一种基于模式控制自由变形的非刚性医学图像配准算法

目的 对手术前MRI／CT图像和手术中超声图像两种模态下都可见的血管结构进行配准。方法 提出一种基于自由变形模型的多模态医学图像的非刚性配准的方法。当两个图像对准时，一种图像中的血管中心点就会对应着另一种图像下灰度脊点。对于全局变换采用刚性变换，而对于局部的形变，采用一种基于模式控制B样条的自由变形模型（FFD）来描述。配准算法采用遗传算法和共轭梯度法结合的优化策略来最小化目标函数。结果 我们设计了两个实验，分别应用于体模和临床数据来评价我们的算法。这种方法是连续而且准确的。最后的变换参数的均方差值是亚像素、亚毫米级的，在0．010弧度以内的。结论 实验结果显示本方法从配准精度和收敛速度上都得到了良好的效果。可以有效地应用于超声图像导航手术系统。     

同体呼气相和吸气相肺部高分辨率CT体积图像的配准

目的 实现同一个体屏住呼吸状态下呼气相与吸气相的肺部高分辨率CT体积图像配准.方法 采集3个被试者两个屏住呼吸状态下的胸部高分辨率CT序列,共3对.利用序列分割算法提取肺组织,并分开存储左右两肺.对单侧肺的呼气与吸气图像进行配准.首先,基于解剖标志面寻找全局仿射变换参数,用此变换重采样呼气相体积图像;其次,利用"Demons"算法对两体积图像进行非刚性配准.结果 两肺的轮廓及内部结构均获得较好的配准效果.配准前,两图像的平均体积重合度为0.7982,经全局仿射变换后提高到0.8936,经"Demons"非刚性配准后增至0.9544.均方根误差值平均下降率为:19.83%(全局仿射变换之后),49.43%(Demons非刚性配准之后).结论 本文所采用的同体肺部图像的配准方法可以有效地配准两个大形变肺部体积图像,为进一步分析肺的呼吸功能奠定了良好的基础.     

三维非刚性配准提高肝脏动态增强MR测量可重复性的初步研究

目的 探讨三维非刚性配准在提高肝脏动态增强MR应用中的可重复性.方法 前瞻性分析18例行动态增强MRI检查的肝硬化背景肝细胞肝癌患者的资料.使用Omni Kinetics软件对获得的图像进行三维非刚性配准,采用Extended Tofts双输入模型对配准前、后的图像进行计算.由两位观测者采用盲法测量配准前、后同层面病灶、肝脏及竖脊肌的Ktrans、Kep值,并对测量值的变异度、一致性进行组间(两位观测者之间)及组内(一位观测者间隔一周测量两次)比较,并使用Bland-Ahman Plot对组间和组内测量值的可重复性进行分析.结果 18例三维非刚性配准后同层不同期两幅图像有差异,像素均值为32761±13575,小于配准前(43202±20884),差异有统计学意义(t=2.637,P＜0.05).相对差值百分比箱图显示组间及组内病灶、肝脏、竖脊肌Ktrans、Kep均数配准后差异均小于配准前,离群值及极值减少或消肖失;配准后组内竖脊肌Ktrans和Kep的四分位间距稍增大,余四分位间距均变小;配准后组间四分位间距均变小.配准后组内、组间病灶、肝脏、竖脊肌Ktrans、Kep可重复性均提高,均值差变小.结论 三维非刚性配准可提高病灶、肝脏及竖脊肌Ktrans、Kep的可重复性.     

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。     

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.     

基于VoxelMorph无监督缺失图像配准的无标记射束方向观肿瘤跟踪算法

目的 基于机器学习提出可应用于低图像质量、多叶准直器(MLC)遮挡和非刚性变形兆伏级(MV)图像的无标记射束方向观(BEV)肿瘤放疗跟踪算法。方法 采用窗口模板匹配法和Voxelmorph端到端无监督网络,处理MV图像中的配准问题。使用动态胸部模体,验证肿瘤跟踪算法的准确性。将模体质量保证(QA)计划在加速器上手动设置治疗偏移后执行,收集治疗过程中的682幅电子射野影像系统(EPID)图像作为固定图像;同时采集计划系统中对应射野角度的数字影像重建(DRR)图作为浮动图像,进行靶区跟踪研究。收集21例肺部肿瘤放疗的533对EPID和DRR图像进行肿瘤跟踪研究,提供治疗过程中肿瘤位置变化定量结果。图像相似度用于算法的第三方验证。结果 算法可应对不同程度(10%~80%)的图像缺失,且对数据缺失图像的非刚性配准表现较好。模体验证中86.8%的跟踪误差<3 mm,<2 mm的比例约80%作用。配准后标准化互信息(NMI)由1.18±0.02提高到1.20±0.02(t=-6.78,P=0.001)。临床病例肿瘤运动以平移为主,平均位移3.78 mm,最大位移可达7.46 mm。配准结果显示存在非刚性形变,配准后NMI由1.21±0.03增至到1.22±0.03(t=-2.91,P=0.001)。结论 肿瘤跟踪算法跟踪精度可靠且鲁棒性好,可用于无创、实时、无额外设备和辐射剂量的肿瘤跟踪。     

