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.     

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。     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

管道骨骼线三维重建在肝脏可视化的应用

目的 研究三维重建数字化虚拟肝脏的方法.方法 将肝脏管道灌注后的肝脏标本进行螺旋CT扫描,获取CT扫描连续图像数据集.然后使用面绘制移动立方体(MC)算法重建肝脏及其内部管道结构表面模型,并对模型进行平滑和简化.确定出管道树上的关键节点,并使用改进的种子生长法生成管道树.将生成管道的表面模型和管道树相结合实现交互式分析.结果 肝脏管道灌注和铸型良好,螺旋CT扫描获取连续肝脏断面图像数据集242张.基于骨骼线提取的肝脏管道结构三维重建肝脏模型形态逼真,交互性强,通过设定各结构的透明度和颜色能单独或组合显示肝脏、肝静脉和下腔静脉、门静脉、胆囊,并可通过旋转、放大、缩小模型观察各结构.结论 基于肝脏管道骨骼线的方法进行肝脏及其管道系统三维重建可视化肝脏,生成肝脏和内部管道系统,立体空间感强,交互性好.     

基于傅里叶梅林变换的图像配准方法

目的:提出一种新的配准框架用于图像引导放射治疗系统中的2D/3D图像配准,有效降低传统方法迭代搜索时间,同时保证放射治疗要求的配准精度。方法:利用傅里叶梅林变换方法对正侧位kV图像与对应方位参考CT图像生成的数字重建放射影像（DRR）进行粗配准,根据傅里叶梅林变换计算得到的二维平移向量以及放射治疗系统的机械几何参数反推出参考CT图像的三维空间位置偏差,更新正侧位的DRR图像,最后通过正侧位kV图像与DRR图像的相似度进行精配准达到临床需求。结果:采用临床金标准数据验证方法的配准性能,实验结果表明,配准误差为0.576 5 mm,平均运行时间为3.34 s。结论:该方法鲁棒性强,对图像的噪声不敏感,人工干预少,可满足临床应用的需求。     

An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy

Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5 min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations (prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.     

Automatic re-contouring in 4D radiotherapy

Delineating regions of interest (ROIs) on each phase of four-dimensional (4D) computed tomography (CT) images is an essential step for 4D radiotherapy. The requirement of manual phase-by-phase contouring prohibits the routine use of 4D radiotherapy. This paper develops an automatic re-contouring algorithm that combines techniques of deformable registration and surface construction. ROIs are manually contoured slice-by-slice in the reference phase image. A reference surface is constructed based on these reference contours using a triangulated surface construction technique. The deformable registration technique provides the voxel-to-voxel mapping between the reference phase and the test phase. The vertices of the reference surface are displaced in accordance with the deformation map, resulting in a deformed surface. The new contours are reconstructed by cutting the deformed surface slice-by-slice along the transversal, sagittal or coronal direction. Since both the inputs and outputs of our automatic re-contouring algorithm are contours, it is relatively easy to cope with any treatment planning system. We tested our automatic re-contouring algorithm using a deformable phantom and 4D CT images of six lung cancer patients. The proposed algorithm is validated by visual inspections and quantitative comparisons of the automatic re-contours with both the gold standard segmentations and the manual contours. Based on the automatic delineated ROIs, changes of tumour and sensitive structures during respiration are quantitatively analysed. This algorithm could also be used to re-contour daily images for treatment evaluation and adaptive radiotherapy.     

Performance of a feature-based algorithm for 3D-3D registration of CT angiography to cone-beam CT for endovascular repair of complex abdominal aortic aneurysms

Background

A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy of a new fully automatic, feature-based algorithm for 3D3D registration of CTA to CBCT.

Methods

The feature-based algorithm was tested on clinical image datasets from 14 patients undergoing complex endovascular aortic repair. Deviations in Euclidian distances between vascular as well as bony landmarks were measured and compared to an intensity-based, normalized mutual information algorithm.

Results

The results for the feature-based algorithm showed that the median 3D registration error between the anatomical landmarks of CBCT and CT images was less than 3 mm. The feature-based algorithm showed significantly better accuracy compared to the intensity-based algorithm (p?<?0.001).

Conclusion

A feature-based algorithm for 3D image registration is presented.

