CT与MRI图像融合技术在颅颌面部的应用

采用基于轮廓特征的奇异值分解－迭代最近点的配准方法和基于小波鑫塔分解的融合方法进行颅颌面CT－MRI医学图像的融合。结果显示，该融合操作简便快捷、图像满意、可靠性好，获得好颅颌面的CT和MRI的融合图像。提示在临床实践中颅颌面CT－MRI医学图像的融合是可行的，可以从解剖结构复杂的颅颌面部图像信息中获得有利于诊断和治疗分析的图像依据。     

基于体素灰度三维多模医学图像配准中相似性测度的选取

目的：在基于体素灰度医学图像配准领域，找出最适合于临床应用的多模医学图像配准相似性测度。方法：在极端的刚体配准条件下，检验出互相关系数，互信息和相关比相似性测度为适合的相似性测度。同时进一步解释了基于互信息相似性测度的医学图像配准易于陷入局部最优，而基于相关比相似性测度的方法易于保证配准得到全局最优，最后，利用加速的多分辨率配准方案和Powell‘s优化算法，对临床医学图像进行了基于相关比相似性测度的多模图像配准试验。结果：通过临床医学专家的判断，利用相关比相似性测度进行多模医学图像配准，安全能满足临床的要求，进行MR／CT，MR／PET三维多模医学图像配准时效果非常理想，结论：相比于其他相似性测度，互相关比相似性测度在基于体素灰度，三维多模医学图像配准领域，是一个更为适宜和准确的相似性测度。     

基于Piella指数优化的CT和MR图像融合

目的 实现CT与MR图像融合,帮助医生做出准确的诊断和治疗.方法 将Piella指数最大化作为图像融合目标,通过滑动窗对配准后的原始图像分块,将每一个图像块字典编纂为列向量;通过优化算法更新图像块加权向量,使Piella指数最大;根据加权向量合并相对应的图像块,并整理得到融合图像.结果 仿真实验对20组CT和MR图像进...     

利用CT和MRI融合技术确定咀嚼肌三维肌力向量

采用基于小波金字塔分解的方法对颅颌面CT－MRI医学图像进行融合，再经可视化技术处理，以测量和分析颅颌面部各咀嚼肌的三维肌力向量，结果显示：咀嚼肌系统在功能状态下不同大小的生理模截面积和最大肌力，不同咀嚼肌以及同一咀嚼肌的不同侧别其肌力的空间方向亦各不相同，提示借助于颅颌面CT－MRI医学图像融合技术获得咀嚼肌的三维肌力向量是可行的，这将为咀嚼肌系统的基础研究提供有意义的研究结果。     

基于梯度矢量流与粒子群优化算法的多模态医学图像配准

目的研究用梯度矢量流与粒子群优化算法实现多模态医学图像配准,提高配准的精度。方法算法对图像配准的特征空间、相似性测度、搜索策略3个方面进行改进：先由原始图像产生梯度矢量流场,作为配准的特征空间;然后提出并计算3种基于梯度矢量流场的相似性测度;最后使用结合了遗传算法交叉机制的粒子群优化算法找到两幅图像的最优变换。结果对仿真及实际医学图像的54次配准实验,表明该方法配准精度优于基于像素的粒子群优化方法和Walsh变换法。结论基于梯度矢量流与粒子群优化算法的图像配准方法能有效地实现多模态医学图像的配准。     

心脏三维标测中配准方法的研究

目的对标测导管采集点与计算机断层（CT）或核磁共振（MR）成像建立的三维心脏模型进行配准。方法现有的方法通常是以三维标测点空间和CT/MR数据模型空间都是正交为前提,因此这些方法通常只调整三维平移、旋转和拉伸等9个自由度来实现配准。而我们的研究表明,这2个空间不一定同时正交。因此引入了基于12个自由度的仿射变换模型和相应的迭代最近点算法来实现配准,并研究了如何把该算法与手术操作结合起来。结合OpenGL,我们用Visual C＋＋ 6.0软件实现了该配准方法。结果仿真结果表明：平均误差由配准前的40像素降到了配准后的1.5像素;进一步的动物实验验证了该算法的有效性。结论本文提出的三维标测配准算法能够实现心脏三维标测中的配准。     

基于信号互相关函数与神经网络的全自动图像配准算法

目的对多模态非刚性变换序列图像进行配准。方法将一种新的信号处理的概念引入配准过程，以两组具有时延特性的随机信号分别描述待配准的两幅医学图像的边缘特性，继而提出一种以信号互相关函数为性能指标，通过利用神经网络的泛化能力对轮廓特征点样本进行训练以得到最优变换参数的头部断层扫描图像自动配准算法。结果仿真结果表明该算法配准误差可达到亚象素级以下，且比之其他基于形状信息的配准算法具有寻优参数少，配准时间短，自动化程度高的特点。最后该算法被成功地应用到了做过开颅手术病人的CT—MRI图像融合上。结论该方法为多模态医学图像配准提供了一种新的有效手段。     

基于薄板样条和形状内容的医学图像非刚性配准方法研究

目的 针对医学图像非刚性点配准的现状,给出一种基于点特征的非刚性配准方法.方法 利用一种新的相似度测量方法--形状内容来解决两幅图像中点的对应关系,并利用点对应关系来估计非刚性映射函数.结果 利用薄板样条实现了医学图像的快速准确非刚性配准.结论 实验结果表明,上述方法获得了很好的配准效果.     

