灰度与形状同步匹配的非刚性配准研究

基于灰度的非刚性配准算法一般假设参考图像和浮动图像对应结构之间的灰度保持一致,然而在基于图谱的图像配准应用中,这种假设往往不符合实际。本文在给出一种可以同时校正灰度和形状差异的弹性配准算法的同时,针对该算法不能校正局部微小形变的弱点,提出采用自由项变换的方法进行校正以提高配准精度。配准实验基于20个IBSR真实脑部MRI图像,结果表明配准后图像与参考图像间的互相关系数得到明显提高。实验证明,本文提出的方法不仅能够同时校正形状差异和灰度变化,而且具有较高的配准质量。     

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。     

多模态图像配准在HIFU定位系统中的应用

在交互式图像导航HIFU(高强度聚焦超声)治疗系统中,需要对病灶目标进行非常精确的实时成像和定位.而现有的超声成像技术很难单独完成这个任务.本文提出了一种用手术前MRI三维图像与手术中的超声图像进行配准的方法,对手术前MR图像和手术中超声图像两种模态下都可见的血管进行配准.配准算法采用遗传算法和共轭梯度法结合的优化策略来最小化目标函数,并设计了两个实验对配准方法进行评价,从实验结果看这种方法从配准精度和收敛速度上都要优于另外的两种经典算法.     

Fast and Accurate Registration Techniques for Affine and Nonrigid Alignment of MR Brain Images

Registration of magnetic resonance brain images is a geometric operation that determines point-wise correspondences between two brains. It remains a difficult task due to the highly convoluted structure of the brain. This paper presents novel methods, Brain Image Registration Tools (BIRT), that can rapidly and accurately register brain images by utilizing the brain structure information estimated from image derivatives. Source and target image spaces are related by affine transformation and non-rigid deformation. The deformation field is modeled by a set of Wendland’s radial basis functions hierarchically deployed near the salient brain structures. In general, nonlinear optimization is heavily engaged in the parameter estimation for affine/non-rigid transformation and good initial estimates are thus essential to registration performance. In this work, the affine registration is initialized by a rigid transformation, which can robustly estimate the orientation and position differences of brain images. The parameters of the affine/non-rigid transformation are then hierarchically estimated in a coarse-to-fine manner by maximizing an image similarity measure, the correlation ratio, between the involved images. T1-weighted brain magnetic resonance images were utilized for performance evaluation. Our experimental results using four 3-D image sets demonstrated that BIRT can efficiently align images with high accuracy compared to several other algorithms, and thus is adequate to the applications which apply registration process intensively. Moreover, a voxel-based morphometric study quantitatively indicated that accurate registration can improve both the sensitivity and specificity of the statistical inference results.     

基于回归预测的肺4D-CT多相位配准

针对由于灰度不均和局部形变较大引起的肺4D-CT图像配准精度不足问题,提出基于回归的逐块预测初始形变的方法。新方法的核心思想是:配准一幅浮动图像至参考图像时,利用与浮动图像相对应的不同相位的图像信息进行形变场预测。首先,利用已有配准算法配准不同相位的图像至参考图像,得到各图像对应的形变场;再将图像和对应形变场分块作为训练集,利用多维支持向量回归机建立回归模型;将浮动图像分块输入回归模型中,预测出初始形变场,从而得到中间图像,并最终细化配准中间图像与参考图像。采用由德克萨斯安德森肿瘤中心DIR实验室采集并公开的数据集,评价所提出的算法。实验量化评价结果表明,与传统的Active Demons算法、Spectral Log-Demons算法相比,图像的均方误差平方和显著降低(Active Demons算法49.34±23.92,Spectral Log-Demons算法31.81±15.09,所提出算法18.97±5.75,P<0.05),相关系数显著提高(Active Demons算法0.952±0.022,Spectral Log-Demons算法0.967±0.015,所提出算法0.980±0.006,P<0.05)。同时,视觉评价结果显示,所提出算法能够获得更准确的配准图像。     

Retrospective Evaluation of PET-MRI Registration Algorithms

The purpose of this study is to evaluate the accuracy of registration positron emission tomography (PET) head images to the MRI-based brain atlas. The [¹⁸F]fluoro-2-deoxyglucose PET images were normalized to the MRI-based brain atlas using nine registration algorithms including objective functions of ratio image uniformity (RIU), normalized mutual information (NMI), and normalized cross correlation (CC) and transformation models of rigid-body, linear, affine, and nonlinear transformations. The accuracy of normalization was evaluated by visual inspection and quantified by the gray matter (GM) concordance between normalized PET images and the brain atlas. The linear and affine registration based on the RIU provided the best GM concordance (average similarity index of 0.71 for both). We also observed that the GM concordances of linear and affine registration were higher than those of the rigid and nonlinear registration among the methods evaluated.     

