数字化人脑图谱演示系统的设计与制作

目的：设计并制作一个高精度、高速度、又易于操作的二-维数字化人脑图谱，重建出人脑内部各个组织之间复杂的空间关系：方法：住PC机上使用自己开发的工具对采集到的二维人脑图像进行先期处理，并自动生成各组织的三维模型，然后使用VRML将三维模型组织在一起并在浏览器中显示出来：结果：成功构建出一个操作简单、效果逼真的脑图谱，适用于手术计划导航以及神经解剖教学：结论：利用可视化技术构建的数字化人脑图谱能够为医学研究、教学与临床提供形象而真实的模型，而且构造出的三维模型文件较小，适合于网络传输和资源共享：     

用于CT和MRI图像的轮廓波变换双水印算法

目的：为解决CT与MRI图像版权侵权与恶意破坏问题,通过结合轮廓波变换与斑点检测技术,提出一种基于轮廓波变换自动选择感兴趣区域（ROI）的双水印算法。方法：该算法通过斑点检测与奇异值分解,选取医学图像轮廓波低频域与诊断最相关的位置作为ROI,在ROI生成零水印图像用于保护图像的完整性,并将ROI生成的零水印与版权水印共同嵌入图像的非感兴趣区域。结果：该双水印算法与其他双水印算法相比,载体医学图像遭受攻击后的不可见性有很大提升,结构相似度达到94%以上。结论：斑点检测轮廓波双水印算法对常规攻击与几何攻击具有较好的鲁棒性,且对于载体医学图像视觉质量有明显提升。     

基于耦合配准网络的MR脑图像标签迁移算法

目的 提出一种单图谱标签迁移算法并命名为Multi-Angle，以期在队列分析中快速有效提取与神经退行性疾病相关的MR脑影像标记物和解剖结构。方法 首先对初始图谱图像施加旋转变换，获得旋转图谱图像组；其次为主配准网络送入合并后的初始图谱图像与个体图像，预测形变场及候选标签；再次为副配准网络送入合并后的旋转图谱图像与个体图像，结合主网络相关特征预测候选标签；最后通过投票法融合多个候选标签获得个体图像标签。结果 在Mindboggle101和HCP数据集的实验结果显示，Multi-Angle算法在两个测试集上重要解剖结构Dice相似性系数均值分别为76%和82%，精确率均值为74.0%和77.8%，平均表面距离均值为0.83 mm和0.69 mm，均优于目前主流算法Voxelmorph和Ants-SyN。结论 本文提出的Multi-Angle算法可以快速有效实现脑神经图谱标签迁移并提高评价指标准确度，对神经退行性疾病分析所需的影像特征提取具有潜在的临床应用价值。     

虚拟现实技术在岩骨手术解剖教学中的作用

背景：岩骨解剖形态和空间构筑关系复杂,手术教学难度大。计算机虚拟现实技术因其高效、直观、交互性等优点,用于岩骨手术解剖教学开发潜力巨大。 目的：评估虚拟现实技术建立岩骨三维解剖模型在岩骨手术教学中的价值。 方法：1例非颅底病变患者头颅CT Dicom格式数据导入虚拟现实工作站进行三维重建,利用工作站中的图像处理软件提取岩骨表面和内部解剖结构图像,构建岩骨三维解剖模型。选取志愿者40名随机分为2组,每组20名,受试组安排阅读教科书结合虚拟影像模型观察,对照组仅安排阅读教科书。在学习后1,2周分别进行相关解剖知识笔试和图谱标识测试。 结果与结论：岩骨虚拟现实解剖模型可视化效果良好。学习后1周测试,受试组解剖知识笔试和图谱标识成绩高于对照组,差异有显著性意义(P < 0.05)。学习后2周测试,受试组解剖知识笔试与对照组差异无显著性意义(P > 0.05),受试组图谱标识成绩高于对照组,差异有显著性意义(P < 0.05)。结果显示虚拟现实三维影像模型用于岩骨手术解剖教学效果良好。      

