医学图像融合技术的研究

利用图像融合技术,将不同模态的医学图像有机地结合在一起,可以充分利用各种医学图像的优点,为临床诊断和治疗提供帮助。本文主要介绍了医学图像融合技术的基本概念、发展情况、常用方法及面临的困难等,并对医学图像的研究前景作了预测。     

使用Radon变换进行二维MRI图像配准

使用了一种基于Radon变换的技术来进行二维的MRI图像配准。MRI的图像配准一般使用灰度配准,而Radon变换一般用于CT图像的重建,虽然现已经存在使用Radon变换进行图像配准,但是比较繁琐,我们对这一配准算法进行了简化。     

自适应指数加权互信息配准医学图像的测度及相应算法

为配准医学图像,本文提出了一种新的自适应指数加权的互信息(Adaptive Exponential Weighted Mutual Informa- tion,AEWMI)测度,分析表明:通过对互信息(Mutual Information,MI)测度进行指数加权可以提高测度曲线的峰值尖锐性和平滑性;而指数的权值则可以通过评估待配准图像的质量和分辨率大小来自适应确定。仿真实验结果在验证分析结果的同时也表明,基于本文AEWMI测度的配准方案,对图像噪声、分辨率差异等有较高的鲁棒性,且可有效地提高配准的成功率。     

基于区域相似性测量的CT／PET图像配准

图像配准在临床诊断中有重要意义,针对这一问题已经提出了许多方法。本文以区域相似性匹配测度,运用改进的分割方法,结合Powell寻优算法实现了CT／PET多模医学图像配准。实验结果表明,该算法易于实现,配准速度快、精度高,鲁棒性较好。     

肺癌影像引导放疗人工图像配准方法分析

目的分析千伏锥形束CT（KVCBCT）引导肺癌放疗人工图像配准法的重复性。方法选择16例在我院行根治性放疗的非小细胞肺癌患者,每周行KVCBCT在线引导体位校正一次,获取患者KVCBCT影像。图像配准选择肺尖和椎体作为参考标记,在矢状位、冠状位和横断位等中心层面上配准患者KVCBCT影像和计划设计cT影像。比较同一名医生相隔一周两次配准,不同医生之间配准和医生与技术员之间配准结果的差异,用于评价KVCBCT引导肺癌放疗人工图像配准法的重复性。结果同一位医生相隔一周两次配准同一幅KVCBCT影像与计划设计cT影像,配准结果在患者左右（LR）、头脚（sI）和前后（AP）三个方向上,差值大于3mm所占的比例分别为：0,13％和6％。不同医生之间的配准结果在LR、SI和AP三个方向上,差值大于3mm所占的比例分别为：11％,19％和14％。医生与技术员的配准结果在LR、SI和AP三个方向上差值大于3mm所占的比例,分别为：16％,27％和27％。结论KVCBCT引导肺癌放疗人工图像配准法的重复性有待进一步提高。尤其表现为不同医生,医生与技术员之间应用该方法的重复性较差。KVCBCT引导肺癌放疗的图像配准方法需要进一步研究。     

基于图像配准分析物种进化关系的新方法

图像配准是图像处理的一个重要技术,可用于分析两幅图像之间的相似度。本文提出了一种基于图像配准分析物种进化关系的新方法:首先利用一阶马尔可夫链方法计算不同基因组序列的寡聚核苷酸转移概率矩阵;然后将转移概率矩阵转换为彩色图像矩阵,并绘制物种两两之间彩色图像矩阵的联合直方图;最后分析联合直方图点集的分布情况,引入直方图点集的散度公式,将其作为相似性测度的标准,从而鉴定物种亲缘关系的远近。100种细菌全基因组的计算结果表明,相较于单基因法或基于基因组寡聚核苷酸频率组分差异信息的方法,本文提出的新方法具有更高的准确度和分辨力,它不仅能够很好地分辨科以下的分类单元,而且对科以上的分类单元同样具有较好的区分效果。该方法有望发展成为物种鉴定及系统发育推断的有效手段。     

基于配准的插值

断层间图像插值是三维重建的一个关键步骤,因为图像上像素之间的间隔常常小于断层图像之间的距离,而在三维重建需要它们有一致的分辨率.由于是同模态断层图像层间插值,对于解决同模态弹性配准问题,Thirion的demons算法比较适合.所以配准采用Demons方法.Demons算法先判断出待配准图像上各个象素点的运动方法,通过对各个象素点的移动来实现非刚性配准.在这个算法中,每张图像都被视为同灰度值轮廓的集合.该算法可以应用于精度要求比较高的体数据插值重建过程.     

