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。     

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.     

一种自动有效的电解剖图与CT曲面配准算法

目的为房颤消融手术提供一种自动精确的电解剖图与CT曲面配准算法。方法首先,以基于主轴的方法粗配准电解剖图与CT曲面,然后以基于迭代最近点的方法进一步精配准两图。结果采用Carto Merge、随机方法和本文提出的基于迭代最近点的方法分别逐一配准三组真实和三组模拟的电解剖图与CT曲面。相对Carto-Merge和随机方法,基于迭代最近点的方法配准结果稳定,精度最高,而且算法完全自动,无需任何手动操作。结论基于迭代最近点的电解剖图与CT曲面配准算法能很好地满足临床房颤消融手术的需求。     

亚像素断层图像配准数据集的建立

目的针对数字化冰冻铣切断层图像的特点,探讨一种实用的高精度图像配准方法,建立基于数字化断层图像的亚像素级配准数据集。方法采用冰冻铣切技术获取成年男性头颈标本的冰冻连续断层图像,在M atlab软件中自动提取定标点图像特征,采用基于2点的刚体变换算法实现图像的自动配准。结果配准后图像定标点与基准配准点的误差小于1个像素,达到亚像素水平。结论采用外定标的图像配准算法可建立亚像素级的配准数据集,定位标记物的准确识别是获得亚像素级配准数据集的保证。     

基于Hausdorff距离的CARTO电解剖图与CT曲面配准

我们提出一种全自动、高精度的CARTO电解剖图(EAM)与CT曲面的配准算法。算法首先以基于主轴的方法粗配准EAM与CT曲面,然后再以基于Hausdorff距离的方法进一步实现EAM与CT曲面精配准。实验结果表明,相对Carto-Merge配准软件以及随机配准算法,基于Hausdorff距离的配准算法能获得更稳定且更精确的EAM与CT曲面配准效果,同时算法完全自动,基于Hausdorff距离的EAM与CT曲面配准算法能很好地满足房颤消融手术的临床应用需求。     

基于SHOT与目标函数对称ICP的低重叠率术前术中点云配准算法

目的 配准是术前影像引导的椎弓根螺钉内固定术中的重要环节。术前CT影像三维重建获得的点云和术中捕获的暴露部位点云重叠率低，易受噪声、遮挡等因素的干扰，使点云配准更具挑战性。本文采用局部特征和距离度量相结合的方式应对低重叠率配准问题。方法 首先利用方向直方图描述子(signature of histograms of orientations, SHOT)和随机抽样一致算法(random sample consensus, RANSAC)提取并匹配几何特征相似的点，完成初始对齐。应用目标函数对称ICP,通过最小化对称化点到面目标函数得到最终变换矩阵。对来源于SpineWeb公开数据集的5组腰椎数据进行配准实验。结果 术前术中点云配准实验中平均配准误差为0.128 mm,平均运行时间为5.750 s。结论 实验结果验证了该算法在低重叠率术前术中点云配准中的有效性，使得外科医生能及时根据配准结果调整手术器械，从而提高椎弓根螺钉置入准确率。     

神经外科导航系统基于迭代局部最近点法技术的形态学三维数据场的快速配准与融合

目的：拟开发一种全自动快速配准技术,用于临床形态学三维数据场的配准与融合。方法：用本研究提出的基于迭代局部最近点法（ILCP）技术的形态学三维数据场的快速配准技术,用临床实际病例的CT、MRI图像进行配准实验。结果：实验得到了满意的全自动快速配准效果。结果显示ILCP法比普通ICP法快10-30倍,比最大互信息法快8～10倍;耗时可以为临床医生所接受。结论;所提出的方法是切实可行的,为进一步应用于临床打下了坚实的基础。     

基于弹性模型的CT与CARTO电解剖图像的配准算法

提出一种全自动、高精度的CARTO电解剖图与CT曲面配准算法。该算法采用了由粗到精的配准策略。粗配准部分分两步:首先采用刚体变换模型以及迭代最近点法,初次配准EAM与CT曲面;然后选择仿射变换模型,再次配准EAM与CT曲面。在粗配准的基础上,以基于B样条的自由形变模型进一步精确配准EAM与CT曲面。实验结果表明,相对临床常用的Carto-Merge配准软件,基于弹性模型的配准算法获得了远高于前者的EAM与CT曲面配准精度,配准效果稳定;同时算法完全自动,不需要任何手工介入。     

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.     

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。     

Model-independent, multimodality deformable image registration by local matching of anatomical features and minimization of elastic energy

With respect to the demands of adaptive and 4D-radiotherapy applications, an algorithm is proposed for a fully automatic, multimodality deformable registration that follows the concept of translational relocation of regularly distributed image subvolumes governed by local anatomical features. Thereby, the problem of global deformable registration is broken down to multiple independent local registration steps which allows for straightforward parallelization of the algorithm. In a subsequent step, possible local misregistrations are corrected for by minimization of the elastic energy of the displacement field under consideration of image information. The final displacement field results from interpolation of the subvolume shift vectors. The algorithm can employ as a similarity measure both the correlation coefficient and mutual information. The latter allows the application to intermodality deformable registration problems. The typical calculation time on a modern multiprocessor PC is well below 1 min, which facilitates almost-interactive, "online" usage. CT-to-MRI and CT-to-cone-beam-CT registrations of head-and-neck data sets are presented, as well as inhale-to-exhale registrations of lung CT data sets. For quantitative evaluation of registration accuracy, a virtual thorax phantom was developed; additionally, a landmark-based evaluation on four lung respiratory-correlated CT data sets was performed. This consistently resulted in average registration residuals on the order of the voxel size or less (3D-residuals approximately 1-2 mm). Summarizing, the presented algorithm allows an accurate multimodality deformable registration with calculation times well below 1 min, and thus bears promise as a versatile basic tool in adaptive and 4D-radiotherapy applications.     