基于最大互信息的人脑多模图像快速配准算法

对脑图谱开发过程中来源于不同成像设备的多模图像进行配准。对预处理后的数码图像和MRI图像,首先提取图像的轮廓,采用基于轮廓的力矩主轴法计算初始平移量和旋转量,然后设定初始缩放系数,将此初始配准参数作为改进单纯形法的初始参数,以互信息作为相似性测度迭代搜索,使互信息最大,从而实现最佳配准。结果表明本算法不需要人为预调整待配准图像的分辨率,自动化程度高,配准速度快,精度较高,能够满足脑图谱开发过程中的多模图像配准要求。     

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.     

引入高斯函数的互信息法多模态图像配准

目的：最大互信息作为相似度测量在医学图像配准中已被广泛应用。在计算图像互信息时,为了避免引入新的灰度值一般采用部分体积插值统计两幅图像的联合直方图。但用该方法计算中,当图像平移整数点时,统计联合直方图会出现缺陷,使目标函数出现局部极值,从而造成误配准。方法：将高斯函数引入到直方图统计中,选取适当的邻域,用高斯函数计算邻域内各点像素对联合直方图的贡献。利用高斯函数的平滑性,避免了在互信息计算过程中统计图像联合直方图时出现误差。使用Powell优化方法,寻找最佳的优化参数,实现图像的最佳配准。结果：采用CT-PET数据进行实验,该方法平滑了目标函数,有效地消除了局部极值,提高了多模态图像配准的精确性,并且,对噪音图像配准也产生很好的效果。结论：该方法适用于多模态医学图像配准,克服了传统互信息计算时的不足,提高了配准的正确率和精确度。     

基于特征提取的脑部医学图像配准研究

目的 基于特征的配准算法具有鲁棒性强、针对性好等显著优势,在图像配准领域被广泛应用,但是该类方法的精度受图像间特征构建和环境噪声影响大,该研究旨在对其缺点进行改进。方法 该研究基于SURF和ORB两种算法,提出了SURF-ORB算法,将参考图像与待配准图像分成上下两部分分别配准。在配准过程中,首先对SURF提取的图像特征点的Harris响应值进行优化,并使用灰度质心法确定特征点主方向。然后计算rBRIEF（旋转BRIEF）描述子,并使用汉明距离进行特征点匹配。最后加入RANSAC精匹配算法,剔除误匹配点。结果和结论 该研究通过对比分析SURF、ORB、SURF-ORB这3种算法的配准结果、抗噪声能力及多模态配准能力,验证了SURF-ORB算法具有较高的配准精度、配准速度和抗噪声能力。文章的创新之处该研究首次将SURF和ORB两种算法进行结合并应用于脑部横断面图像。     

Image registration with auto-mapped control volumes

Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction, in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable registration of inhale and exhale phases of a lung 4D CT. Algorithm convergence was confirmed by starting the registration calculations from a large number of initial transformation parameters. An accuracy of approximately 2 mm was achieved for both deformable and rigid registration. The proposed image registration method greatly reduces the complexity involved in the determination of homologous control points and allows us to minimize the subjectivity and uncertainty associated with the current manual interactive approach. Patient studies have indicated that the two-step registration technique is fast, reliable, and provides a valuable tool to facilitate both rigid and nonrigid image registrations.     

Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation

A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point (ICP) algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.     

3D Elastic Registration of Ultrasound Images Based on Skeleton Feature

INTRODUCTION The development of3D imaging has attracted great attention in the field of med-ical imaging by recent years.A majority of investigations in ultrasound imaging sys-tem have also focused on3D ultrasound image reconstruct system.All those recon-struct system based on recombination of2D images has a same condition that spatialposition of object being scanned remains unchanged as time passed by.Only in thisway,3D figure of human’s organ can be reconstructed by2D images obtained…     

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.     

基于先验知识和MRF随机场模型的医学图像弹性配准方法

本研究提出了一种新的基于先验知识的弹性配准算法，首次把马尔可夫模型应用于图像的弹性配准方面。为了把关于变形场的先验知识融合到弹性配准过程中，本研究以马尔可夫随机场模型作为理论框架，以B样条为基函数来构造弹性变形模型，以弹性模型的B样条系数作为待估参数，以原图像和变形图像作为已知条件，把弹性变形模型和关于变形场的先验知识有机的融合到了马尔可夫随机场模型中，实现了一种基于变形场先验知识的弹性配准算法。这种新算法因为有变形场的先验知识，所以可以得到更好配准结果。本研究以变形场的平滑作为先验知识，可以有效改善局部极值的状况，提高算法的可靠性和鲁棒性。本研究分别对2D和3D图像进行了试验，试验结果证明了这种算法的有效性。     

