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1.
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.  相似文献   

2.
In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial (skin marker point-based registration (PBR), and face-based surface registration) and intracranial methods (feature PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness). The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patient's intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance (MR) image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6 +/- 0.5 mm, 1.7 +/- 0.5 mm, 3.9 +/- 3.4 mm, and 2.0 +/- 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial registration methods from the earliest stages. Based on the results reported here, organ-based approaches to registration would improve this, especially for shallow lesions.  相似文献   

3.
In this paper, a novel registration method is presented for three-dimensional mapping of cardiac chambers and computed tomography (CT) segmented surfaces. The proposed method applies random perturbations to three-dimensional mapping of cardiac chambers in order to move out of local minima. It effectively solves the problem of the existing algorithm based on iterative closest point (ICP) in which the optimization often falls into local minima, and improves the registration accuracy and success rate. The registration experiment results show that the final registration accuracy and success rate of the proposed method are better than the existing ICP-based algorithm, and it can implement the registration when the three-dimensional mapping of cardiac chambers and CT segmented surface have a wide range of initial offset.  相似文献   

4.
Medical image registration is commonly required in order to combine the complementary information provided by different medical imaging modalities. In this paper, a new automatic registration scheme is proposed to register 3-D CT-MR head images and is currently tested on a clinical environment. The proposed scheme, after the preprocessing and the outer surface extraction of the data, is based on the use the rigid transformation method, in conjunction with a combination of global and local optimization techniques. Analytically, the paper exploits the optimization efficiency of three widely used optimization techniques, in obtaining the parameters of the rigid transformation model: the Downhill Simplex Method, the Genetic Algorithms and the Simulated Annealing. These optimization techniques are further combined by the sequential application of the Powell optimization method in order to refine the registration and increase its accuracy. A comparative study involving these optimization techniques in conjunction with the rigid transformation, and two other methods, the ICP and the manual methods, is also presented, for a sufficient number of clinical CT-MR brain images. Finally, quantitative and qualitative results are also presented to validate the performance of these automatic surface-based registration schemes, in terms of consistency and accuracy. Throughout of this study, the automatic registration scheme comprising of the rigid transformation in conjunction with the Simulated Annealing method sequentially combined with the Powell method has been performed superior regarding all the other compared registration schemes.  相似文献   

5.
Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5° in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation.  相似文献   

6.
Image registration of multimodality images is an essential task in numerous applications in three-dimensional medical image processing. Medical diagnosis can benefit from the complementary information in different modality images. Surface-based registration techniques, while still widely used, were succeeded by volume-based registration algorithms that appear to be theoretically advantageous in terms of reliability and accuracy. Several applications of such algorithms for the registration of CT-MRI, CT-PET, MRI-PET, and SPECT-MRI images have emerged in the literature, using local optimization techniques for the matching of images. Our purpose in this work is the development of automatic techniques for the registration of real CT and SPECT images, based on either surface- or volume-based algorithms. Optimization is achieved using genetic algorithms that are known for their robustness. The two techniques are compared against a well-established method, the Iterative Closest Point-ICP. The correlation coefficient was employed as an independent measure of spatial match, to produce unbiased results. The repeated measures ANOVA indicates the significant impact of the choice of registration method on the magnitude of the correlation (F = 4.968, p = 0.0396). The volume-based method achieves an average correlation coefficient value of 0.454 with a standard deviation of 0.0395, as opposed to an average of 0.380 with a standard deviation of 0.0603 achieved by the surface-based method and an average of 0.396 with a standard deviation equal to 0.0353 achieved by ICP. The volume-based technique performs significantly better compared to both ICP (p<0.05, Neuman Keuls test) and the surface-based technique (p<0.05, Neuman-Keuls test). Surface-based registration and ICP do not differ significantly in performance.  相似文献   

