利用边缘和表面特征实现脑图像的自动配准

本文中我们使用基于CT、MR和PET图像等值特征表面的配准算法对多模医学图像进行了配准研究，在CT、MR和PET的原始图像中提取等值特征表面，进行图像的几何对准，并对结果进行初步评估，同时对该算法的稳健性，搜索最近点策略和采样策略进行了研究，结果表明；这种方法能够达到亚像素级的配准精度，是一种稳健、高精度、全自动的配准方法。     

"虚拟中国人男性一号"多模态图像配准

目的：解决“虚拟中国人男性一号”CT图像、MRI图像与断层切削图像之间的多模态图像配准问题。材料和方法：根据这三种图像的特点，选择CT图像为基准图像，在对MRI图像进行配准时，通过求解两幅图像梯度特征的最大互信息，搜索出最佳配准参数；在对断层切削图像进行配准时，采用基于解剖结构特征提取的配准方法获取最佳配准参数：最后．根据所得配准参数对待配图进行变换，从而达到配准目的。结果：对头部三种模态图像数据集进行了配准，与高精度手工分割图像数据集进行对比，配准正确率达到95．8％。结论：配准结果准确，解决了“虚拟中国人男性一号”多模态图像配准问题，为数字化虚拟人多模态图像配准提供了参考。     

基于混合互信息和改进粒子群优化算法的医学图像配准方法

为满足医学图像辅助诊断与治疗的需要,提出一种基于混合互信息和改进粒子群优化算法的医学图像配准方法。在每次迭代时,首先使用基于Renyi熵的改进粒子群优化算法对图像进行全局搜索,再使用基于Shannon熵的Powell算法对当前得到的最优解进行局部寻优。实验图像为60幅模拟图像和10幅临床图像,对70幅图像进行单模态和多模态的医学图像配准实验,所提出算法的单模态医学图像配准结果均达到亚像素级。在多模态医学图像配准实验中,采用5种性能指标,评价配准结果的质量。同3种医学图像配准算法进行比较,结果显示新算法除计算时间外,其他4项指标均为最优,MI指数、NMI指数和CC指数的均值分别为1.338 6、1.363 1和0.837 8。主观和客观分析显示,所提出算法在精确度和收敛速度方面均优越于其他配准算法。     

利用边缘和表面特征实现脑图像CT-MR的自动配准

应用基于CT和MR图像等值特征表面的配准算法对多模医学图像进行了配准研究.在CT、MR图像中提取等值特征表面,进行图像的几何对准,并对结果进行初步评估,同时对该算法的稳健性,搜索最近点策略和插值策略进行了研究.结果表明:这种方法能够达到亚象素级的配准精度,是一种稳健、高精度、全自动的配准方法.     

PSO和Powell混合算法在医学图像配准中的应用研究

基于互信息的图像配准方法具有自动化程度高、配准精度高等优点,已被广泛应用于医学图像的配准.但是,基于互信息的目标函数经常是不光滑的,存在许多局部极值,给问题的求解带来了很大的困难.本文讨论了互信息函数的多极值特性,并提出了一种粒子群优化算法(particle swarm optimization,PSO)和Powell混合优化方法.经检验,这种方法能有效地克服互信息函数的局部极值,大大地提高了配准精度,达到亚像素级.     

基于CUDA的2D、3D刚性配准方法

目的:实时医学图像配准技术是外科手术导航系统的关键技术之一。在医学图像分析中,图像配准通常是一个非常耗时的操作,不利于临床实时性需求,本文研究实现了图像配准过程的加速。方法:为了提高配准速度,本文提出了一种基于CUDA(compute unified device architecture)编程模型的硬件加速配准新技术,采用并行的方法实现像素的坐标变换,线性插值,同时计算对应像素的灰度值残差。结果:配准误差为亚像素级别,配准速度要比基于CPU的配准快几十甚至上百倍。结论:该方法在保持配准精度不变的前提下,大大提高了刚性配准的速度。     

一种基于小波变换的配准特征点自动标记算法

以颅脑CT图像为研究对象,提出了一种基于小波变换的自动标记非刚性配准所需对应特征点的算法.这种算法充分考虑了颅脑CT图像的像素点及其临域的纹理特征,通过进行小波变换建立对应于每个像素点的多分辨率小波特征向量,并以小波特征向量间的差异作为判别依据,在目标图像中标记非刚性配准所需的对应特征点.一系列的实验结果表明,这种基于小波变换的算法能够准确地在目标图像中标记出配准所需的对应特征点,可以作为基于特征的非刚性配准对应特征点自动标记的参量之一.     

