带有预处理的计划CT和在线CT的形变配准方法

CT扫描中,水溶性碘造影的存在使得计划CT和在线CT图像中血管内的HU值出现非常大的偏差,从而导致计划CT和在线CT图像错配。针对该问题,本研究提出了一种基于预处理的计划CT和在线CT形变配准方法。首先,根据CT图像组织和结构的信息,利用阈值分割方法分割出血管,并将所有分割中最大的联通区域作为初始分割的强化血管;其次,利用分割得到的强化血管区域外扩5 mm,作为外扩的强化血管,并将血管用固定的HU值进行填充;最后,对完成填充后的图像利用Demons算法进行形变配准。实验结果显示本文提出的带有预处理的形变配准方法,可以较好地解决水溶性碘造影剂引起的CT错配问题。     

基于区域相似性测量的CT/PET图像配准

图像配准在临床诊断中有重要意义,针对这一问题已经提出了许多方法.本文以区域相似性匹配测度,运用改进的分割方法,结合Powell寻优算法实现了CT/PET多模医学图像配准.实验结果表明,该算法易于实现,配准速度快、精度高,鲁棒性较好.     

基于图谱的肝脏CT三维自动分割研究

目的在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤。由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一。方法本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序。首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准。配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积。结果评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部（主要在肝脏尖端）出现较大的误差。结论该方法分割临床肝脏CT图像具有可行性。     

一种病变眼底图像的配准方法

对于具有病变的眼底图像,血管结构不够清晰,采用基于血管分割和血管分支点、交叉点等眼底图像配准方法具有一定的局限性。为了解决这个问题,提出一种基于不变特征的眼底图像配准方法。提取眼底图像的尺度不变特征(SIFT)作为特征点,提出双边或的Best-Bin-First(BBF)算法进行特征点匹配,并根据特征点具有旋转不变性的方向特征和空间斜率及空间距离等几何特性的一致性检测去除误匹配,精化匹配特征,利用得到的匹配特征进行M估计得到眼底图像的变换关系。通过对不同程度病变的眼底图像数据进行配准实验,观察配准结果,对比匹配特征的正确匹配数量和分析衡量配准精度的均方根误差。结果表明,该方法实现了良好的细节对齐,保留了足够的正确匹配对,对实验的病变眼底图像配准成功后的均方根误差均小于1,且浮动于0.5左右,验证了方法的精确性和有效性。     

DSA图像中运动伪影的消除方法

由于病人存在着各种运动(如呼吸、肌肉运动、心脏运动、设备噪声),在成像过程中常会造成图像上出现伪影,干扰医生的正常诊断,为消除这种伪影,本文提出一种基于图像配准思想的全自动消除伪影的方法,该方法能够自动消除DSA图像中的大部分运动伪影,使DSA图像得到较好的增强,并为后面的血管分割和三维重建提供便利,是一种快速有效的方法.     

双源 CT 冠状动脉三维分割算法的研究

本研究旨在从心脏双源 CT 数据中自动精确分割出冠状动脉。采用一种基于多尺度滤波和概率决策的血管自动分割算法。先基于多尺度 Hessian 矩阵增强图像中的管状结构,再利用最大后验概率基于灰度将体素分为目标和背景2类,最后用26邻域区域生长法分割出左冠状动脉。实验结果表明,可精确分割出冠状动脉并提取血管中心线。该算法避免了血管泄露问题,无伪血管,无需人工交互,是一种有效的双源 CT 冠状动脉自动提取方法。     

正交小波基函数实现图像引导放射系统中计划CT与锥形束CT图像的形变配准

计划CT图像与锥形束CT(CBCT)图像的配准是基于CBCT图像引导放射系统中实现自适应放疗的重要部分。为了提高系统中形变配准的精度和速度,提出一种基于正交小波变换的形变配准方法,此方法利用正交小波变换的多分辨率特性描述计划CT和CBCT图像的全局和局部形变,由Navier偏微分方程设计极小化能量函数来实现小波系数的能量估计。实验表明,所提出方法用于基于CBCT的图像引导放射系统时,可将日常放疗时的CBCT图像和计划CT图像进行准确且快速的配准,并且可用于放射计划系统中器官的自动分割,从而有效指导自适应治疗。     

基于图像块匹配和测地线活动轮廓模型的肿瘤自动检测算法

提出一种基于图像块匹配和测地线活动轮廓模型的肿瘤自动检测算法。给定肿瘤区域的术前图像和术后(或术中)图像,首先通过刚性配准得到全局配准参数,然后将术前图像分割成大小适合的正方形区域,在术后(或术中)图像中寻找与之匹配最好的区域,并得到各个分块对应的配准参数。根据分块配准和全局配准之间关系的判决比较,得到肿瘤所在的区域,最后在此区域内利用测地线活动轮廓模型,获得较为准确的肿瘤初始轮廓。     

一种新颖的序列图像自动配准方法

序列图像的配准是医学临床与科研实践中扮演着非常重要的角色.为了快速、准确地进行医学序列图像配准,本文提出了一种利用图像联合直方图进行序列图像自动配准的新方法.首先对图像阈值分割,将其联合直方图划分为4个区域,然后根据不同的配准图像数据,选择定义在不同区域上的计数值作为参数计算的准则函数.该方法设计简单、巧妙,以计数方法代替其他方法中大量的浮点运算.由于准则函数具有良好的光滑特性,且选择Powell算法做最优化搜索,因此保证了优化结果的准确性.和其他算法相比,该方法大大简化了准则函数的计算,从而显著提高了配准优化搜索的速度.根据实验结果,及与基于互信息量方法的对比,证明该方法准确、简便、快速、有效.     

