【作者】 吴越；

【导师】 罗立民； 陈阳；

【作者基本信息】 东南大学 ， 计算机科学与技术， 2016， 硕士

【摘要】 X射线计算机断层成像(X-ray Computerized Tomography, CT)具有空间分辨率高,扫描速度快,病人成本低,能够提供三维成像的优点。近年来, CT扫描技术已经在临床医学诊断中得到了广泛的应用,成为放射诊断不可缺少的工具之一。然而,随着CT技术的不断普及,CT扫描中的X射线辐射也逐渐引起了人们的关注,辐射剂量的积累会诱发癌症等病变。通过调节管电流可以减少剂量,但生成的CT图像中会产生大量的噪声和伪影,降低CT图像质量,甚至影响医生对病灶的确诊率。因此,如何在降低CT剂量的同时得到高质量的CT图像,具有非常重要的临床意义,也是近年来医学成像领域热门的研究课题。本文将主要研究基于区别性字典的低剂量CT图像处理算法,并利用区别性字典的方法去除CT截断伪影,最后探索了CT金属伪影的去除,具体内容如下：对于低剂量CT图像处理,本文提出了一种有效的方法,称为区别性特征表示算法(Discriminative Feature Representation, DFR)。这种方法把低剂量CT图像当作高剂量CT特征和噪声伪影的合成,把高剂量CT特征分离出来,作为处理后的图像。特征分离通过区别性字典实现,字典包含了从不同剂量的CT体模图像中提取出的高剂量CT图像特征和噪声伪影。为了得到最有效的特征表示,构建字典所使用的体模图像,和待处理的低剂量CT图像,采集自相同的CT机,并且大部分扫描参数一致。从图像质量看,该算法在不同公司CT机的临床数据上都取得了不错的效果,不仅有效抑制了低剂量CT图像中的噪声伪影,同时没有丢失对比度和细小组织结构。更重要的是,该算法简洁明了,易于实现,参数设置鲁棒性好,可以方便地应用在现有的CT系统中。CT图像中的伪影常常比噪声更加影响医生的诊断,因而本文还研究了CT图像中两种常见伪影的去除。对于CT截断伪影的去除,继续利用了区别性表示的思想,根据截断伪影的特点模拟出伪影字典,在图像域实现伪影的分离。该方法在体模验证和临床数据上都取得了不错的效果。最后,本文还探索了使用自洽性条件进行CT金属伪影的去除。该方法取得了初步的成效,还有待进一步的研究。更多还原

【Abstract】 X-ray Computerized Tomography has the advantages of high spatial resolution, short scanning time, the capability of providing three-dimensional imaging and low patient cost. In recent years, CT scanning has been widely used in clinical diagnosis as an indispensable tool. However, with the growing popularity of CT technology, X-ray radiation during CT scan has gradually attracted people’s attention because the accumulation of radiation dose can cause cancer and other diseases. Dose can be reduced by adjusting tube current, but the resulting image will have more noise and artifacts which degrade the image quality and even lower diagnosis rate of lesions. So it is of great significance to obtain high quality CT images with low dose, and low dose CT (LDCT) is actually a hot research topic in the field of medical imaging. This thesis mainly focuses on discriminative dictionary based low dose CT image processing algorithm. The discriminative dictionary is then used to remove truncation artifacts. In the end, a novel method to remove metal artifacts is explored. Details are as follows:This thesis proposes an effective approach termed Discriminative Feature Representation (DFR) for LDCT image processing. This DFR method considers LDCT images as the superposition of high dose CT (HDCT) 3-D features and noise-artifact features (the collective noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features can be used to yield the LDCT images with high quality. The proposed approach works through a discriminative representation using a featured dictionary composed of atoms to represent HDCT features and noise-artifact features. Boosted feature representations are brought by constructing dictionary using physical phantom images collected from the same CT scanner which generated the images to be processed. Allowing an easy implementation in practice, the proposed DFR method has good robustness in parameter setting for different CT scanner types. Comparative experiments with abdomen LDCT data validated the good performance of the proposed approach. Moreover, this algorithm has concise implementation and robust parameter settings, so it can be easily applied in conventional CT systems.In many cases, artifacts are more unacceptable in doctor’s diagnosis compared to noise. This thesis also studies reduction of two common artifacts in CT images. The idea of discriminative dictionary is used again to separate truncation artifacts in the image domain. Both experimental phantom studies and in vivo human subject studies were performed to validate the proposed method and to evaluate its performance. Finally, the reduction of metal artifacts was explored using consistency condition, and some preliminary results are given. The future research directions are discussed in the end.更多还原