【作者】 冯艳玲；

【导师】 何继善； 陈真诚；

【作者基本信息】 中南大学 ， 生物医学工程， 2008， 博士

【摘要】 超声引导的HIFU治疗系统以其无损,无辐射,价格相对低廉等独特的优点,在肿瘤临床治疗中有着广阔的发展前景。作为HIFU相关技术中的热点问题,对无损监控及检测治疗效果的研究,虽然引起广泛关注,并取得了一定的研究成果,但仍未见成熟的、可应用于临床的方法问世。目前基于超声信号及超声图像的无损测温、测组织损伤程度方法,多建立在理想化的声场或温场模型基础上,并要求预知各种组织的声特性,给方法的实施带来很大困难。而从大量的、不完全的、有噪声的、模糊的实际应用数据中,提取隐含在其中的、人们事先不知道的、但又是潜在有用的信息和知识的数据挖掘技术,在医学数据分析、内在规律找寻的研究中表现出越来越强的优越性。对本课题的核心研究内容为:挖掘对超声图像中蕴含的可以辅助监控、检测HIFU治疗效果的信息,寻找其中的规律,最终形成易于理解和接受的、可量化的HIFU治疗效果评价。作为一种高温热疗手段,治疗过程中靶区的温度是临床所关注的一个主要参数。论文对HIFU超声图像信息中与温度相关信息的挖掘建立在组织受热会导致超声图像纹理发生改变这一假设的基础上。采用多种纹理包括灰度共生矩阵、分形盒维数方法、小波分解等分析方法,分别提取HIFU辐照后各个温度下组织超声原始图像,及其与基准温度(37℃)下的超声图像之间的减影图像的纹理参数。结果表明:超声原始图像纹理参数与温度间不存在明显的、易于量化的函数关系;但是超声减影图像的纹理参数与温度间均表现出了较好的相关性。对其中优选参数进行与温度间的回归分析表明,HIFU焦域中心温度与超声减影图像的灰度共生矩阵参数中的四个参数与小波分解系数能量参数在40℃—80℃之间存在线性关系。采用8个与温度存在线性关系的纹理参数进行基于主成分分析的多元线性回归,40℃—70℃以下,回归方程的精度为3℃。HIFU监控所关注的另一个参数是辐照对组织实际形成的损伤程度。一本文提出一种亚象素级基于超声图像的HIFU束损伤监控方法,通过追踪特征点的位移及变化,并针对变化程度,定义了相关距离加以量化,来实现对HIFU束损伤等级的评估。对温度与组织损伤程度、剂量与组织损伤程度、组织损伤程度与相关距离的关系进行了分析。实验结论为:温度与组织损伤等级,剂量与组织损伤等级间的关系难以量化;而辐照前后,HIFU焦域相关距离与HIFU束损伤等级之间表现出相关性,HIFU束损伤等级随相关距离的增大而增大。采用支持向量机SVM分类方法,设计分类器,分类结果表明算法可有效识别HIFU束损伤的等级。可为HIFU临床剂量的选取提供参考。除了针对温度,组织损伤程度的监控研究外,本文还针对如何在高噪声的超声图像中,自动挖掘可以辅助诊断HIFU治疗结束后所产生的有效治疗区域做了方法上的研究。本文提出一种基于双零双速度水平集的算法,利用超声图像灰度的变化信息,完成零水平集的自动初始化,并从高噪声HIFU超声图像信息中提取HIFU有效治疗范围边缘信息及器官边缘信息。通过三维重建给出可反映HIFU有效治疗范围的大小及形状的三维信息,有望成为HIFU临床辅助治疗系统功能的有益补充。更多还原

【Abstract】 With its advantages of non-invasive, no radiation and low cost, ultrasound guided HIFU is supposed to be a promising therapy method for tumor. As one of the key problems in HIFU related technique, ultrasound-based temperature and HIFU effect estimation have been attracting many researchers attention. But there is still no resolution which is mature enough for clinic use.By now, most ultrasound signal or image-based non-invasive temperature or HIFU effect estimation methods are based on ideal sound field and need to know some sonic characters of tissue first. That is a very difficult condition to achieve. Data mining technology whose aim is analyzing data from different perspectives and summarizing it into useful information, has its advantages in medical data analysis and research This thesis concentrates on: Dada mining parameters from ultrasound images which can help to monitor HIFU therapy effect; Finding the rules and giving the understandable and acceptable evaluation of HIFU effect. Temperature is a very important parameter need to be monitored during HIFU therapy. Data mining temperature related ultrasound image texture parameter is based on the hypothesis that texture will change with tissue temperature. Gray Level Co-Occurrence Matrix (GLCM) , box fractal dimension, wavelet methods were used to extract texture parameters from ultrasound images at different temperature and their subtraction with ultrasound image at basic temperature(37℃). Results show that original ultrasound images’ texture has no obvious relationship with temperature of tissue, but some GLCM texture parameters and wavelet coefficient energy parameters of subtraction images have linear relationship with temperature of tissue from 40℃to 80℃. Eight parameters have linear relationship with temperature were used to construct principal components analysis based multiple regression prediction model. The regression formula’s precision is 3℃at temperature under 70℃.Another way to estimate HIFU effect is monitoring lesion of tissue. A sub-pixel ultrasound image based method is proposed in this thesis and correlation distance was defined for quantization of featured points’ movement and change in HIFU lesion area image. Results show that temperature has no quantitative relationship with lesion degree, neither dose, but sub-pixel cross-correlation vector field can reflect the ablation lesions position and correlation distance is helpful for detecting the degree of the beam ablation lesions. Correlation distance based SVM classification method can help to detect HIFU lesion degree automatically and efficiently.Besides research on monitoring temperature and HIFU lesion, another theme of this thesis is to find a way which can automatically provide the HIFU lesion area’s shape and position after therapy. Data mining edge information of ROI (region of interest) from high-noise ultrasound images is the key. We proposed a double zero level set, double speed approach to extract the organ and lesion area’s contour. Gray level change was used to initial zero set automatically Serial "slice" contours were used to reconstruct three dimension (3D) HIFU lesion areas in the sample. Result: Experiments show that level set contour extraction method based 3D reconstruction is helpful in monitoring the size, shape and location of HIFU lesion.更多还原