多回路联合配准误差估计

目的基于多回路图像配准的质量评估算法(assessing quality using image registration circuits,AQUIRC)通过构建多回路图像配准网络,可直接利用空间变换场信息估计配准误差。本文研究了AQUIRC算法估计仿射变换相关的配准误差的性能,旨在给出对该算法较为全面的认识。方法采用脑磁共振(magnetic resonance,MR)图像设计不同形变程度的旋转、缩放、错切、平移等线性变换构造模拟误差,并在无差别均匀采样的空间位置处分析AQUIRC算法估计的配准误差与目标配准误差(target registration error,TRE)的线性相关性,给出二者的区域分布情况。结果在旋转、缩放、错切误差实验中,算法估计的配准误差与TRE呈较强相关,且区域分布基本一致。在平移误差实验中,二者不存在线性相关关系。将平移变换与其他类型足够大的变换复合构造模拟误差,则可一定程度上提高二者的相关度。结论 AQUIRC算法能够准确估计旋转、缩放、错切变换相关的配准误差,但不能正确估计单一平移变换造成的配准误差。     

基于最大互信息的人脑多模图像快速配准算法

对脑图谱开发过程中来源于不同成像设备的多模图像进行配准。对预处理后的数码图像和MRI图像,首先提取图像的轮廓,采用基于轮廓的力矩主轴法计算初始平移量和旋转量,然后设定初始缩放系数,将此初始配准参数作为改进单纯形法的初始参数,以互信息作为相似性测度迭代搜索,使互信息最大,从而实现最佳配准。结果表明本算法不需要人为预调整待配准图像的分辨率,自动化程度高,配准速度快,精度较高,能够满足脑图谱开发过程中的多模图像配准要求。     

Robust image registration for functional magnetic resonance imaging of the brain

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.     

Construction and evaluation of an average CT brain image for inter-subject registration

An average CT brain image is constructed to serve as reference frame for inter-subject registration. A set of 96 clinical CT images is used. Registration includes translation, rotation, and anisotropic scaling. A temporary average based on a subset of 32 images is constructed. This image is used as reference for the iterative construction of the average CT image. This approach is computationally efficient and results in a consistent registration of the 96 images. Registration of new images to the average CT is more consistent than registration to a single CT image. The use of the average CT image is illustrated.     

A Projection-Based Image Registration Algorithm and Its Application

Summary: Proposed is a projection-based image registration technique where, by rearranging the projections of characteristic images, the image registration is implemented with two independent steps - rotation and translation, to perform the two-dimensional or three-dimensional rigid-body image registration addressing the head motion problem in functional magnetic resonance imaging (fMRI). For a 2D problem, the approach is based on a one-dimensional projection of a segmented two-dimensional characteristic image, in which the translation and rotation parameters are obtained with a one-dimensional cross-correlation-based estimator. This is then used to compute the cross-correlation between the projection of an image and a registration table that is created by rearranged projections of a selected two-dimensional image with various rotation angles. In this approach, the translation registration table may be created by rearranged projections of sub-voxel level two-dimensional images with various sub-voxel level parameters, and so it may be applied into a sub-voxel registration. Such an approach replaced the general multi-dimensional optimization procedure with a linear projection calculation and a finite cross-correlation with a registration table, thus the amount of computation is considerably reduced. The performance of this method was confirmed by simulation study different SNRs and applications to 2D and 3D actual functional MRI images. Supported by the 973 Project number 2003CB716106, NSFC 90208003, #30130180 and #30200059, TRAPOYT, Doctor training Fund of MOE, PRC, Fok Ying Tong Education Foundation (91041). The authors wish to thank the Wellcome Department of Imaging Neuroscience for the permission of using the fMRI experimental data.     