     

2D/3D registration of endoscopic ultrasound to CT volume data

This paper describes a computer-aided navigation system using image fusion to support endoscopic interventions such as the accurate collection of biopsy specimens. An endoscope provides the physician with real-time ultrasound (US) and a video image. An image slice that corresponds to the corresponding image from the US scan head is derived from a preoperative computed tomography (CT) or magnetic resonance image volume data set using oblique reformatting and displayed side by side with the US image. The position of the image acquired by the US scan head is determined by a miniaturized electromagnetic tracking system (EMTS) after calibrating the endoscope's scan head. The transformation between the patient coordinate system and the preoperative data set is calculated using a 2D/3D registration. This is achieved by calibrating an intraoperative interventional CT slice with an optical tracking system (OTS) using the same algorithm as for the US calibration. The slice is then used for 2D/3D registration with the coordinate system of the preoperative volume. The fiducial registration error (FRE) for the US calibration was 2.0 mm +/- 0.4 mm; the interventional CT FRE was 0.36 +/- 0.12 mm; and the 2D/3D registration target registration error (TRE) was 1.8 +/- 0.3 mm. The point-to-point registration between the OTS and the EMTS had an FRE of 0.9 +/- 0.4 mm. Finally, we found an overall TRE for the complete system to be 3.9 +/- 0.6 mm.     

A deformable image registration method to handle distended rectums in prostate cancer radiotherapy

In image-guided adaptive radiotherapy, it is important to have the capability to automatically and accurately delineate the rectal wall, which is a major dose-limiting organ in prostate cancer radiotherapy. As image registration is a process to find the spatial correspondence between two images, a major challenge in intensity-based deformable image registration is to deal with the situation where no correspondence exists for some objects between the two images to be registered. One example is the variation of rectal contents due to the presence and absence of bowel gas. The intensity-based deformable image registration methods alone cannot create the correct spatial transformation if there is no correspondence between the source and target images. In this study we implemented an automatic image intensity modification procedure to create artificial gas pockets in the planning computed tomography (CT) images. A diffusion-based deformable image registration algorithm was developed to use an adaptive smoothing algorithm to better handle large organ deformations. The process was tested in 15 prostate cancer cases and 30 daily CT images containing the largest distended rectums. The manually delineated rectums agreed well with the autodelineated rectums when using the image-intensity modification procedure.     

Automatic CT-SPECT registration of livers treated with radioactive microspheres

We have developed an automated technique to accurately register the CT and SPECT scans of the liver of patients treated with radioactive microspheres for tumour targeting assessment. An anthropomorphic phantom was used to validate the accuracy of the registration algorithm. The phantom liver and three fiducial markers placed on its surface were filled with 99mTc. The phantom was scanned with CT and SPECT scanners in different positions. The liver volume was contoured on the CT scans from which a three-dimensional liver mask was created. By constraining the liver volume to the volume obtained from the CT scans, another liver mask was automatically created from the SPECT images. An adaptive simulated annealing algorithm was used to minimize the difference between the two volumes formed by the two sets of masks. The algorithm involved rigid transformation of the SPECT mask to reach the optimization goal. The accuracy of the algorithm was evaluated by the superposition of the fiducial markers on the CT and SPECT. The registered SPECT overlaid on the CT scan of the phantom showed congruence of the fiducial markers on CT and SPECT images within 1 mm. The technique was applied to a patient image set who received the microsphere infusion procedure. The registered CT and SPECT images of the patient showed that the majority of the activity was concentrated in the tumour, indicating a successful tumour targeting.     

Respiratory motion correction for PET oncology applications using affine transformation of list mode data

Respiratory motion is a source of artefacts and reduced image quality in PET. Proposed methodology for correction of respiratory effects involves the use of gated frames, which are however of low signal-to-noise ratio. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. We have implemented an affine transformation of list mode data for the correction of respiratory motion over the thorax. The study was performed using datasets of the NCAT phantom at different points throughout the respiratory cycle. List mode data based PET simulated frames were produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an affine registration and were subsequently applied on the original list mode data. The corrected and uncorrected list mode datasets were subsequently reconstructed using the one-pass list mode EM (OPL-EM) algorithm. Comparison of corrected and uncorrected respiratory motion average frames suggests that an affine transformation in the list mode data prior to reconstruction can produce significant improvements in accounting for respiratory motion artefacts in the lungs and heart. However, the application of a common set of transformation parameters across the imaging field of view does not significantly correct the respiratory effects on organs such as the stomach, liver or spleen.