24孕周胎儿标本听小骨3.0 T MR影像的解剖形态观察

目的探讨24孕周胎儿标本听小骨3.0 T MR影像的解剖形态。方法以1具24孕周流产胎儿冷冻标本为研究对象, 女性, 发育指标正常, 双顶径62 mm, 头围22.3 cm, 腹围18.9 cm, 身长27.5 cm, 体质量580 g。使用西门子3.0 T MR对胎儿标本的双侧颞骨进行扫描, 获取中耳的各向同性薄层MR断层图像。选取山东大学解剖教研室胎儿标本库中的1具25孕周发育指标正常的女性胎儿标本的颞骨CT扫描图像, 以及1例来山东省妇幼保健院就诊的发育正常的2岁儿童颞骨CT图像, 将CT图像与24孕周流产胎儿的MRI进行形态结构对照。观察项目:观察胎儿听小骨的MRI信号特点;比较CT图像与MRI对听小骨的显示效果;经多向调整多平面重组(MPR), 分别显示锤骨、砧骨、镫骨及相关结构的典型断层解剖形态, 标识重要解剖结构;通过最大密度投影(MIP)重组对听骨链进行三维显示。结果 (1)MRI信号特点:胎儿听小骨在MR T2WI上显示为低信号, 其中骨化完全部分呈明显低信号、未完全骨化部分为较低信号;中耳鼓室中充满羊水, 表现为均匀T2WI高信号, 在羊水信号衬托下, 听小骨可清晰...     

基于SE序列的MR图像权重计算方法

目的：探究一种基于SE序列的MR图像权重计算方法，为判定sE加权像提供更加科学及准确的判定依据。方法：提出一种计算两种生物组织的质子密度特性差异、纵向弛豫时间特性差异及横向弛豫时间特性差异各自对磁共振图像对比度的贡献比例方法，首先，将磁共振图像对比度公式进行等量变换，建立SE序列权重对比度数学关系式；其次，对该关系式进行对数变换，在对数坐标下，乘性权重因子转化为加性权重因子；最后，利用比例计算公式计算生物组织的三种特性差异各自对磁共振图像的贡献比例。结果：利用医用核磁共振仪器，通过对重复时间及回波时间不同设置后，获得两幅关于白质和灰质的MR图像，然后利用本文介绍的基于SE序列的MR图像权重计算方法，获得质子密度特性差异、纵向弛豫时间特性差异及横向弛豫时间特性差异各自对MR图像对比度贡献比例，并显示于这两幅MR图像上．可定量判定SE加权像。结论：通过本文介绍的基于sE序列的MR图像权重计算方法，对精确判定磁共振SE加权像有极大的帮助。     

基于curvelet变换快速迭代收缩阈值算法的压缩采样磁共振图像重建

目的 为提高MR图像的重建效果和降低重建图像边缘模糊,本文提出一种基于curvelet变换的MRI快速迭代收缩阈值算法(fast iterative shrinkage-thresholding algorithm,FISTA)。方法 利用curvelet变换多尺度、各向奇异性、对图像边缘有更好的几何表达等特性,将curvelet稀疏变换和FISTA结合,并与传统基于小波变换的FISTA对相同MR图像作重建对比。重建图像的质量以峰值信噪比(peak signal to noise ratio,PSNR)、均方误差(mean square error,MSE)、结构相似性度(structural similarity degree,SSIM)来衡量。结果 实验选用Lena图像和脑部MR图像,从重建图像细节、差值图像、评估参数三方面对算法重建效果进行比较分析,证明该curvelet-FISTA算法可有效恢复完全采样图像从核磁共振成像中的欠采样数据。结论 与传统基于小波变换的FISTA相比,该方法可以更好地保持重建图像的细节信息,并有效地消除图像边缘的模糊现象,显示了较好的重建效果。     

Rician噪声水平场的估计及其在MR图像去噪中的应用

针对MR图像中空间变化Rician噪声的抑制问题,提出了一种噪声水平场的估计方法,同时结合方差稳定变换和BM3D算法实现MR图像的去噪.噪声水平场通过Rician噪声水平的局部估计和稀疏性约束模型进行估计,利用噪声水平场对噪声图像幅值进行空间自适应方差稳定变换,使得噪声与信号幅值和空间位置无关,采用BM3D算法即可实现对噪声的抑制,最后通过方差稳定逆变换得到无偏的去噪图像.仿真实验中,噪声水平场估计的平均相对误差小于0.2％,利用空间自适应方差稳定变换进行去噪,相比方差稳定变换,去噪图像的峰值信噪比可提高2 dB;采用真实乳腺MR图像进行去噪实验,利用自适应方差稳定变换可得到较高的Q度量.结果表明,所提出的方法能有效估计Rician噪声水平场,并用于抑制MR图像中空间变化的噪声.     