基于体表定位的PET/CT/MRI"二机三维"图像融合的数字化对照

目的:尝试一种基于体表定位的二维图像配准方法,逐一实现PET、MRI和CT异机图像之间的精确三维融合.方法:输入PET/CT/MRI原始数据后采用数字化格式转换,设计"9点3面"立体定位法进行配准,在实时工作站Mimics按照信息交互自动融合模式,通过讯号叠加技术完成图像融合.结果:以肺癌患者的头、胸、膝为实例交叉试验CT+MRI、PET+MRI和PET+CT立体图像的异机融合,生成了分辨软、硬组织病变性质和位置的清晰互补影像.结论:这种先进的数字化融合算法对提高早期诊断和鉴别诊断具有临床意义,虽然异机融合工序目前尚未像PET+CT的同机融合那样完全成熟,但这一实验将为医学成像厂家进一步研制CT+MRI或PET+MRI同机融合设备提供经验借鉴.     

医学X线图像配准技术研究进展及其存在的问题

医学X线图像受到探测器面积大小的限制,成像范围有限,对较大器官的扫描无法一次完成.在观察病变部位时,医生需要结合多幅图像来进行诊断或治疗,因此需要对多张影像进行拼接处理.作为图像拼接技术的核心,图像配准技术已被广泛应用于医学成像中,将那些从扫描中获得的多类型信息进行配准从而得到更详细的信息.首先,本文重点综述了目前面向X线图像的比较主流和新兴的配准技术,如基于互信息的配准法,基于特征的配准法和基于变换域的配准法.其次,指出了X线图像配准中存在的影像漂移问题、拍摄角度的限制、非刚性配准仍未成熟、没有绝对的配准评价标准等问题.最后,总结了基于FPGA等硬件的医学图像配准、采用超分辨率重建技术以获取更高质量的待配准图像从而提高图像配准的精度和速度等发展趋势与研究前景.     

模拟功能咬合时人颞下颌关节内的应力分布和位移特征

为了认识正中咬合时颞下颌关节（TMJ）内的应力分布和位移特征，作者采用基于小波金字塔分解的方法对人颅颌面CT、MRI医学图像进行融合，以测量和分析颅颌面部各咀嚼肌的三维肌力向量，再借助实验获得的人TMJ各结构的材料性能参数和在活体基础上建立的模拟了关节内接触关系的TMJ三维非线性有限元模型，计算并分析正中咬合时TMJ内各结构的应力和位移。结果显示，正中咬合时关节盘、髁突和关节窝表面应力分布的范围和力值大小差异较大，以髁突受力最大，并节窝次之，而关节盘相对较小。正中咬合时关节盘，髁突和关节容向上、后及内侧移动，以髁突位移最大，关节盘次之，关节窝最小。研究表明，在临床实践中，系统、合理地计算和分析正中咬合时TMJ内的应力分布和位移特征是切实可行的，结果是满意的。     

Automatic method to assess local CT-MR imaging registration accuracy on images of the head

BACKGROUND AND PURPOSE: Precise registration of CT and MR images is crucial in many clinical cases for proper diagnosis, decision making or navigation in surgical interventions. Various algorithms can be used to register CT and MR datasets, but prior to clinical use the result must be validated. To evaluate the registration result by visual inspection is tiring and time-consuming. We propose a new automatic registration assessment method, which provides the user a color-coded fused representation of the CT and MR images, and indicates the location and extent of poor registration accuracy. METHODS: The method for local assessment of CT-MR registration is based on segmentation of bone structures in the CT and MR images, followed by a voxel correspondence analysis. The result is represented as a color-coded overlay. The algorithm was tested on simulated and real datasets with different levels of noise and intensity non-uniformity. RESULTS: Based on tests on simulated MR imaging data, it was found that the algorithm was robust for noise levels up to 7% and intensity non-uniformities up to 20% of the full intensity scale. Due to the inability to distinguish clearly between bone and cerebro-spinal fluids in the MR image (T1-weighted), the algorithm was found to be optimistic in the sense that a number of voxels are classified as well-registered although they should not. However, nearly all voxels classified as misregistered are correctly classified. CONCLUSION: The proposed algorithm offers a new way to automatically assess the CT-MR image registration accuracy locally in all the areas of the volume that contain bone and to represent the result with a user-friendly, intuitive color-coded overlay on the fused dataset.     

