、空间分辨率及对比度分辨率成为现代影像学的杰出代表,在临床诊断和治疗中广泛使用。但过量照射X光可诱发癌症、白血病或其他遗传性疾病,因此CT的剂量安全问题成为业界关注的焦点。如何以最小的代价和最低的X射线剂量获得最佳的CT图像质量,已经成为临床最为迫切的需求。降低X射线剂量的方法有多种,如通过降低管电流和缩短曝光时间来降低X射线球管的毫安秒(mAs)或者减少扫描角向采样数目等直接性的措施来降低X谢线剂量。但与此同时,上述扫描模式下解析重建的图像质量将严重退化,图像中的噪声和伪影影响了临床诊断和应用。近年来,针对低剂量CT图像优质重建的研究方兴未艾,主要集中于对解析重建的低剂量CT图像进行后处理滤波、对低剂量CT投影数据进行恢复,以及包括代数迭代和统计迭代的迭代重建算法。其中,针对迭代重建算法的研究尤其热烈。相对于解析重建算法,迭代重建算法在CT固有的物理系统建模方面具有较大的优势,如可精确模拟系统成像几何,X线多能光谱、线束硬化、散射、噪声等。因此,迭代重建算法能保持或者提高图像空间分辨力的同时抑制伪影和噪声。进一步的,统计迭代重建算法能基于光子统计学建立多种更精确的噪声模型,在降低图像噪声方面有更佳的表现。常规的,在迭代重建过程中,准确建模投影数据的测量方程是得到优质重建的前提和基础,而先验知识的合理利用对于求解的稳定性及获得高精度重建图像具有非常重要的意义。目前,在疾病的临床诊断和治疗过程中,常用到反复的CT扫描,如在灌注CT成像、4D-CT成像、CT图像引导的活体组织穿刺检查以及图像引导放疗等过程中。以图像引导放疗为例,除计划阶段CT扫描外,在整个放射治疗阶段,在分次治疗前需对患者进行CBCT扫描来进行定位,在此情况下,患者所接受的累加辐射剂量较常规CT检查扫描将会很高,反复CT扫描增加了罹患肿瘤的风险。在上述反复CT扫描过程中,对于同一个病人,先后两次CT扫描的图像之间除了几何位置和某些不自主运动引起的不同外,大部分解剖结构是相同的。换句话说,两次扫描获取的CT图像之间存在大量的结构冗余信息。传统的针对低剂量CT的研究方法仅仅引入图像自身的局部邻域的约束作为先验信息,未能考虑同一病人先前扫描获取的图像所能提供的先验信息来导引当前CT的图像的重建。鉴于此,本论文针对基于先前图像的先验信息的合理引入、先验信息引入后算法的优化等问题进行了深入的研究,同时本文对基于平板探测器的CBCT(Cone BeamCT)成像系统的噪声特性进行了进一步实验分析和验证,为后续迭代重建建立了理论和系统模型基础。归纳起来,本文的主要工作有：1)为合理的引入先验图像信息,同时突破传统先验单纯依赖于目标图像局部邻域内的像素灰度信息的约束,本文利用非局部的思想通过在固定大小的搜索窗内检测基于图像块匹配的像素相似性而形成正则化项来实现基于先验图像的先验信息的有效引入。新方法在能够引入先验图像信息的同时弱化了对当前待重建目标图像和先验图像间配准精度的要求。数值仿真,物理体模以及临床数据实验表明,新方法能够提高当前低剂量图像的重建质量,同时不引入伪结构信息。2)针对脑灌注CT成像的两大特点：(1)灌注扫描前的平扫描标准剂量图像的分辨率高且噪声低,可以为低剂量灌注序列增强图像的重建提供极为丰富的形态结构冗余信息；(2)对同一感兴趣层面反复扫描所得的增强序列图像除增强信息外,各图像之间的相对形变量较小；本文提出一种标准剂量平扫描图像导引的低剂量CT脑灌注序列图像重建方法。新方法首先利用利用标准剂量平扫描图像丰富的冗余信息实现基于改进的非局部均值算法的低剂量增强扫描图像的优质恢复,然后对恢复后的图像做去卷积迭代重建。数值仿真体模和病人数据实验表明,该算法能有效抑制图像中噪声,提高图像信噪比,从而使得相应血流动力学参数的计算更加准确。3)为解决先验图像和当前待重建图像之间的结构不匹配问题,本文提出了一种利用当前测量所得投影数据与先验图像配准的方法获取与当前待重建图像相近或类似的先验图像用于基于先验图像的CBCT少角度重建。具体来说,本文算法是通过当前测量投影数据与先验图像在当前成像几何下的前向投影数据的匹配来实现图像域变形场的估计,然后将该变形场作用于先验图像来获取配准的先验图像,用于基于先验图像的重建策略中。特别的,本文采用的重建算法为PICCS(Prior Image Compress Constrained Sensing)算法。本文提出的算法能够有效避免图像空间配准时FDK算法重建的图像中噪声和条形伪影对配准精度的影响。XCAT仿真实验以及临床数据实验结果表明,本文提出的方案能够获取优质先验图像,重建结果优于传统PICCS算法。4)鉴于CBCT成像系统中平板探测器与常规扇形束CT探测器物理设计上的不同,本文实验性的针对瓦里安TrueBeam系统中的平板探测器采集信号进行噪声相关性研究。通过对固定角度下反复测量的的探测数据的分析得出投影数据在探测器单元之间存在噪声相关性。实验结果表明,二维探测器八邻域内的噪声相关性是明显可见的,且一阶邻域内的相关系数明显高于二阶邻域内相关系数。同时,该噪声相关性与扫描所用X线剂量无关。本文将该噪声相关性应用于基于投影数据噪声模型的惩罚加权最小二乘恢复算法中,重建图像结果表明,该噪声相关性的引入能够提高重建图像质量。该实验研究为CBCT图像重建提供了更精准的系统建模。更多还原

【Abstract】 X-ray computed tomography (CT) has become an excellent example for medical imaging with its high temporal resolution, spatial resolution and contrast resolution. It has been widely used in the clinical diagnosis and treatment. But the excessive X-ray radiation may induce cancer, leukemia, or some other hereditary disease, so the safety and security of CT imaging has become one focus problem in the industry. How to get the best CT image quality with the minimum cost and minimum X-ray dose, has become one of the most important issue.Until now, various techniques have been investigated, including directly lowering the mAs or down-sample the viewing angle, to reduce radiation dose in CT scans. Meanwhile, the associated reconstructed image usually suffers from serious noise and artifacts, which has negative influence for clinical diagnosis. Recently, investigation focused on low dose CT reconstruction can be grouped into three categories:the post-processing of the reconstructed CT image, low dose CT projection restoration, and iterative reconstruction methods that include the algebraic