足月孕妇腹盆部的矢状断层影像解剖学研究

目的　为给围产期孕妇腹、盆部B超、MR诊断提供矢状断面解剖学基础。 方法　将1例足月妊娠尸体做腹盆部MR扫描,获取图像数据。用电动带锯按等距法制成与正中矢状面平行的腹、盆部连续矢状断层标本,并获取连续矢状断层标本图像数据。 结果　足月孕妇腹盆部矢状断面的解剖结构在标本和MR图像上均得到了很好地显示;胎盘和宫内胎儿主要脏器显示层面与母体椎骨之间的有较为固定的对应关系。 结论　足月孕妇连续矢状断层标本、MR图像分析结果能够为围产期孕妇腹、盆部B超、MR诊断提供正常的形态学资料。     

乳腺肿瘤超声图像中感兴趣区域的自动检测

由于斑点噪声、伪影以及病灶形状多变的影响,乳腺肿瘤超声图像中肿瘤区域的自动检测以及病灶的边缘提取比较困难,已有的方法主要是由医生先手工提取感兴趣区域(ROI)。本研究提出一种乳腺肿瘤超声图像中感兴趣区域自动检测的方法,选用超声图像的局部纹理、局部灰度共生矩阵以及位置信息作为特征,采用自组织映射神经网络进行分类,自动识别乳腺肿瘤区域。对包含168幅乳腺肿瘤超声图像的数据库进行识别的结果表明:该方法自动识别ROI的准确率达到86.9%,可辅助医生提取肿瘤的实际边缘以及进一步诊断。     

基于伪超声和互信息的磁共振与超声图像配准

手术中超声图像与术前磁共振图像的配准在手术导航系统中非常重要。我们利用磁共振和超声成像原理,提出了基于伪超声和互信息,并结合多分辨率技术与Powell优化算法对两种模态图像进行配准的方法,该方法可以有效降低寻优过程中陷入局部极值收敛的概率,提高两种模态图像的配准精度。实验结果表明,我们提出的基于伪超声和互信息的配准方法比目前手术导航系统中普遍采用的标记点方法具有更高的配准精度。     

Three-Dimensional Registration of Magnetic Resonance Image Data to Histological Sections with Model-Based Evaluation

We developed a three-dimensional (3D) registration method to align medical scanner data with histological sections. After acquiring 3D medical scanner images, we sliced and photographed the tissue using, a custom apparatus, to obtain a volume of tissue section images. Histological samples from the sections were digitized using a video microscopy system. We aligned the histology and medical images to the reference tissue images using our 3D registration method. We applied the method to correlate in vivo magnetic resonance (MR) and histological measurements for radio-frequency thermal ablation lesions in rabbit thighs. For registration evaluation, we used an ellipsoid model to describe the lesion surfaces. The model surface closely fit the inner (M1) and outer (M2) boundaries of the hyperintense region in MR lesion images, and the boundary of necrosis (H1) in registered histology images. We used the distance between the model surfaces to indicate the 3D registration error. For four experiments, we measured a registration accuracy of 0.96± 0.13 mm (mean±SD) from the absolute distance between the M2 and H1 model surfaces, which compares favorably to the 0.70 mm in-plane MR voxel dimension. This suggests that our registration method provides sufficient spatial correspondence to correlate 3D medical scanner and histology data.     

Computerized method for nonrigid MR-to-PET breast-image registration

We have developed and tested a new simple computerized finite element method (FEM) approach to MR-to-PET nonrigid breast-image registration. The method requires five-nine fiducial skin markers (FSMs) visible in MRI and PET that need to be located in the same spots on the breast and two on the flanks during both scans. Patients need to be similarly positioned prone during MRI and PET scans. This is accomplished by means of a low gamma-ray attenuation breast coil replica used as the breast support during the PET scan. We demonstrate that, under such conditions, the observed FSM displacement vectors between MR and PET images, distributed piecewise linearly over the breast volume, produce a deformed FEM mesh that reasonably approximates nonrigid deformation of the breast tissue between the MRI and PET scans. This method, which does not require a biomechanical breast tissue model, is robust and fast. Contrary to other approaches utilizing voxel intensity-based similarity measures or surface matching, our method works for matching MR with pure molecular images (i.e. PET or SPECT only). Our method does not require a good initialization and would not be trapped by local minima during registration process. All processing including FSMs detection and matching, and mesh generation can be fully automated. We tested our method on MR and PET breast images acquired for 15 subjects. The procedure yielded good quality images with an average target registration error below 4 mm (i.e. well below PET spatial resolution of 6-7 mm). Based on the results obtained for 15 subjects studied to date, we conclude that this is a very fast and a well-performing method for MR-to-PET breast-image nonrigid registration. Therefore, it is a promising approach in clinical practice. This method can be easily applied to nonrigid registration of MRI or CT of any type of soft-tissue images to their molecular counterparts such as obtained using PET and SPECT.     