基于恒定空间分辨率离散序列小波变换的眼底血管造影图像拼接方法

将恒定空间分辨率离散序列小波变换（discrete sequence wavelet transform,DSWT)应用于眼底吲哚青绿血管造影（indocyanine green angiography,ICGA)图像的拼接,解决了传统基为2的DSWT会导致分解结果的空间分辨率下降的问题。提出对图像小波分解细节逼近和平滑逼近分别使用加权平均拼接和直接平均拼接进行处理的策略,以得到兼顾视觉效果和保真性的拼接结果。并且针对眼底图像背景光照不一致,提出在小波域进行处理的策略。实验结果表明拼接算法效果良好。     

共聚焦激光检眼镜下眼底图像的非特征配准约束模型研究

研究共聚焦激光检眼镜下不基于特征提取的眼底图像自动配准方法中的运动约束模型,从成像机理上分析共聚焦激光检眼镜下图像对间的运动模式,并分析比较多种实际全局运动模型约束下的配准精度和效率,进而给出一种由粗到细的复合约束模型对眼底图像进行配准。实验结果证实了该模型效果良好。     

基于非刚体配准的体数据插值重建方法

三维图像的处理和操作需要将一般的断层序列插值成为具有各坐标轴一致的分辨率的体数据,而目前最常用的线性插值方法在层间距较大时会导致图像边缘模糊和出现伪影。Penney根据现有的非刚体匹配方法,提出了利用图像形变场数据的插值算法,大大提高了层间插值的质量。本文对Penney提出的算法进行了两方面的改进,在配准过程中用简单的单射性约束取代了复杂的平滑性约束,用邻域平均算法替代Penney使用的最邻近直线插值方法,并将新算法的实验结果与原算法、线性插值进行了对比,新算法在保持高质量插值的前提下提高了计算速度。该算法可以应用于精度要求比较高的体数据插值重建过程。     

Registration methods in radiotherapy

PurposeThe aim of this study is to present a short and comprehensive review of the methods of medical image registration, their conditions and applications in radiotherapy. A particular focus was placed on the methods of deformable image registration.MethodsTo structure and deepen the knowledge on medical image registration in radiotherapy, a medical literature analysis was made using the Google Scholar browser and the medical database of the PubMed library.ResultsChronological review of image registration methods in radiotherapy based on 34 selected articles. A particular attention was given to show: (i) potential regions of the application of different methods of registration, (ii) mathematical basis of the deformable methods and (iii) the methods of quality control for the registration process.ConclusionsThe primary aim of the medical image registration process is to connect the contents of images. What we want to achieve is a complementary or extended knowledge that can be used for more precise localisation of pathogenic lesions and continuous improvement of patient treatment. Therefore, the choice of imaging mode is dependent on the type of clinical study. It is impossible to visualise all anatomical details or functional changes using a single modality machine. Therefore, fusion of various modality images is of great clinical relevance. A natural problem in analysing the fusion of medical images is geographical errors related to displacement. The registered images are performed not at the same time and, very often, at different respiratory phases.     

基于PACS的医学图像压缩

从PACS和DICOM的定义出发,对基于PACS的医学图像压缩的要求和算法等方面作了阐述,还介绍了JPEG2000在医学图像压缩中的优势。     

Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student’s t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05).     

Optical-CT Dual-Modality Mapping Base on DRR Registration

Optical-CT dual-modality imaging requires the mapping between 2D fluorescence images and 3D body surface light flux. In this paper, we proposed an optical-CT dual-modality image mapping algorithm based on the Digitally Reconstructed Radiography (DRR) registration. In the process of registration, a series of DRR images were computed from CT data using the ray casting algorithm. Then, the improved HMNI similarity strategy based on Hausdorff distance was used to complete the registration of the white-light optical images and DRR virtual images. According to the corresponding relationship obtained by the image registration and the Lambert’s cosine law based on the pin-hole imaging model, the 3D light intensity distribution on the surface of the object could be solved. The feasibility and effectiveness of the mapping algorithm are verified by the irregular phantom and mouse experiments.     

Dynamic Lung Modeling and Tumor Tracking Using Deformable Image Registration and Geometric Smoothing

A greyscale-based fully automatic deformable image registration algorithm, based on an optical flow method together with geometric smoothing, is developed for dynamic lung modeling and tumor tracking. In our computational processing pipeline, the input data is a set of 4D CT images with 10 phases. The triangle mesh of the lung model is directly extracted from the more stable exhale phase (Phase 5). In addition, we represent the lung surface model in 3D volumetric format by applying a signed distance function and then generate tetrahedral meshes. Our registration algorithm works for both triangle and tetrahedral meshes. In CT images, the intensity value reflects the local tissue density. For each grid point, we calculate the displacement from the static image (Phase 5) to match with the moving image (other phases) by using merely intensity values of the CT images. The optical flow computation is followed by a regularization of the deformation field using geometric smoothing. Lung volume change and the maximum lung tissue movement are used to evaluate the accuracy of the application. Our testing results suggest that the application of deformable registration algorithm is an effective way for delineating and tracking tumor motion in image-guided radiotherapy.