基于血管匹配的三维超声与CT图像的配准方法

提出一种基于血管匹配的三维超声与CT图像配准的新方法.首先,基于水平集方法自动分割出CT图像中的血管；其次,由于超声图像中的声影与血管均属于低回声区域,我们结合声影形成的物理原理及图像纹理特性,自动检测出声影区域,以提高配准的鲁棒性；最后,采用进化算法,将CT图像中分割出的血管与超声图像中低回声区域进行匹配.在肝脏体模和临床脾脏数据上进行了实验验证,自动配准的成功率在95％以上,平均目标配准误差在2 mm以内,实验结果验证了本方法的可行性.     

儿童头部标本薄层断面数据集的建立

目的 以0.1mm层厚建立儿童头部标本薄层断面数据集。 方法 选择中国8岁男孩头颅一个，处于生长发育高峰前期，发育中等，无重大疾病及畸形。标本经过包埋、固定和冷冻30d后，在山东大学医学院断层影像解剖学研究中心实施数控铣切。铣切层厚：0.1mm；铣切环境：-13℃恒低温实验室。标本铣切完毕，使用佳能EOS-1DS MarkII数码相机进行图像采集。 结果 整个头部标本共获得图像2 150张，所有原始图像保存格式为RAW，每张图片约20.4MB，共42.87GB 大小。再分别转换为PSD格式和JPG格式，用于更广泛的数据分享和交流。 结论 成功建立了中国儿童头部断层铣切解剖数据集，该数据集铣切层厚为0.1mm，可以为各种儿童头部相关研究提供解剖学基础。     

基于球形标记的多模态医学体配准

本文提出在医学多模态数据集（尤其是MRI和CT）中基于球形人造标记的体配准过程，此过程是半自动或是全自动完成的，半自动方法要求数据集中标出球形标记的近似点位置，再自动进行配准，全自动方法不需要用户的任何交互操作，即所有配准子任务（球体的分割，寻找两组球体的对应关系，最后把第一套球体映射成第二套球体的几何变换的计算）能由计算机自动执行，在全自动配准中，积聚器算法和迭代最近点算法的结合证明是一种有效的和鲁棒性好的点匹配方法。     

Effect of fiducial marker localization on stereotactic target coordinate calculation in CT slices and radiographs

Novel methods are presented which allow the estimation of the effect of stereotactic fiducial marker localization on target localization in a stereotactic surgery planning program based on the integration of image data from various modalities. The results of simulations are presented. A method for the evaluation of the effect of fiducial marker localization error on stereotactic localization, both for computed tomography (CT) and digitized radiography. Is proposed based on perturbation studies. For tomographic data, the comparison of the stability of two types of fiducial localizers: a standard commercial device based on nine (six vertical and three oblique) rods and an imaginary system with an extra set of rods, called '4-N', is discussed. Results of the perturbation studies clearly indicate the necessity of sub-pixel registration of the fiducial markers in CT. On the other hand, results demonstrate that stereotactic localization based on projection images is less sensitive to fiducial localization error than CT. Finally, the authors present an algorithm for the localization of fiducial markers to sub-pixel accuracy in radiographs acquired with a commercial angio localizer.     