Matching of digitised brain atlas to magnetic resonance images

A method has been developed to match a standard digitised brain atlas onto MR images for identification of cerebral structures in anatomical images. This method uses, first, a three-dimensional crude registration based on the proportional system of Talairach. Then, a two-dimensional refined registration is performed using a deformation function based on a set of homologous landmarks on both images (MR and atlas). Displacements vectors are computed between each corresponding landmark. These vectors are interpolated by thin-plate splines, generating an unwarping function defined on the whole image. This function can then be applied on any structure of the atlas. An evaluation of the matching procedure has been performed. First, the influence of the choice of the landmarks has been evaluated for the fine registration method. The latter has been then compared to the crude registration method considered as a classical reference method. These results show the advantages of the fine registration approach.     

Robust and fast shell registration in PET and MR/CT brain images

A robust and fast hybrid method using a shell volume that consists of high contrast voxels with their neighbors is proposed for registering PET and MR/CT brain images. Whereas conventional hybrid methods find the best matched pairs from several manually selected or automatically extracted local regions, our method automatically selects a shell volume in the PET image, and finds the best matched corresponding volume using normalized mutual information (NMI) in overlapping volumes while transforming the shell volume into an MR or CT image. A shell volume not only can reduce irrelevant corresponding voxels between two images during optimization of transformation parameters, but also brings a more robust registration with less computational cost. Experimental results on clinical data sets showed that our method successfully aligned all PET and MR/CT image pairs without losing any diagnostic information, while the conventional registration methods failed in some cases.     

Use of simulated annealing for optimization of alignment parameters in limited MRI acquisition volumes of the brain

Studies suggest that clinical outcomes are improved in repeat trigeminal neuralgia (TN) Gamma Knife radiosurgery if a different part of the nerve from the previous radiosurgery is treated. The MR images taken in the first and repeat radiosurgery need to be coregistered to map the first radiosurgery volume onto the second treatment planning image. We propose a fully automatic and robust three-dimensional (3-D) mutual information- (MI-) based registration method engineered by a simulated annealing (SA) optimization technique. Commonly, Powell's method and Downhill simplex (DS) method are most popular in optimizing the MI objective function in medical image registration applications. However, due to the nonconvex property of the MI function, robustness of those two methods is questionable, especially for our cases, where only 28 slices of MR T1 images were utilized. Our SA method obtained successful registration results for all the 41 patients recruited in this study. On the other hand, Powell's method and the DS method failed to provide satisfactory registration for 11 patients and 9 patients, respectively. The overlapping volume ratio (OVR) is defined to quantify the degree of the partial volume overlap between the first and second MR scan. Statistical results from a logistic regression procedure demonstrated that the probability of a success of Powell's method tends to decrease as OVR decreases. The rigid registration with Powell's or the DS method is not suitable for the TN radiosurgery application, where OVR is likely to be low. In summary, our experimental results demonstrated that the MI-based registration method with the SA optimization technique is a robust and reliable option when the number of slices in the imaging study is limited.     

利用相关比相似性测度多分辨率配准MR和CT医学图像的方法

本文提出了有效的、能被临床应用所接受的磁共振（MR）和CT医学图像配准方法。在基于体素灰度的医学图像配准领域。本文采用了全新的相关比相似性测度作为配准的测度准则。具体设计时，采用了加速的多分辨率配准方案，对方案中涉及的几何变换选取、重采样、多分辨率体数据表达及最优化方法进行了设计分析。最后，利用本文提出的多分辨率配准方法，对MR和CT临床医学图像进行配准，给出了令人满意的效果。     

基于最大互信息的人脑MR-PET图像配准方法

利用最大互信息法进行多模医学图像配准近来成为医学图像处理领域的热点。MR和PET图像配准对研究神经组织的结构关系和引导神经外科手术有着重要的指导意义。本文描述了一种基于互信息的人脑MR－PET图像配准方法。我们将这种方法应用于图像的几何对准并给出了初步的评估结果。由于不需要对不同成像模式下的图像灰度间的关系作任何假设,最大互信息法是一种稳健性强,可广泛应用于基于体素的多模图像的配准方法。     

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。     

Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.