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。     

利用互信息量准则实现医学序列图像的广义配准

通过扫描仪输入计算机的医学序列图像由于平移和旋转而失去配准,本文利用互信息量准则来解决这一问题。它是从信息解释的角度,认为在配准的情况下同一序列相邻两幅图像之间的互信息量为最大。此准则的意义在于通过各图像之间的配准来获取帧间相关,从而为医学序列图像的压缩提供前提条件。实验证明利用互信息量准则解决医学序列图像配准问题具有令人满意的结果。     

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.     

Evaluation of three types of reference image data for external beam radiotherapy target localization using digital tomosynthesis (DTS)

Digital tomosynthesis (DTS) is a fast, low-dose three-dimensional (3D) imaging approach which yields slice images with excellent in-plane resolution, though low plane-to-plane resolution. A stack of DTS slices can be reconstructed from a single limited-angle scan, with typical scan angles ranging from 10 degrees to 40 degrees and acquisition times of less than 10 s. The resulting DTS slices show soft tissue contrast approaching that of full cone-beam CT. External beam radiotherapy target localization using DTS requires the registration of on-board DTS images with corresponding reference image data. This study evaluates three types of reference volume: original reference CT, exact reference DTS (RDTS), and a more computationally efficient approximate reference DTS (RDTSapprox), as well as three different DTS scan angles (22 degrees, 44 degrees, and 65 degrees) for the DTS target localization task. Three-dimensional mutual information (MI) shared between reference and onboard DTS volumes was computed in a region surrounding the spine of a chest phantom, as translations spanning +/-5 mm and rotations spanning +/-5 degrees were simulated along each dimension in the reference volumes. The locations of the MI maxima were used as surrogates for registration accuracy, and the width of the MI peaks were used to characterize the registration robustness. The results show that conventional treatment planning CT volumes are inadequate reference volumes for direct registration with on-board DTS data. The efficient RDTSapprox method also appears insufficient for MI-based registration without further refinement of the technique, though it may be suitable for manual registration performed by a human observer. The exact RDTS volumes, on the other hand, delivered a 3D DTS localization accuracy of 0.5 mm and 0.50 along each axis, using only a single 44 degrees coronal on-board DTS scan of the chest phantom.     

Rigid Registration of Medical Images Using 1D and 2D Binary Projections

Image registration is a necessary procedure in everyday clinical practice. Several techniques for rigid and non-rigid registration have been developed and tested and the state-of-the-art is evolving from the research setting to incorporate image registration techniques into clinically useful tools. In this paper, we develop a novel rigid medical image registration technique which incorporates binary projections. This technique is tested and compared to the standard mutual information (MI) methods. Results show that the method is significantly more accurate and robust compared to MI methods. The accuracy is well below 0.5° and 0.5 mm. This method introduces two more improvements over MI methods: (1)for 2D registration with the use of 1D binary projections, we use minimal interpolation; and (2) for 3D registration with the use of 2D binary projections the method converges to stable final positions, independent of the initial misregistration.     

Comparison of rigid and elastic matching of dynamic magnetic resonance mammographic images by mutual information

The present study aims at (i) the evaluation of the performance of a rigid and of an elastic matching algorithm for the coregistration of dynamic magnetic resonance (MR) images visualizing the female breast, and (ii) the evaluation of the mutual information (MI) as a matching criterion. To this end, ten patient data sets were analyzed. The comparison was performed with respect to the achieved increase in the MI and by visual inspection of the dynamic image series in a continuous film sequence ((cine mode). In most cases, the achieved increase in MI by elastic image registration is much higher than that achieved by rigid registration. Only for three of the ten data sets could the MI be increased by rigid image registration to a similar or even larger degree than by elastic image registration. Taking into account the results of the visual inspection of the rigid and elastic matched data sets, however, the elastic match leads to equal or better results for all data sets. Therefore, elastic matching is the gold standard for the registration of dynamic MR mammographic images of the female breast. The comparison of the increase in MI and the visual inspection further shows that the visual impression agrees in most cases with the result of the calculation of the MI. Therefore, the MI proves to be a suitable matching criterion for the type of data sets studied.     

基于改进最大互信息法的MR切片图像配准

医学图像配准是医学图像处理分析的关键步骤,是医学图像融合首先要解决的问题。本研究的主要目的是实现帕金森患者深脑部刺激手术前后MR图像的配准。将互距离引入互信息测度,实现手术前后两组MR切片图像的对应匹配,然后将对应的两组MR切片系列重建三维图像,最后用Powell优化算法对重建的三维图像进行配准。通过术前术后MR三维图像的配准,可以定量的分析手术后植入电极和手术前丘脑底核的相对位置关系,从而实现对深脑部刺激手术质量的科学评估。     

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。     

Targeted 2D/3D registration using ray normalization and a hybrid optimizer

X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell).     

Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes

Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62 % (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90 %). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.     

Fast Mutual Information Registration Method of 3-D Medical Image

INTRODUCTION　　Multimodal registration,which brings images from different modality into spa-tial correspondence,is of importance in many clinical applications.Over the years,research of multimodal registration has produced a lotof differentmethods.Surveysof medical image registration with a classification have been made〔1,2〕.The registra-tion methods can be classified as frame- based,point- landmark- based,surface- basedand voxel- based.Voxel- based methods achieve registration by iden…     

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.     

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。     

Development and validation of a CT-3D rotational angiography registration method for AVM radiosurgery

In this paper a novel technique is proposed and validated for radiosurgery treatment planning of arteriovenous malformations (AVMs). The technique was developed for frameless radiosurgery by means of the CyberKnife, a nonisocentric, linac-based system which allows highly conformed isodose surfaces to be obtained, while also being valid for other treatment strategies. The technique is based on registration between computed tomography (CT) and three-dimensional rotational angiography (3DRA). Tests were initially performed on the effectiveness of the correction method for distortion offered by the angiographic system. These results determined the registration technique that was ultimately chosen. For CT-3DRA registration, a twelve-parameter affine transformation was selected, based on a mutual information maximization algorithm. The robustness of the algorithm was tested by attempting to register data sets increasingly distant from each other, both in translation and rotation. Registration accuracy was estimated by means of the "full circle consistency test." A registration quality index (expressed in millimeters) based on these results was also defined. A hybrid subtraction between CT and 3DRA is proposed in order to improve 3D reconstruction. Preprocessing improved the ability of the algorithm to find an acceptable solution to the registration process. The robustness tests showed that data sets must be manually prealigned within approximately 15 mm and 20 degrees with respect to all three directions simultaneously. Results of the consistency test showed agreement between the quality index and registration accuracy stated by visual inspection in 20 good and 10 artificially worsened registration processes. The quality index showed values smaller than the maximum voxel size (mean 0.8 mm compared to 2 mm) for all successful registrations, while it resulted in much greater values (mean 20 mm) for unsuccessful registrations. Once registered, the two data sets can be used for CyberKnife treatment planning. Target delineation is performed on 3DRA while dose calculation and DRR generation are performed on CT. In conclusion, a method was developed for using 3DRA images for AVM frameless radiosurgery treatment planning. The method proved to be feasible, robust, and accurate for clinical use. 3DRA can be performed at different times or locations compared to standard, frame based stereotactic angiography. Unlike two-dimensional angiography, 3DRA allows examination of the shape of the AVM and of the surrounding target from any arbitrary point of view during treatment planning. The method can be applied to any case of intermodality registration, is operator-independent, and allows estimation of registration quality. Further research is desirable to improve time resolution in order to distinguish between feeding and draining vessels.     

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。     

从MRI体数据提取大脑表面形态的新方法

人脑形态研究在许多领域都有日益广泛的应用。将人的大脑从MR图像体数据中提取出来是脑的可视化、形态学分析、脑图象配准等多种工作的重要基础。本文结合解剖学知识、用基于灰度梯度的极坐标搜索方法从人脑MR图像自动剔除大脑皮层和颅骨及其它非脑成分,仅保留大脑部分。此后,对于3D图像遍历,我们使用移动立方体法根据全部256种构型,通过查表法把位于大脑表面上的像素与大脑内部及图像背景分开,得到准确的大脑表面轮廓。然后计算该表面轮廓的各个小三角形面片法线,结合适当的光照模型实现大脑表面形态的真实再现。大脑的提取工作是在轴向的方向完成的。但与轴向图正交的冠状图和矢状图显示结果表明,本算法对大脑部分的提取无论从哪一个方向来看都是很成功的。     

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.     

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.     

Medical Image Registration Based on Phase Congruency and Regional Mutual Information

In this paper, a new approach of multimodality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. Firstly, instead of standard mutual information ( MI ) based on joint intensity histogram, regional mutual information ( RMI ) is employed, which allows neighborhood information to be taken into account. Secondly, a new feature images obtained by means of phase congruency are invariants to brightness or contrast changes. By incorporating these features and intensity into RMI, we can combine the aspects of both structural and neighborhood information together, which offers a more robust and a high level of registration accuracy.