7.
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.  相似文献   

8.
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。  相似文献   

9.
10.
Automatic lumbar vertebral identification using surface-based registration   总被引:2,自引:0,他引:2  
This work proposes the use of surface-based registration to automatically select a particular vertebra of interest during surgery. Manual selection of the correct vertebra can be a challenging task, especially for closed-back, minimally invasive procedures. Our method uses shape variations that exist among lumbar vertebrae to automatically determine the portion of the spinal column surface that correctly matches a set of physical vertebral points. In our experiments, we register vertebral points representing posterior elements of a single vertebra in physical space to spinal column surfaces extracted from computed tomography images of multiple vertebrae. After registering the set of physical points to each vertebral surface that is a potential match, we then compute the standard deviation of the surface error for each registration trial. The registration that corresponds to the lowest standard deviation designates the correct match. We have performed our current experiments on two plastic spine phantoms and two patients.  相似文献   

11.
The paper presents a computationally efficient 3D-2D image registration algorithm for automatic pre-treatment validation in radiotherapy. The novel aspects of the algorithm include (a) a hybrid cost function based on partial digitally reconstructed radiographs (DRRs) generated along projected anatomical contours and a level set term for similarity measurement; and (b) a fast search method based on parabola fitting and sensitivity-based search order. Using CT and orthogonal x-ray images from a skull and a pelvis phantom, the proposed algorithm is compared with the conventional ray-casting full DRR based registration method. Not only is the algorithm shown to be computationally more efficient with registration time being reduced by a factor of 8, but also the algorithm is shown to offer 50% higher capture range allowing the initial patient displacement up to 15 mm (measured by mean target registration error). For the simulated data, high registration accuracy with average errors of 0.53 mm +/- 0.12 mm for translation and 0.61 +/- 0.29 degrees for rotation within the capture range has been achieved. For the tested phantom data, the algorithm has also shown to be robust without being affected by artificial markers in the image.  相似文献   

12.
As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4 +/- 0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust method of acquiring anatomical surfaces intraoperatively.  相似文献   

13.
Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62 % (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90 %). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.  相似文献   

14.
Aiming at transferring the preoperative planning information to the patient in oral implantology, this study presents an image guided oral implant system (IGOIS), which integrates 3D medical modelling, preoperative surgical planning and intraoperative tracking into a dental navigation system. With the fabrication of a tooth-supported polymer resin template, a non-invasive point-based registration method through ICP algorithm is explicitly discussed. The experimental test shows that deviation between planned and achieved position of the implant is 1.2 mm at the tip and 1.3 mm at the head, which thereby demonstrates that IGOIS can reach a level of accuracy where further clinical developments are feasible.  相似文献   

15.
Cumulative dose distributions in fractionated radiation therapy depict the dose to normal tissues and therefore may permit an estimation of the risk of normal tissue complications. However, calculation of these distributions is highly challenging because of interfractional changes in the geometry of patient anatomy. This work presents an algorithm for deformable structure registration of the bladder and the verification of the accuracy of the algorithm using phantom and patient data. In this algorithm, the registration process involves conformal mapping of genus zero surfaces using finite element analysis, and guided by three control landmarks. The registration produces a correspondence between fractions of the triangular meshes used to describe the bladder surface. For validation of the algorithm, two types of balloons were inflated gradually to three times their original size, and several computerized tomography (CT) scans were taken during the process. The registration algorithm yielded a local accuracy of 4 mm along the balloon surface. The algorithm was then applied to CT data of patients receiving fractionated high-dose-rate brachytherapy to the vaginal cuff, with the vaginal cylinder in situ. The patients' bladder filling status was intentionally different for each fraction. The three required control landmark points were identified for the bladder based on anatomy. Out of an Institutional Review Board (IRB) approved study of 20 patients, 3 had radiographically identifiable points near the bladder surface that were used for verification of the accuracy of the registration. The verification point as seen in each fraction was compared with its predicted location based on affine as well as deformable registration. Despite the variation in bladder shape and volume, the deformable registration was accurate to 5 mm, consistently outperforming the affine registration. We conclude that the structure registration algorithm presented works with reasonable accuracy and provides a means of calculating cumulative dose distributions.  相似文献   

