基于B样条的弹性配准在放疗CT图像间配准的应用

目的:应用B样条弹性模型研究肿瘤患者放疗前CT图像与放疗后CT图像之间的弹性配准。方法:利用B样条自由弹性模型(free-form deformations,FFD),以灰度差平方和(Sum of Squared Differences,SSD)为相似性测度函数,通过基于多分辨率的B样条弹性配准方法对同一患者放疗前与放疗后的CT图像进行配准。结果:图像通过高斯滤波处理,应用B样条弹性模型,得到较为理想的弹性配准实验结果。结论:基于B样条的CT与CT图像弹性配准可以较好地建模放疗前与放疗后肿瘤区域的形变,为临床分析肿瘤的变化提供支持。     

基于局部互信息和自动更新的医学图像形变不均匀配准

针对互信息只考虑图像像素的灰度信息和图像存在形变不均匀的情况下,本文提出局部互信息和部分多层次B样条结合的方法。第一步采用主轴质心法对多模医学图像进行粗配准,从而快速实现两幅图像的粗配准。第二步采用部分多层次B样条法针对解决局部形变不均匀的配准对象,首先是粗网格进行全局粗配准,然后只是对部分区域实现细化网格处理,加快配准速度。文中对网格进行自动更新,将采用以局部互信息为相似度检测,结合这3种方法,从而实现多模医学图像的精确和快速配准。     

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。     

基于多目标交叉变异粒子群算法的多模医学图像非刚性配准

以互信息为相似性测度,采用B样条变换对多模态医学图像进行非刚性配准时,由于噪声及图像插值等原因造成的互信息局部极值使得传统优化方法不能搜索到最佳配准参数。为此,使用粒子群智能优化方法作为搜索策略,以降低对图像预处理的要求,进一步提高基于互信息的非刚性配准的鲁棒性。为了克服粒子群算法受初始值选取等因素的影响易陷于局部最优的缺点,使用LBFGS优化得到的结果构造初始粒子群,采用多目标优化方法结合交叉变异策略加以改进,使得算法在解空间搜索的遍历性得到改善,优化结果更接近全局最优。MR-T2与MR-PD图像的配准实验证明,该方法提高了基于互信息的B样条非刚性配准的鲁棒性,配准率达到94%;CT与PET图像的配准实验表明该方法相比惯性权重粒子群算法提高了配准精度,互信息增加了0.026;另外,CT与CBCT图像的配准实验也验证了本方法的有效性。     

HIFU治疗中基于呼吸和脉搏信号的B超图像配准

目的：超声引导的HIFU治疗系统在临床治疗中,在同一治疗层面上,医生需要移动B超探头对比不同位置的图像定位靶区,本研究提出了一种基于呼吸和脉搏信号的动态B超图像配准方法。方法：根据呼吸和脉搏信号周期出现的特点,探头先运动到理想位置连续采集大概10 s的B超图像作为参考图像,同时记录每帧参考图像采集时刻的呼吸和脉搏信号作为参考信号。探头再返回到治疗位置通过DTW算法配准实时的呼吸和脉搏信号,找到配准信号对应的参考图像再补偿B超探头移动导致的图像位移,最终实现实时图像与参考图像的配准。结果：在实验中,通过调节B超探头与人体皮肤的距离,找到理想位置采集B超图像和呼吸脉搏信号作为参考数据,再回到治疗位置开始B超图像的配准,配准结果发现该技术具有很高的准确性和稳定性。结论：该技术能有效实现动态B超图像配准,也解决了模糊图像无法配准的难题。     

基于分段B样条形变配准方法的头颈部伪CT生成研究

目的:探讨分段B样条形变配准方法在头颈部伪CT（sCT）生成中的应用,以及对sCT生成精度的影响。方法:收集已经进行调强放射治疗的鼻咽癌患者45例,每例计划均包括头颈部T1加权核磁共振成像（MRI）和CT图像。使用3D Slicer软件对MRI和CT图像分别进行分段B样条形变配准、整体B样条形变配准、分段刚性配准和整体刚性配准4种方法配准,比较配准后的MRI图像和真实CT图像的Dice相似性系数（DSC）值。随机选取其中的30例患者作为训练集,15例患者为测试集,将配准后的MRI和CT图像通过pix2pix网络进行模型训练生成sCT,对生成的sCT和真实CT进行平均绝对误差（MAE）、结构相似性系数（SSIM）和峰值信噪比（PSNR）值的比较,分析通过阈值法分割为不同组织（骨头、软组织、空气和脂肪）的MAE值。结果:配准后的MRI和真实CT图像比较,分段B样条形变配准方法的DSC值最优;使用4种配准方法生成的sCT和真实CT图像进行MAE、SSIM和PSNR值比较,分段配准方法比整体配准方法好,B样条形变配准方法比刚性配准方法好。分段B样条形变配准方法的MAE值为（74.783±9.869） HU,SSIM值为0.839±0.032,PSNR值为（28.859±0.957） dB,均比其余几种配准方法好。分段B样条形变配准方法在骨头、软组织和脂肪区域的MAE值较好,但是在空气区域的MAE值较刚性配准方法稍差。结论:分段B样条形变配准方法在头颈部sCT生成精度优于整体和刚性配准方法,该方法可以改善头颈部图像的配准精度,从而改善sCT的生成精度。 【关键词】分段B样条形变配准方法;头颈部;伪CT; Dice相似性系数;平均绝对误差     