iterative reconstruction algorithm and statistical iterative reconstruction algorithm. Among them, the study on iterative reconstruction algorithms is more popular. Compared with the analytical reconstruction algorithm, the iterative reconstruction algorithm can overcome the inherent physical limitations of CT by modeling the imaging geometry, X-ray spectrum, beam hardening, scattering, and noise, etc. Therefore, the iterative reconstruction algorithm can further improve the image spatial resolution and reduce the artifacts. Furthermore, iterative reconstruction algorithms that based on photon statistics constructing an accurate noise model, has a better performance in reduce image noise. Generally, accurate modeling of projection data measurements is the foundation of high quality reconstruction, and the introduction of prior knowledge to avoid solving ambiguity and high precision image reconstruction has very important significance.X-ray CT technologies have been widely explored for specific applications in clinic including perfusion imaging,4D-CT imaging, image-guided intervention and radiotherapy, et al. Under these situations, repeated topographic acquisitions are often prescribed. For instance, except the planning CT, in daily Cone Beam CT (CBCT) examinations for target localization in image-guided radiation therapy (IGRT), repeated scans have become routine procedures. In this case, the cumulative radiation dose still significantly increase as comparison with the conventional CT scans, which has raised major concerns in patients. With regard to the repeated CT scans, a previously scanned high-quality diagnostic CT image volume usually contains same anatomical information as the current scan except for some anatomical changes due to internal motion or patient weight change. In other words, there exists redundant information among the repeated scan CT images. Traditionally, the prior information is constrained on the different values of the local neighboring pixels in the image domain. So the CT scans at different times are often dealt independently and no systematic attempt has been made to integrate the valuable patient-specific prior knowledge, i.e., the previous scanned data that hold a wealth of prior information on the patient-specific anatomy, to promote the subsequent imaging process. According to the above, in this paper, we focus on exploiting reasonable approaches to incorporate the redundancy of information in the prior-image and also the optimization with the introduction of the prior information. Meanwhile, we have a further study on the noise prosperities of the flat panel detector (FPD) mounted on the CBCT imaging system, which has constructed the statitiscal model for CT iterative reconstruction. On the whole, the main works of this paper can be summarized as follows:1) To introduce the prior information in a reasonable way, and to overcome the disadvantage of the locally designed prior term with only the different values of the neighboring pixels as the constraint, in this paper, we propose to utilize the nonlocal criteria to search the patched based similarity between different pixels to form the regulation term. In this way, the prior information can be introduced efficiently. The new approach relaxes the need of accurate registration between the target