基于调幅-调频与熵图多模态医学图像的配准

背景：基于传统互信息量的多模态医学图像配准方法配准时需要利用二维直方图或者Parzen窗函数的方法估计概率密度分布,进而计算互信息量,这种方式计算速度慢,而且只考虑了图像的灰度信息,容易出现误配。 目的：针对目前主流的配准方法鲁棒性差、耗时的缺点,提出了一种新的基于调幅-调频(AM-FM)特征互信息量的快速配准方法。 方法：该方法考虑了图像的空间和结构信息;首先通过AM-FM模型对图像进行分解,得到图像的AM-FM特征,与图像的灰度特征一起组成高维特征;然后利用熵图和最小生成树加快AM-FM特征互信息量的计算,从而实现了医学图像的快速配准。 结果与结论：对20组磁共振T1-T2加权图像、CT/正电子发射计算机断层成像图像进行了实验,结果表明该方法在图像空间分辨率较低,有噪声影响等情况下均可以达到较好的结果,且配准精度优于国际上的主流方法,具有计算速度快,精度高,鲁棒性强的特点,适于临床应用。     

Registration and geometric modelling of the spine during scoliosis surgery: a comparison study of different pre-operative reconstruction techniques and intra-operative tracking systems

During scoliosis instrumentation surgery, it is difficult for surgeons fully to track vertebral motion in 3D, because only the posterior elements of the spine are exposed. Different intra-operative modelling approaches are evaluated using a registration technique that matches intra-operative measurements with a 3D pre-operative model of the spine. Two tracking systems (magnetic digitiser and mechanical arm) and two pre-operative reconstruction techniques (multiplanar radiography and CT scan) are sequentially combined to build four intra-operative models. Their accuracy is assessed by comparison with the pre-operative geometry. The most minimally invasive approach (multiplanar radiographic reconstruction and magnetic digitiser) has an accuracy of 5.9 mm in translation, and errors on vertebral rotations are 4.4 degrees, 6.7 degrees and 5.0 degrees in the frontal, sagittal and transverse planes, respectively. With CT scan reconstruction, the accuracy is significantly increased by about 2 mm in translation and as much as 4.5 degrees for vertebral rotations in the sagittal plane. For the mechanical arm, the accuracy is increased by less than 1 mm in translation and 1 degree for vertebral rotations. CT scan is the most accurate reconstruction technique, but its use for long spinal segments is generally not allowed because of the high radiation exposure. Multiplanar radiographic reconstruction may be an alternative solution for long spinal segments when great accuracy is not necessary. Considering the small increase in accuracy and its awkwardness, the use of the mechanical arm may not be appropriate during surgical manoeuvres.     

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.     

A Neural Network Based Method for Optical Patient Set-up Registration in Breast Radiotherapy

Patient set-up optimization is required in breast-cancer radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. Opto-electronic systems allow implementing automatic procedures to minimize the positional mismatches of light-reflecting markers located on the patient surface with respect to a corresponding reference configuration. The same systems are used to detect the position of the irradiated body surface by means of laser spots; patient set-up is then corrected by matching the control points onto a CT based reference model through surface registration algorithms. In this paper, a non-deterministic approach based on Artificial Neural Networks is proposed for the automatic, real-time verification of geometrical set-up of breast irradiation. Unlike iterative surface registration methods, no passive fiducials are used and true real-time performance is obtained. Moreover, the non-deterministic modeling performed by the neural algorithm minimizes sensitivity to intra-fractional and inter-fractional non-rigid motion of the breast. The technique was validated through simulated activities by using reference CT data acquired on four subjects. Results show that the procedure is able to detect and reduce simulated set-up errors and revealed high reliability in patient position correction, even when the surface deformation is included in testing conditions.     

Deformable Registration for Longitudinal Breast MRI Screening

MRI screening of high-risk patients for breast cancer provides very high sensitivity, but with a high recall rate and negative biopsies. Comparing the current exam to prior exams reduces the number of follow-up procedures requested by radiologists. Such comparison, however, can be challenging due to the highly deformable nature of breast tissues. Automated co-registration of multiple scans has the potential to aid diagnosis by providing 3D images for side-by-side comparison and also for use in CAD systems. Although many deformable registration techniques exist, they generally have a large number of parameters that need to be optimized and validated for each new application. Here, we propose a framework for such optimization and also identify the optimal input parameter set for registration of 3D T₁-weighted MRI of breast using Elastix, a widely used and freely available registration tool. A numerical simulation study was first conducted to model the breast tissue and its deformation through finite element (FE) modeling. This model generated the ground truth for evaluating the registration accuracy by providing the deformation of each voxel in the breast volume. An exhaustive search was performed over various values of 7 registration parameters (4050 different combinations of parameters were assessed) and the optimum parameter set was determined. This study showed that there was a large variation in the registration accuracy of different parameter sets ranging from 0.29 mm to 2.50 mm in median registration error and 3.71 mm to 8.90 mm in 95 percentile of the registration error. Mean registration errors of 0.32 mm, 0.29 mm, and 0.30 mm and 95 percentile errors of 3.71 mm, 5.02 mm, and 4.70 mm were obtained by the three best parameter sets. The optimal parameter set was applied to consecutive breast MRI scans of 13 patients. A radiologist identified 113 landmark pairs (~?11 per patient) which were used to assess registration accuracy. The results demonstrated that using the optimal registration parameter set, a registration accuracy (in mm) of 3.4 [1.8 6.8] was achieved.