宫颈癌图像引导放疗技术在不同图像配准方法的应用价值

目的 探讨将不同图像配准方法用于宫颈癌图像引导放疗技术中的应用价值。方法 选取经病理确诊为宫颈癌患者150例,均经图像引导放射治疗（IGRT）,利用机载锥形束CT(CBCT)XVI系统对患者进行治疗前摆位扫描、在线调整后扫描和治疗结束后扫描。对重建中获得的CBCT图像和治疗计划系统的CT图像行手动、骨性和灰度三种配准方法,分析X（左右）、Y（头脚）、Z（前后）轴水平方向和GX、GY、GZ旋转方向的不同误差,比较这三种配准方法之间的差异性。结果 灰度配准在X水平方向的摆位误差低于手动配准和骨性配准,差异有统计学意义（P<0.05）,配准值高于手动配准和骨性配准,差异有统计学意义（P<0.05）,三种图像配准方法在Y和Z水平方向的摆位误差和配准值比较差异无统计学意义（P>0.05）;三种图像配准方法在GX、GY、GZ轴旋转方向的摆位误差和配准值比较均差异无统计学意义（P>0.05）。结论 宫颈癌图像引导放疗技术中骨性和灰度配准方法均可选用,建议首选骨性配准,必要的情况下再应用灰度配准辅之。     

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。     

颅颌面CT与MR图像的配准

目的 :实现颅颌面CT MR医学图像的配准。材料和方法 :基于轮廓特征的奇异值分解 迭代最近点法 (SingularValueDecomposition IterativeClosestPoint ,SVD ICP)。结果 :该配准操作简便、图像满意、可靠性好 ,尚可以用于任意维度向量集合的匹配。结论 :在临床实践中颅颌面CT MR医学图像的配准是可行的 ,为进一步实现图像的融合奠定了基础     

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。     

Mutual information-based multimodal image registration using a novel joint histogram estimation

Mutual information (MI)-based image registration has been proved to be very effective in multimodal medical image applications. For computing the mutual information between two images, the joint histogram needs to be estimated. As we know, the joint histogram estimation through linear interpolation and partial volume (PV) interpolation methods may result in the emergency of the local extreme in mutual information registration function. The local extreme is likely to hamper the optimization process and influence the registration accuracy. In this paper, we present a novel joint histogram estimation method (HPV) by using an approximate function of Hanning windowed sinc as kernel function of partial volume interpolation. We apply it to both rigid registration and non-rigid registration. In addition, we give a new method estimating the gradient of mutual information with respect to the model parameters during non-rigid registration. By the experiments on both synthetic and real images, it is clearly shown that the new algorithm has the ability to reduce the local extreme, and the registration accuracy is improved.     

An improved 3D shape context based non-rigid registration method and its application to small animal skeletons registration

3D shape context is a method to define matching points between similar shapes. It can be used as a pre-processing step in a non-rigid registration. The main limitation of the method is point mismatching, which includes long geodesic distance mismatch causing wrong topological structure, and neighbors crossing mismatch between two adjacent points. In this paper, we propose a topological structure verification method to correct the long geodesic distance mismatch and a correspondence field smoothing method to correct the neighbors crossing mismatch. A robust 3D shape context model is generated and further combined with thin-plate spline model for non-rigid registration. The method was tested on phantoms and applied to rat hind limb and mouse hind limb skeletons registration from micro-CT images. Errors between the registered surfaces were reduced by using the proposed method. The robustness of the method is demonstrated.     

Improvement of multislice oxygen-enhanced MRI of the lung by fully automatic non-rigid image registration

Purpose

In oxygen-enhanced magnetic resonance imaging of the lung (O2-MRI), motion artifacts related to breathing hamper the quality of the parametric O2-maps. In this study, fully automatic non-rigid image registration was assessed as a post-processing method to improve the quality of O2-MRI.

Materials and methods

Twenty healthy volunteers were investigated on a 1.5 T MR system. O2-MRI was obtained in four coronal sections using an IR-HASTE sequence with TE/TI of 12/1200 ms. Each section was repeatedly imaged during oxygen and room-air ventilation. Spatial differences among the images were corrected by fully automatic non-rigid registration. Signal variability, relative enhancement ratio between oxygen and room air images, and spatial heterogeneity of lung enhancement were assessed before and after image registration.

Results

Motion artifacts were corrected in 5–10 s. Non-rigid registration reduced signal variability of the source images and heterogeneity of the O2-maps by 1.1 ± 0.2% and 11.2 ± 2.9%, respectively (p < 0.0001). Registration did not influence O2 relative enhancement ratio (p = 0.06).

Conclusion

Fully automatic non-rigid image registration improves the quality of multislice oxygen-enhanced MRI of the lung.     