A Novel Method for Constructing Histological Section Datasets of the Basal Ganglia in Digitized Human Brain

To investigate the construction of the histological section datasets in the basal ganglia of digitized human brain to provide a reference for the meso‐level histological data acquisition. A fresh adult brain from a cadaver with no neurological disease was selected, and tissue blocks of the basal ganglia in the right hemisphere was extracted using the visualization method, followed by pretreatments including gradient dehydrating, gelatin‐embedding and setting of calibration points. And then the tissue blocks was cryosectioned into 60‐μm‐thick coronal sections and the sectional images were captured simultaneously by a digital camera at a fixed position. Two series of sections (one section out of ten) were Nissl‐stained with Toluidine blue and immunostained with the calbindin D‐28K, respectively. Stained sections were digitized by a high resolution scanner. After alignment and registration, contours of nuclei and different nucleic function divisions in the digital images of stained sections were identified, and then were segmented and labeled using software exploited by ourselves. Datasets of one set of registrated serial sectional images and two sets of registrated histochemically stained images in basal ganglia area were obtained, which provide a histological reference for the neurosurgery and diagnostic imaging. a systematic method of cutting, slicing, staining, data acquisition and image registration of large tissue blocks was established, providing a reference for histological data acquisition on the digital human. Anat Rec, 300:1011–1021, 2017. © 2016 Wiley Periodicals, Inc.     

三维几何建模与仿真技术在牙颌修复中的应用

提出正常人牙颌组织从CT数据处理到目标组织几何建模及仿真设计的技术路线.将CT断层图像经过线性插值构造出三维数据场,分割出目标牙颌组织后,利用Marching Cubes算法提取牙颌组织的三角面几何模型.采用层切法重建完整的牙列咬和面,其层切精度达到0.2mm.经过配准,实现层切法和CT扫描所得图形线框的坐标拟合.然后在PowerSHAPE中对牙列单元进行三维重建,形成一套易于修改、方便组合的模型库;最后通过编程建立了牙列缺损与修复的几何仿真系统.     

Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images.

We present a validation of an intensity based two- to three-dimensional image registration algorithm. The algorithm can register a CT volume to a single-plane fluoroscopy image. Four routinely acquired clinical data sets from patients who underwent endovascular treatment for an abdominal aortic aneurysm were used. Each data set was comprised of two intraoperative fluoroscopy images and a preoperative CT image. Regions of interest (ROI) were drawn around each vertebra in the CT and fluoroscopy images. Each CT image ROI was individually registered to the corresponding ROI in the fluoroscopy images. A cross validation approach was used to obtain a measure of registration consistency. Spinal movement between the preoperative and intraoperative scene was accounted for by using two fluoroscopy images. The consistency and robustness of the algorithm when using two similarity measures, pattern intensity and gradient difference, was investigated. Both similarity measures produced similar results. The consistency values were rotational errors below 0.74 degree and in-plane translational errors below 0.90 mm. These errors approximately relate to a two-dimensional projection error of 1.3 mm. The failure rate was less than 8.3% for three of the four data sets. However, for one of the data sets a much larger failure rate (28.5%) occurred.     

Image Registration Based on Improved Mutual Information with Hybrid Optimizer

An improved image registration method is proposed based on mutual infor- mation with hybrid optimizer. Firstly, mutual information measure is combined with morphological gradient information. The essence of the gradient information is that locations a large gradient magnitude should be aligned, but also the orientation of the gradients at those locations should be similar. Secondly, a hybrid optimizer combined PSO with Powell algorithm is proposed to restrain local maxima of mutual information function and improve the registration accuracy to sub-pixel level. Lastly, muhlresolution data structure based on Mallat decomposition can not only improve the behavior of registration function, but also improve the speed of the algorithm. Experimental results demonstrate that the new method can yield good registration result, superior to traditional optimizer with respect to smoothness and attraction basin as well as convergence speed.     

Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data

Spatial and soft tissue information provided by magnetic resonance imaging can be very valuable during image-guided procedures, where usually only real-time two-dimensional (2D) x-ray images are available. Registration of 2D x-ray images to three-dimensional (3D) magnetic resonance imaging (MRI) data, acquired prior to the procedure, can provide optimal information to guide the procedure. However, registering x-ray images to MRI data is not a trivial task because of their fundamental difference in tissue contrast. This paper presents a technique that generates pseudo-computed tomography (CT) data from multi-spectral MRI acquisitions which is sufficiently similar to real CT data to enable registration of x-ray to MRI with comparable accuracy as registration of x-ray to CT. The method is based on a k-nearest-neighbors (kNN)-regression strategy which labels voxels of MRI data with CT Hounsfield Units. The regression method uses multi-spectral MRI intensities and intensity gradients as features to discriminate between various tissue types. The efficacy of using pseudo-CT data for registration of x-ray to MRI was tested on ex vivo animal data. 2D-3D registration experiments using CT and pseudo-CT data of multiple subjects were performed with a commonly used 2D-3D registration algorithm. On average, the median target registration error for registration of two x-ray images to MRI data was approximately 1 mm larger than for x-ray to CT registration. The authors have shown that pseudo-CT data generated from multi-spectral MRI facilitate registration of MRI to x-ray images. From the experiments it could be concluded that the accuracy achieved was comparable to that of registering x-ray images to CT data.     