image and the prior image. Experiments on the physical phantom, numerical XCAT phantom, and patient data show that the proposed approach can improve the image quality without introducing new fake structure in low dose CT imaging.2) Base on two main characteristics of cerebral perfusion CT imaging:(1) the normal dose pre-contrast scanned image has relative higher resolution and lower noise level as compared to the follow up low dose contrast image. So the redundant information in the precious high quality image can be used to promote the low dose contrast image reconstruction.(2) During the sequential scan of the selected slices, most of the anatomical structures are unchanged except the changes of the intensity level. In his paper, we propose a normal dose pre-contrast scan induced low dose cerebral perfusion CT image reconstruction algorithm. Firstly, the low dose contrasted image is restored by using the improved nonlocal means criterion with the normal dose pre-contrast image. Then the image is reconstructed by an iterative deconvolution reconstruction framework. Experiments on both numerical simulation and patient data show that the algorithm can effectively suppress noise and improve the image SNR, and then to make the corresponding hemodynamic parameters calculation more accurate.3) To address the mismatch problem between the prior image and target image for the prior image constrained compressed sensing (PICCS) algorithm, in this paper, we propose to obtain similar or closed image volume by registering the on-treatment projection data and the prior image volume. Particularly, this procedure is facilitated by estimating the deformation vector fields (DVF) through matching the forward projection of the prior image and the measured on-treatment projection. Then by translating the DVF onto the prior image, we get the deformed-prior image that used as the prior image for prior-image based image reconstruction algorithm. The present approach can effectively avoid the negative influence of the noise and artifacts in the FDK reconstructions in the image domain registration. Experimental studies on the XCAT phantom and patient data show that the proposed approach generates high-quality registered prior image with most anatomical structures aligned to the target image, the image quality of the reconstructions has been improved as compared to the standard PICCS algorithm.4) Given the physically difference between the flat panel detector in CBCT imaging system and the conventional fan beam CT detector, in this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data on a True-Beam on-board system. The noise correlation coefficients among detector bins were calculated with repeated scanned measurements. The analyses showed that non-zero noise correlation coefficient values can be found between the eight nearest neighboring pixels. For the first order neighbors, the noise correlation coefficients are larger than the second order ones. Meanwhile, the noise correlation coefficients are independent of the dose level. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least-squares (PWLS) criterion for noise reduction. Reconstruction shows that the consideration of noise correlation results in improved reconstruction quality. An accurate noise model of CBCT projection data was obtained.更多还原