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.     

Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information

In order to utilize both ultrasound (US) and computed tomography (CT) images of the liver concurrently for medical applications such as diagnosis and image-guided intervention, non-rigid registration between these two types of images is an essential step, as local deformation between US and CT images exists due to the different respiratory phases involved and due to the probe pressure that occurs in US imaging. This paper introduces a voxel-based non-rigid registration algorithm between the 3D B-mode US and CT images of the liver. In the proposed algorithm, to improve the registration accuracy, we utilize the surface information of the liver and gallbladder in addition to the information of the vessels inside the liver. For an effective correlation between US and CT images, we treat those anatomical regions separately according to their characteristics in US and CT images. Based on a novel objective function using a 3D joint histogram of the intensity and gradient information, vessel-based non-rigid registration is followed by surface-based non-rigid registration in sequence, which improves the registration accuracy. The proposed algorithm is tested for ten clinical datasets and quantitative evaluations are conducted. Experimental results show that the registration error between anatomical features of US and CT images is less than 2 mm on average, even with local deformation due to different respiratory phases and probe pressure. In addition, the lesion registration error is less than 3 mm on average with a maximum of 4.5 mm that is considered acceptable for clinical applications.     

An Effective Non-rigid Registration Approach for Ultrasound Image Based On “Demons” Algorithm

Medical image registration is an important component of computer-aided diagnosis system in diagnostics, therapy planning, and guidance of surgery. Because of its low signal/noise ratio (SNR), ultrasound (US) image registration is a difficult task. In this paper, a fully automatic non-rigid image registration algorithm based on demons algorithm is proposed for registration of ultrasound images. In the proposed method, an “inertia force” derived from the local motion trend of pixels in a Moore neighborhood system is produced and integrated into optical flow equation to estimate the demons force, which is helpful to handle the speckle noise and preserve the geometric continuity of US images. In the experiment, a series of US images and several similarity measure metrics are utilized for evaluating the performance. The experimental results demonstrate that the proposed method can register ultrasound images efficiently, robust to noise, quickly and automatically.     

Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching

The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.     

A finite element method to correct deformable image registration errors in low-contrast regions

Image-guided adaptive radiotherapy requires deformable image registration to map radiation dose back and forth between images. The purpose of this study is to develop a novel method to improve the accuracy of an intensity-based image registration algorithm in low-contrast regions. A computational framework has been developed in this study to improve the quality of the 'demons' registration. For each voxel in the registration's target image, the standard deviation of image intensity in a neighborhood of this voxel was calculated. A mask for high-contrast regions was generated based on their standard deviations. In the masked regions, a tetrahedral mesh was refined recursively so that a sufficient number of tetrahedral nodes in these regions can be selected as driving nodes. An elastic system driven by the displacements of the selected nodes was formulated using a finite element method (FEM) and implemented on the refined mesh. The displacements of these driving nodes were generated with the 'demons' algorithm. The solution of the system was derived using a conjugated gradient method, and interpolated to generate a displacement vector field for the registered images. The FEM correction method was compared with the 'demons' algorithm on the computed tomography (CT) images of lung and prostate patients. The performance of the FEM correction relating to the 'demons' registration was analyzed based on the physical property of their deformation maps, and quantitatively evaluated through a benchmark model developed specifically for this study. Compared to the benchmark model, the 'demons' registration has the maximum error of 1.2 cm, which can be corrected by the FEM to 0.4 cm, and the average error of the 'demons' registration is reduced from 0.17 to 0.11 cm. For the CT images of lung and prostate patients, the deformation maps generated by the 'demons' algorithm were found unrealistic at several places. In these places, the displacement differences between the 'demons' registrations and their FEM corrections were found in the range of 0.4 and 1.1 cm. The mesh refinement and FEM simulation were implemented in a single thread application which requires about 45 min of computation time on a 2.6 GHz computer. This study has demonstrated that the FEM can be integrated with intensity-based image registration algorithms to improve their registration accuracy, especially in low-contrast regions.     

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。     

CT/MRI混合配准方法及其在放疗计划系统中的应用

旨在研究放疗中图像配准方法,特别是针对放疗中常用的CT、MRI,提出基于混合框架的配准方法,该方法主要包 括两个方面:（1）采用掩膜（Mask）提取感兴趣区域、形态学运算等图像处理方法以及CPU多线程并行技术,大幅度提高配 准速度;（2）采用由全局到局部的混合配准策略,首先利用基于仿射变换的刚性配准整体配准,以防止图像间偏差过大,在 此基础上针对感兴趣区域采用B样条弹性配准,调整局部形变。通过实验表明,采用预处理及加速策略的刚性配准,在保 持其精度的情况下,提速比可达10倍,测试结果已达到临床需求;此外,采用基于GPU加速的混合配准策略,其配准速度 提至约4 min。     

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。     

基于数字血管减影的无框架配准算法的实验研究

我们在无框架立体定位算法的基础上,进行了脑部多模医学图像配准的临床实验研究。实验证明,本配准算法能准确地将数字血管减影(Digital subtraction angiography,DSA)图像中的血管信息融合进计算机体层成像(Computed tomography,CT)的解剖结构中,三维显示后方便医生进行诊断和手术计划,对于计算机辅助外科手术研究具有重要的临床医学价值。     

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。     

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.