Software for image registration: algorithms,accuracy, efficacy

Image registration is finding increased clinical use both in aiding diagnosis and guiding therapy. There are numerous algorithms for registration, which all involve maximizing a measure of similarity between a transformed floating image and a fixed reference image. The choice of the similarity measure depends, to some extent, on the application. Methods based on the use of the joint intensity histogram have become popular because of their flexibility and robustness. A distinction is made between rigid-body and non-rigid transformations. The latter are needed for inter-subject registration or intra-subject registration in cases where the region of the body of interest is not considered rigid. Non-rigid transformation is normally achieved using a global model of the deformation but can also be defined by a set of locally rigid transformations, each constrained to a small block in the image. There is scope for further research on the incorporation of appropriate constraints, especially for the application of non-rigid transformations to nuclear medicine studies. Most of the initial practical concerns regarding image registration have been overcome and there is increasing availability of commercial software. There are several approaches to the validation of registration software, with validation of non-rigid algorithms being particularly difficult. Studies have demonstrated the accuracy on the order of half a pixel for both intra- and inter-modality registration (typically 2 to 3 mm). Although hardware-based registration has now become possible by using dual-modality instruments, software-based registration will continue to play an important role in nuclear medicine.     

全局异常信号环境下基于体素灰度多模医学图像配准研究

目的 在全局异常信号环境下,找出适合于临床应用的、满足精度和鲁棒性要求的基于体素灰度多模医学图像配准相似性测度。方法 结合对各种相似性测度的分析,对无异常信号的实际医学图像,和分别加了随机噪声及全局异常信号的多模医学图像进行配准精度的分析。结果 对各种已有成熟的相似性测度进行理论分析和实验对比研究的基础上,归一化互信息在全局异常信号环境下对多模医学图像进行配准,它们的配准精度和鲁棒性表现都令人满意,能得到准确的配准结果。而基于相关比和互信息的配准方法,不能准确地配准加了全局异常信号的多模医学图像。结论 相比于其他相似性测度,归一化互信息在全局异常信号环境下,是一个能满足配准精度和鲁棒性要求的合适相似性测度。     

Accuracy of semi-automated versus manual localisation of liver tumours in CT-guided ablation procedures

Objectives

To compare the accuracy of liver tumour localisation in intraprocedural computed tomography (CT) images of computer-based rigid registration or non-rigid registration versus mental registration performed by interventional radiologists.

Methods

Retrospectively (2009-2017), 35 contrast-enhanced CT (CECT) images incorporating 56 tumours, acquired during CT-guided ablation procedures and their corresponding pre-procedural diagnostic CECTs were retrieved from the picture archiving and communication system (PACS). The original intraprocedural CECTs were de-enhanced to create a virtually unenhanced CT image (VUCT). Alignment of diagnostic CECTs to their corresponding intraprocedural VUCTs was performed with non-rigid or rigid registration. Mental registration was performed by four interventional radiologists. The original intraprocedural CECT served as the reference standard. Accuracy of tumour localisation was assessed with the target registration error (TRE). Statistical differences were analysed with the Wilcoxon signed-rank test.

Results

Non-rigid registration failed to register two CT datasets, incorporating four tumours. In the remaining 33 datasets, non-rigid, rigid and mental registration showed a median TRE of 3.9 mm, 9.0 mm and 10.9 mm, respectively. Non-rigid registration was significantly more accurate in tumour centre localisation in comparison to rigid (p < 0.001) or mental registration (p < 0.001). Rigid registration was not statistically different from mental registration (p = 0.169). Non-rigid registration was most accurate in localising tumour centres in 42 out of 52 tumours (80.8%), while rigid and mental registration were most accurate in only seven (13.5%) and three (5.8%) tumours, respectively.

Conclusions

Computer-based non-rigid registration is statistically significantly more accurate in localising liver tumours in intraprocedural unenhanced CT images in comparison to rigid registration or interventional radiologists’ mental mapping abilities.

Key Points

? Computer-based non-rigid registration is better (p < 0.001) in localising target tumours prior to ablation in intraprocedural CT images in comparison to rigid registration or interventional radiologists’ mental mapping abilities. ? Human experts perform sub-optimal localisation of target tumours when relying solely on mental mapping during challenging CT-guided procedures. ? This non-rigid registration method shows promising results as a safe alternative to intravenous contrast media in liver tumour localisation prior to ablation during CT-guided procedures.

     

Adaptive ultrasound imaging of the lumbar spine for guidance of epidural anesthesia

Ultrasound imaging can help in choosing the needle trajectory for epidural anesthesia but anatomical features are not always clear. Spatial compounding can emphasize structures; however, features in the beam-steered images are not aligned due to varying speeds of sound. A non-rigid registration method, called warping, shifts pixels of the beam-steered images to best match the reference image. Linear prediction is used to find the warping vectors and decrease computational cost. An adaptive median-based combination technique for compounding is also investigated. The algorithms are tested on a spine phantom and human subjects. The results show a significant improvement in quality when using warping with adaptive median-based compounding.