Aspects of MR Image Distortions in Radiotherapy Treatment Planning

BACKGROUND: Registration of computed tomography (CT) and magnetic resonance (MR) images are commonly performed to define the different target regions used in radiotherapy treatment planning (RTTP). The accuracy of target definition will then depend on the spatial accuracy of the CT and MR data, and on the technique used to register the images. CT images are usually regarded as geometrically correct, while MR images are known to suffer from geometric distortion. The aim of this paper is to discuss the possible impact of MR image distortions in the radiotherapy treatment planning process. METHODS: The origin, magnitude, and relative impact of the different sources of geometric distortions that affect the MR image data at different magnetic fields and for different acquisition settings are described. Techniques for distortion correction are reviewed, and their limitations are outlined. The sensitivity of image registration techniques to the presence of geometric distortions in the MR data is discussed. Finally, an overview of image registration techniques used and results obtained in clinical radiotherapy treatment planning applications is given. RESULTS: Spatial distortions in MR images vary with field strength and with the image acquisition protocol. The spatial accuracy generally decreases with distance from the magnet isocenter. Distortion correction techniques based on phantom evaluations cannot adequately model patient-induced distortions. CONCLUSION: Image protocols with high gradient bandwidths should be used to reduce the spatial distortions in MR images. Correction techniques based only on phantom measurements could be sufficient at low magnetic fields, while at higher fields additional corrections of patient-related distortions might be needed. Registration techniques based on matching of Landmark points located far from the magnet isocenter are especially prone to MR distortions.     

Evaluation of Four Volume-Based Image Registration Algorithms

We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images, (2) the CT and magnetic resonance (MR) phantom images, and (3) the CT and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the results were compared with anatomical landmark–based manual registration to qualitatively determine how the results were close to a clinically acceptable registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given, and final visual assessment is necessary to guarantee reasonable results.     

Evaluation of Four Volume-Based Image Registration Algorithms

We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images, (2) the CT and magnetic resonance (MR) phantom images, and (3) the CT and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the results were compared with anatomical landmark–based manual registration to qualitatively determine how the results were close to a clinically acceptable registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given, and final visual assessment is necessary to guarantee reasonable results.     

Image fusion of CT and MRI data enables improved target volume definition in 3D-brachytherapy treatment planning

PURPOSE: To integrate MRI into CT-based 3D-brachytherapy treatment planning using a software system for image registration and fusion. METHODS AND MATERIALS: Sixteen patients with recurrent head-and-neck cancer, vulvar cancer, liposarcoma, and cervical cancer were treated with interstitial (n=12) and endocavitary (n=4) brachytherapy. CT and MRI scans were performed after implantation and prior to treatment planning. Image registration to integrate the CT and MR information into a single geometric framework was performed using a software algorithm based on mutual information. Conventional 3D-brachytherapy planning based on CT-information alone was compared to brachytherapy planning based on fused CT and MRI data. The accuracy of the image fusion was measured using predefined corresponding landmarks in the CT and MRI data. RESULTS: The presented automated algorithm proved to be robust and reliable (mean registration error 1.8 mm, range 0.8-4.1 mm, SD 0.9 mm). Tumor visualization was difficult using CT alone in all cases. Brachytherapy treatment planning based on fused CT and MRI data enabled better definition of target volume and risk structures as compared to treatment planning based on CT alone. CONCLUSIONS: Image registration and fusion is feasible for afterloading brachytherapy treatment planning. Treatment planning based on fused CT and MRI data resulted in improved target volume and risk structure definition.     

Prostate cancer: precision of integrating functional MR imaging with radiation therapy treatment by using fiducial gold markers

The use of intensity-modulated radiation therapy for treatment of dominant intraprostatic lesions may require integration of functional magnetic resonance (MR) imaging with treatment-planning computed tomography (CT). The purpose of this study was to compare prospectively the landmark and iterative closest point methods for registration of CT and MR images of the prostate gland after placement of fiducial markers. The study was approved by the institutional ethics review board, and informed consent was obtained. CT and MR images were registered by using fiducial gold markers that were inserted into the prostate. Two image registration methods--a commonly available landmark method and dedicated iterative closest point method--were compared. Precision was assessed for a data set of 21 patients by using five operators. Precision of the iterative closest point method (1.1 mm) was significantly better (P < .01) than that of the landmark method (2.0 mm). Furthermore, a method is described by which multimodal MR imaging data are reduced into a single interpreted volume that, after registration, can be incorporated into treatment planning.     

Registration of three-dimensional MR and CT studies of the cervical spine

A three-dimensional image registration technique for CT and MR studies of the cervical spine was evaluated for feasibility and efficacy. Registration by means of external fiducial markers was slightly more accurate than registration by anatomic landmarks. The interrelationships between bony (eg, neural foramina) and soft tissue structures (eg, nerve roots) in the cervical spine were more conspicuous on registered images than on conventional displays. Registration of CT and MR images may be used to examine more precisely the relationships between bony and soft tissue structures of the cervical spine.