锥形束CT图像引导乳腺癌放疗中不同配准方法研究

目的：比较锥形束CT(CBCT)图像引导乳腺癌放疗中采用不同配准方法[3D配准（平移）和6D配准（平移+旋转）对配准精度的影响,为选择合理的图像引导方法提供临床依据。方法：回顾性分析18例乳腺癌患者共101次治疗前摆位CBCT图像,将CBCT图像与计划CT图像分别采用3D灰度配准和6D灰度配准方法进行配准,比较和分析两种配准方法的3D平移方向的配准偏差差异并计算其平移差别,进一步统计分析旋转角度偏差与平移差别的相关性并做线性模拟。结果：3D和6D两种配准方法得到不同的平移配准结果,其中在头脚（Y）方向[（-1.47±3.00）mm vs(-0.87±3.27)mm]和前后（Z）方向[（-2.91±4.49）mm vs(-3.41±5.38)mm]的差异存在统计学意义（P<0.001）。旋转角度偏差与平移差别存在相关性,其中ΔX和ΔZ均与Ry呈线性强相关性,其相关系数PCC分别为-0.883和0.795(P<0.001);ΔY与Rx呈线性强负相关性,PCC=-0.722(P<0.001)。根据线性模拟公式计算,当Rx>1°且Ry>2°时,两种配准方法将在各平...     

Patient positioning method based on binary image correlation between two edge images for proton-beam radiation therapy

A new technique based on normalized binary image correlation between two edge images has been proposed for positioning proton-beam radiotherapy patients. A Canny edge detector was used to extract two edge images from a reference x-ray image and a test x-ray image of a patient before positioning. While translating and rotating the edged test image, the absolute value of the normalized binary image correlation between the two edge images is iteratively maximized. Each time before rotation, dilation is applied to the edged test image to avoid a steep reduction of the image correlation. To evaluate robustness of the proposed method, a simulation has been carried out using 240 simulated edged head front-view images extracted from a reference image by varying parameters of the Canny algorithm with a given range of rotation angles and translation amounts in x and y directions. It was shown that resulting registration errors have an accuracy of one pixel in x and y directions and zero degrees in rotation, even when the number of edge pixels significantly differs between the edged reference image and the edged simulation image. Subsequently, positioning experiments using several sets of head, lung, and hip data have been performed. We have observed that the differences of translation and rotation between manual positioning and the proposed method were within one pixel in translation and one degree in rotation. From the results of the validation study, it can be concluded that a significant reduction in workload for the physicians and technicians can be achieved with this method.     

Fast and Accurate Registration Techniques for Affine and Nonrigid Alignment of MR Brain Images

Registration of magnetic resonance brain images is a geometric operation that determines point-wise correspondences between two brains. It remains a difficult task due to the highly convoluted structure of the brain. This paper presents novel methods, Brain Image Registration Tools (BIRT), that can rapidly and accurately register brain images by utilizing the brain structure information estimated from image derivatives. Source and target image spaces are related by affine transformation and non-rigid deformation. The deformation field is modeled by a set of Wendland’s radial basis functions hierarchically deployed near the salient brain structures. In general, nonlinear optimization is heavily engaged in the parameter estimation for affine/non-rigid transformation and good initial estimates are thus essential to registration performance. In this work, the affine registration is initialized by a rigid transformation, which can robustly estimate the orientation and position differences of brain images. The parameters of the affine/non-rigid transformation are then hierarchically estimated in a coarse-to-fine manner by maximizing an image similarity measure, the correlation ratio, between the involved images. T1-weighted brain magnetic resonance images were utilized for performance evaluation. Our experimental results using four 3-D image sets demonstrated that BIRT can efficiently align images with high accuracy compared to several other algorithms, and thus is adequate to the applications which apply registration process intensively. Moreover, a voxel-based morphometric study quantitatively indicated that accurate registration can improve both the sensitivity and specificity of the statistical inference results.     

磁共振成像PROPELLER采样数据重建中的运动估计新算法

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。     

A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data

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.     