16.
Aiming at transferring the preoperative planning information to the patient in oral implantology, this study presents an image guided oral implant system (IGOIS), which integrates 3D medical modelling, preoperative surgical planning and intraoperative tracking into a dental navigation system. With the fabrication of a tooth-supported polymer resin template, a non-invasive point-based registration method through ICP algorithm is explicitly discussed. The experimental test shows that deviation between planned and achieved position of the implant is 1.2 mm at the tip and 1.3 mm at the head, which thereby demonstrates that IGOIS can reach a level of accuracy where further clinical developments are feasible.  相似文献   

17.
针对基于迭代优化的传统2D-3D医学图像配准算法运行速度慢,难以达到实时配准的要求,本研究提出一种实时2D-3D配准方法。通过将空间刚体变换参数分解到两个平面上,将2D-3D配准简化为两个步骤,包含2D-2D近似刚体配准与单参数2D-3D刚体配准。同时利用深度卷积神经网络拟合患者X射线影像残差与其对应姿态差异间的非线性映射关系,从X-DRR图像对的残差回归出空间刚体变换参数。经由头颅CT数据训练后的网络,在0.04 s内完成了高精度的双X射线配准。本研究提出的配准方法满足了放疗过程中进行实时2D-3D配准工作的要求。  相似文献   

18.
Three-dimensional stereolithographic models (SL models), made of solid acrylic resin derived from computed-tomography (CT) data, are an established tool for preoperative treatment planning in numerous fields of medicine. An innovative approach, combining stereolithography with computer-assisted point-to-point navigation, can support the precise surgical realization of a plan that has been defined on an SL model preoperatively. The essential prerequisites for the application of such an approach are: (1) The accuracy of the SL models (including accuracy of the CT scan and correspondence of the model with the patient's anatomy) and (2) the registration method used for the transfer of the plan from the SL model to the patient (i.e., whether the applied registration markers can be added to the SL model corresponding to the markers at the patient with an accuracy that keeps the "cumulative error" at the end of the chain of errors, in the order of the accuracy of contemporary navigation systems). In this study, we focus on these two topics: By applying image-matching techniques, we fuse the original CT data of the patient with the corresponding CT data of the scanned SL model, and measure the deviations of defined parameter (e.g., distances between anatomical points). To evaluate the registration method used for the planning transfer, we apply a point-merge algorithm, using four marker points that should be located at exactly corresponding positions at the patient and at connective bars that are added to the surface of the SL model. Again, deviations at defined anatomical structures are measured and analyzed statistically. Our results prove sufficient correspondence of the two data sets and accuracy of the registration method for routine clinical application. The evaluation of the SL model accuracy revealed an arithmetic mean of the relative deviations from 0.8% to 5.4%, with an overall mean deviation of 2.2%. Mean deviations of the investigated anatomical structures ranged from 0.8 mm to 3.2 mm. An overall mean (comprising all structures) of 2.5 mm was found. The fiducial registration error of the point-merge algorithm ranged from 1.0 mm to 1.4 mm. The evaluated chain of errors showed a mean deviation of 2.5 mm. This study verifies that preoperative planning on SL models and intraoperative transfer of this plan with computer assisted navigation is a suitable and sufficiently reliable method for clinical applications.  相似文献   

19.
PROPELLER(推进器)采样技术能够利用K空间中心重叠采样区域的数据来估计采集过程中受检查者的运动进而加以补偿,对运动伪影的消除效果非常显著。然而,由于其重建时的运动估计是基于最大化频域空间上相关系数的配准算法,该算法为了实现旋转估计与平移估计的分离,在进行旋转估计时,仅仅采用K空间数据的模,在数据量有限的情况下造成估计精度较低,在重建图像上表现为模糊及星条状伪影。本研究基于最大化图像空间上的互信息提出一种PROPELLER采样数据的运动估计新算法,首先由每个K空间带进行傅立叶逆变换后取模重建出系列临时图像,对这些图像进行模糊增强后以互信息作为相似性测度迭代搜索最优的运动参数。实验证明,该方法能显著提高PROPELLER采样数据重建中运动估计与补偿的精度,从而更好地消除伪影,特别是用于有运动时T1加权头部成像时。  相似文献   

20.
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