Minimally invasive 3D data registration in computer and robot assisted total knee arthroplasty

Computer and robot assisted surgery is concerned with the improvements achievable by using computer methods and robotic devices to plan and execute surgical interventions. The registration of different coordinate frames, often achieved through the matching of 3D data sets, represents a crucial step connecting planning and execution. Orthopaedic surgery already features a number of functioning applications which include registration routines relying on presurgically implanted fiducial markers. Replacing such invasive routines with non-fiducial registration procedures is regarded as a necessary step towards a minimisation of surgical invasiveness. A minimally invasive registration technique based on the iterative closest point algorithm is presented and conceived for a specific computer and robot assisted orthopaedic reconstructive intervention, namely total knee arthroplasty. The whole surgical protocol is examined in detail and the experimental results, relative to tests performed on synthetic and animal specimens, are thoroughly reported and discussed. The authors indicate that the proposed registration approach is well-suited for the relevant application and appropriate for in vivo testing.     

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.     

Validation of a semi‐automatic co‐registration of MRI scans in patients with brain tumors during treatment follow‐up

There is an expanding research interest in high‐grade gliomas because of their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment. We have developed a two‐stage semi‐automatic method to co‐register MRI scans at different time points before and after surgical and adjuvant treatment of high‐grade gliomas. This two‐stage co‐registration includes a linear co‐registration of the semi‐automatically derived mask of the preoperative contrast‐enhancing area or postoperative resection cavity, brain contour and ventricles between different time points. The resulting transformation matrix was then applied in a non‐linear manner to co‐register conventional contrast‐enhanced T₁‐weighted images. Targeted registration errors were calculated and compared with linear and non‐linear co‐registered images. Targeted registration errors were smaller for the semi‐automatic non‐linear co‐registration compared with both the non‐linear and linear co‐registered images. This was further visualized using a three‐dimensional structural similarity method. The semi‐automatic non‐linear co‐registration allowed for optimal correction of the variable brain shift at different time points as evaluated by the minimal targeted registration error. This proposed method allows for the accurate evaluation of the treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients. Copyright © 2016 John Wiley & Sons, Ltd.     

Metallic copper as a fiducial marker for both CT and PET

There is great interest in augmenting computed tomography (CT) with information gained from other imaging modalities. Positron emission tomography (PET) provides valuable data related to patient physiology to aid in the delineation of tumor volumes. Combining the information provided by these imaging modalities requires accurate spatial registration of the two data sets. Fiducial based mapping provides straightforward registration based on corresponding landmark points or fiducials in the two image sets. When external fiducials are employed, consistent intermodality marker placement and centroid identification are essential to achieving an accurate and reliable registration. Similarity of marker design between modalities greatly aides in achieving this goal. Solid copper may serve as a fiducial marker in both CT and PET. Small spheres or wires of copper are readily visible in CT while neutron activation of these same markers produces positron emitting Copper-64 for detection by PET. The use of identical shaped markers in both imaging modalities greatly simplifies the task of intermodality centroid matching. Copper has excellent machining properties and, prior to activation, is easy and safe to handle. The feasibility of Cu as a marker for both CT and PET is demonstrated using imaging phantoms.     

Fast free-form deformable registration via calculus of variations

In this paper, we present a fully automatic, fast and accurate deformable registration technique. This technique deals with free-form deformation. It minimizes an energy functional that combines both similarity and smoothness measures. By using calculus of variations, the minimization problem was represented as a set of nonlinear elliptic partial differential equations (PDEs). A Gauss-Seidel finite difference scheme is used to iteratively solve the PDE. The registration is refined by a multi-resolution approach. The whole process is fully automatic. It takes less than 3 min to register two three-dimensional (3D) image sets of size 256 x 256 x 61 using a single 933 MHz personal computer. Extensive experiments are presented. These experiments include simulations, phantom studies and clinical image studies. Experimental results show that our model and algorithm are suited for registration of temporal images of a deformable body. The registration of inspiration and expiration phases of the lung images shows that the method is able to deal with large deformations. When applied to the daily CT images of a prostate patient, the results show that registration based on iterative refinement of displacement field is appropriate to describe the local deformations in the prostate and the rectum. Similarity measures improved significantly after the registration. The target application of this paper is for radiotherapy treatment planning and evaluation that incorporates internal organ deformation throughout the course of radiation therapy. The registration method could also be equally applied in diagnostic radiology.     

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.     

Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with normalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient.     

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.