不同配准方式下鼻咽癌千伏级CBCT图像引导放疗的摆位误差

目的:比较鼻咽癌千伏级锥形束CT（CBCT）不同配准方式得出的配准结果,为摆位修正提供参考。方法:回顾性分析2018年5月~2019年8月在广州医科大学附属肿瘤医院进行治疗的100例鼻咽癌患者的CBCT图像。按照大区域（Large）即全扫描范围、小区域（Small）即计划靶区范围、骨性（Bone）、灰度（Grey）的不同组合方式,对千伏级CBCT图像分别采用大区域骨性（LB）、大区域灰度（LG）、小区域骨性（SB）、小区域灰度（SG）这4种方式进行配准,并对平移误差和旋转误差结果进行分析。结果:4种方式等中心点平移误差均值范围为-0.29~1.07 mm,旋转误差均值范围为-0.10°~0.61°。除LB与SB在Y方向的平移误差存在显著性差异（P=0.00）之外,其余平移和旋转误差均无显著性差异（P=0.05~0.82）。LG与SG的所有平移和旋转误差均无显著性差异（P=0.14~0.64）。而LG与LB相比除Y方向的平移和旋转误差无显著性差异（P=0.67, 0.57）外,其余所有误差均有显著性差异（P=0.00~0.02）。SB与SG相比除X方向平移误差和Y方向旋转误差无显著性差异（P=0.36, 0.72）外,其余所有误差均有显著性差异（0.00~0.02）。Pearson相关性分析表明三维方向上所有平移和旋转摆位误差结果均呈正相关（R=0.48~0.98, P<0.01）。采用LG和SG进行配准得出的摆位误差在X、Y、Z平移和旋转方向上的95％一致性限度分别为［1.19, -1.25］、［0.95, -1.01］、［1.13, -1.31］和［0.80, -0.85］、［0.69, -0.60］、［1.02, -1.13］。按照2 mm和2°界值标准二者摆位误差具有一致性。结论:在鼻咽癌的图像引导放疗中用千伏级CBCT进行自动模式下的图像配准时,三维方向上的配准结果可能因骨性或灰度配准方式的不同而有差别。配准区域选择靶区或全扫描范围时,配准结果差异不明显。     

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。     

A robust method of multileaf collimator (MLC) leaf-configuration verification

Presents a novel and robust method for leaf-position verification with a multileaf collimator (MLC). On the portal image associated with an MLC-generated treatment field, all true treatment-held-edge lines are either parallel or perpendicular to each other. This unique feature of an MLC treatment field has been fully exploited by the authors' method. Employing a Hough-type transformation as an edge-line-orientation detector and a chamfer-matching method, the authors can find the best matching parameters (including translation, rotation and scaling) adaptively between a prescribed MLC leaf configuration and the actual treatment-held edges generated by the MLC system. This works even if the portal image is partially corrupted by noise or covered by compact bony structures. Comparing these parameters with clinically accepted tolerances, the authors can make a "go-or-no-go" decision quickly.     

A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery

The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position. A multiphase framework is designed to register the digitally reconstructed radiographs with the x-ray images periodically acquired during patient setup and treatment. The registration in each projection is performed independently; the results in the two projections are then combined and converted to a 3D rigid transformation by 2D-3D geometric backprojection. The in-plane transformation and the out-of-plane rotation are estimated using different search methods, including multiresolution matching, steepest descent minimization, and one-dimensional search. Two similarity measures, optimized pattern intensity and sum of squared difference, are applied at different registration phases to optimize accuracy and computation speed. Various experiments on an anthropomorphic head-and-neck phantom showed that, using fiducial registration as a gold standard, the registration errors were 0.33 +/- 0.16 mm (s.d.) in overall translation and 0.29 degrees +/- 0.11 degrees (s.d.) in overall rotation. The total targeting errors were 0.34 +/- 0.16 mm (s.d.), 0.40 +/- 0.2 mm (s.d.), and 0.51 +/- 0.26 mm (s.d.) for the targets at the distances of 2, 6, and 10 cm from the rotation center, respectively. The computation time was less than 3 s on a computer with an Intel Pentium 3.0 GHz dual processor.     