Dynamic ventilation imaging from four-dimensional computed tomography

A novel method for dynamic ventilation imaging of the full respiratory cycle from four-dimensional computed tomography (4D CT) acquired without added contrast is presented. Three cases with 4D CT images obtained with respiratory gated acquisition for radiotherapy treatment planning were selected. Each of the 4D CT data sets was acquired during resting tidal breathing. A deformable image registration algorithm mapped each (voxel) corresponding tissue element across the 4D CT data set. From local average CT values, the change in fraction of air per voxel (i.e. local ventilation) was calculated. A 4D ventilation image set was calculated using pairs formed with the maximum expiration image volume, first the exhalation then the inhalation phases representing a complete breath cycle. A preliminary validation using manually determined lung volumes was performed. The calculated total ventilation was compared to the change in contoured lung volumes between the CT pairs (measured volume). A linear regression resulted in a slope of 1.01 and a correlation coefficient of 0.984 for the ventilation images. The spatial distribution of ventilation was found to be case specific and a 30% difference in mass-specific ventilation between the lower and upper lung halves was found. These images may be useful in radiotherapy planning.     

基于互信息的多分辨率三维脑图像配准方法

在3D多模医学图像的配准方法中，最大互信息法精度高，鲁棒性强，使用范围广，本文将归一化互信息作为相似性测度，采用不同的采样范围和采样子集，使用Powell多参数优化法和Brent一维搜索算法对3DCT，MR和PET脑图像进行了刚体配准，为了加快配准速度，使用了多分辨的金字塔方法，对PET图像采用基于坐标的阈值选取方法对图像进行分割预算法，消除了大部分放射状背景伪影，美国万德贝尔大学对结果进行的评估证明配准精度可达亚体元级。     

A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data

The paper presents a computationally efficient 3D-2D image registration algorithm for automatic pre-treatment validation in radiotherapy. The novel aspects of the algorithm include (a) a hybrid cost function based on partial digitally reconstructed radiographs (DRRs) generated along projected anatomical contours and a level set term for similarity measurement; and (b) a fast search method based on parabola fitting and sensitivity-based search order. Using CT and orthogonal x-ray images from a skull and a pelvis phantom, the proposed algorithm is compared with the conventional ray-casting full DRR based registration method. Not only is the algorithm shown to be computationally more efficient with registration time being reduced by a factor of 8, but also the algorithm is shown to offer 50% higher capture range allowing the initial patient displacement up to 15 mm (measured by mean target registration error). For the simulated data, high registration accuracy with average errors of 0.53 mm +/- 0.12 mm for translation and 0.61 +/- 0.29 degrees for rotation within the capture range has been achieved. For the tested phantom data, the algorithm has also shown to be robust without being affected by artificial markers in the image.     

基于傅里叶梅林变换的图像配准方法

目的:提出一种新的配准框架用于图像引导放射治疗系统中的2D/3D图像配准,有效降低传统方法迭代搜索时间,同时保证放射治疗要求的配准精度。方法:利用傅里叶梅林变换方法对正侧位kV图像与对应方位参考CT图像生成的数字重建放射影像（DRR）进行粗配准,根据傅里叶梅林变换计算得到的二维平移向量以及放射治疗系统的机械几何参数反推出参考CT图像的三维空间位置偏差,更新正侧位的DRR图像,最后通过正侧位kV图像与DRR图像的相似度进行精配准达到临床需求。结果:采用临床金标准数据验证方法的配准性能,实验结果表明,配准误差为0.576 5 mm,平均运行时间为3.34 s。结论:该方法鲁棒性强,对图像的噪声不敏感,人工干预少,可满足临床应用的需求。