基于隐含形状表示和边缘信息融合的非刚体图像配准

本研究提出基于隐含形状表示和边缘信息融合的多分辨率网格非刚体图像配准算法，使用从全局到局部的层次变换模型覆盖整个变换域，解决有较大局部形变的图像配准问题。首先用隐含形状表示图像的外部轮廓，将轮廓作为距离函数的零水平集隐含地嵌入到高一维的距离变换空间，在该隐含嵌入空间中使用互信息方法，实现了一个具有平移、旋转、尺度不变性的全局配准框架，对齐图像外部轮廓。然后选择基于B样条的多分辨率网格FFD模型进行局部配准，兼顾了结果精确度和计算效率。算法采用了与图像边缘信息融合的方法，强调图像边缘信息在配准中的贡献，得到平滑、连续且保证一对一映射的变换域。最后将该算法分别应用于脑部MR、CT图像的配准，得到令人满意的效果。     

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。     

一种基于图像形态尺度变化的矢/冠状面图像重建算法

为了得到高质量的矢/冠状面(Sagittal/Coronal)重建图像,提出一种基于图像形态尺度变化的插值重建算法.对整型B样条曲面引入控制点权重,并将权重作为图像值的函数,利用B样条曲面的局部支撑性,由图像值对插值曲面的局部形状进行控制.该算法充分考虑了断层间解剖组织分布形态尺度变化的因素,因此计算精度高,插值图像具有较高的保形性,断层间插值层的过度连续性为C(n)(n>1).     

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.     

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。     

An adaptive irregular grid approach for 3D deformable image registration

Deformable registration is an important application in medical image analysis and processing. We propose a physics-based parametric approach for deformable image registration, where non-rigid transformations are computed using an irregular grid of control points distributed within the image domain. The image is modelled as a three-dimensional (3D) homogeneous infinite elastic medium. It is assumed that a Gaussian-shaped force is applied at every control point, where the strengths, directions and influence areas of the forces as well as the positions of the control points are considered as free parameters whose optimization leads to maximization of the similarity measure between the images to be registered. For optimization, a computationally efficient Levenberg-Marquardt method is used. The proposed approach has certain advantages over traditional landmark-based methods or the registration methods based on regular grids, for example B-splines, since comparable results can be achieved by using less control points. Experimental results with 3D clinical images demonstrate that our method is capable of successfully coping with complex registration tasks.     

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.     

Auto-adjusted 3-D optic disk viewing from low-resolution stereo fundus image

Three-dimensional (3-D) visualization of the optic nerve head (optic disk) is very useful for clinical applications. It allows clinicians to measure the disk parameters more accurately and thus make the pathological diagnosis and progression monitoring easier. This paper describes an automatic, precise, 3-D optic nerve head reconstruction method from a pair of stereo images for which efficient steps including sparse-image registration and dense-depth recovery are used. A combination of two registration methods is used to detect the sub-pixel correspondences. The proposed method takes advantages of both the correlation methods which is robust to noise and the feature-based method on its accuracy. The searching range in image registration is auto-adjusted based on the previous iteration result. Only sparse matched points are computed to speed up the processing and the sub-pixel matching is used to overcome the problem of low resolution in the image. This is followed by the piecewise cubic interpolation to obtain the dense disparities and depths. Multiple windowing is applied here by first using the large window to obtain basic disparities followed by the small window and previous basic disparities to measure details. The result is then smoothed and displayed as the final 3-D shape.     

基于圆形网格化的磁共振PROPELLER旋转校正算法

PROPELLER数据采集成像技术利用K空间中心重叠采样区域的数据来估计受检查者的运动并加以校正,能较好地消除运动伪影。本文提出了一种基于圆形网格化的PROPELLER旋转校正算法,将中心重叠区域数据网格化到圆形的网格点上,避免了旋转估计时需多次网格化的缺点;并提出有效的相似性测度公式,通过计算测度值估计相应的旋转运动参数,据此对各数据带进行旋转校正。实验表明,与传统旋转校正算法相比,该算法运行速度快,成像质量好。     

B-spline interpolation for bend intra-oral radiographs

Digital subtraction radiography (DSR) has been demonstrated to improve the detection of minute bone changes during the diagnosis of Periodontal diseases. However, during a dental X-ray session, it is not possible to control the bending of the X-ray film when installed in a patient's mouth. This leads to errors in the subsequent analysis. Error compensation can be done by a warping transformation, in conjunction with a reference wire grid attached to the X-ray film. However, the wire grid has to be attached to the film manually. The grid itself is a source of error in that it obscures the image and causes information loss. In this paper we propose a compensation method without the use of a reference grid. It is a simple algorithm based on B-spline interpolation and image scaling. No prior information about the bending is required. When compared with warping transformation, the method induces less disturbance to the pixel values during the compensation process. As a result, the proposed method produces more accurate image data for subsequent analysis.