The effect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI

A dynamic-contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset consists of many imaging frames, often acquired both before and after contrast injection. Due to the length of time spent acquiring images, patient motion is likely and image re-alignment or registration is required before further analysis such as pharmacokinetic model fitting. Non-rigid image registration procedures may be used to correct motion artefacts; however, a careful choice of registration strategy is required to reduce misregistration artefacts associated with enhancing features. This work investigates the effect of registration on the results of model-fitting algorithms for 52 DCE-MR mammography cases for 14 patients. Results are divided into two sections: a comparison of registration strategies in which a DCE-MRI-specific algorithm is preferred in 50% of cases, followed by an investigation of parameter changes with known applied deformations, inspecting the effect of magnitude and timing of motion artefacts. Increased motion magnitude correlates with increased model-fit residual and is seen to have a strong influence on the visibility of strongly enhancing features. Motion artefacts in images close to the contrast agent arrival have a disproportionate effect on discrepancies in parameter estimation. The choice of algorithm, magnitude of motion and timing of the motion are each shown to influence estimated pharmacokinetic parameters even when motion magnitude is small.     

Comparisons of surface vs. volumetric model-based registration methods using single-plane vs. bi-plane fluoroscopy in measuring spinal kinematics

Several 2D-to-3D image registration methods are available for measuring 3D vertebral motion but their performance has not been evaluated under the same experimental protocol. In this study, four major types of fluoroscopy-to-CT registration methods, with different use of surface vs. volumetric models, and single-plane vs. bi-plane fluoroscopy, were evaluated: STS (surface, single-plane), VTS (volumetric, single-plane), STB (surface, bi-plane) and VTB (volumetric, bi-plane). Two similarity measures were used: ‘Contour Difference’ for STS and STB and ‘Weighted Edge-Matching Score’ for VTS and VTB. Two cadaveric porcine cervical spines positioned in a box filled with paraffin and embedded with four radiopaque markers were CT scanned to obtain vertebral models and marker coordinates, and imaged at ten static positions using bi-plane fluoroscopy for subsequent registrations using different methods. The registered vertebral poses were compared to the gold standard poses defined by the marker positions determined using CT and Roentgen stereophotogrammetry analysis. The VTB was found to have the highest precision (translation: 0.4 mm; rotation: 0.3°), comparable with the VTS in rotations (0.3°), and the STB in translations (0.6 mm). The STS had the lowest precision (translation: 4.1 mm; rotation: 2.1°).     

A new approach to elastography using mutual information and finite elements

Historically, increased mechanical stiffness during tissue palpation exams has been associated with assessing organ health as well as with detecting the growth of a potentially life-threatening cell mass. As such, techniques to image elasticity parameters (i.e., elastography) have recently become of great interest to scientists. In this work, a new method of elastography will be introduced within the context of mammographic imaging. The elastography method proposed represents a non-rigid iterative image registration algorithm that varies material properties within a finite element model to improve registration. More specifically, regional measures of image similarity are used within an objective function minimization framework to reconstruct elasticity images of tissue stiffness. Numerical simulations illustrate: (1) the encoding of stiffness information within the context of a regional image similarity criterion, (2) the methodology for an iterative elastographic imaging framework and (3) elasticity reconstruction simulations. The real strength in this approach is that images from any modality (e.g., magnetic resonance, computed tomography, ultrasound. etc) that have sufficient anatomically-based intensity heterogeneity and remain consistent from a pre- to a post-deformed state could be used in this paradigm.     

Effects of x-ray and CT image enhancements on the robustness and accuracy of a rigid 3D/2D image registration

A rigid body three-dimensional/two-dimensional (3D/2D) registration method has been implemented using mutual information, gradient ascent, and 3D texturemap-based digitally reconstructed radiographs. Nine combinations of commonly used x-ray and computed tomography (CT) image enhancement methods, including window leveling, histogram equalization, and adaptive histogram equalization, were examined to assess their effects on accuracy and robustness of the registration method. From a set of experiments using an anthropomorphic chest phantom, we were able to draw several conclusions. First, the CT and x-ray preprocessing combination with the widest attraction range was the one that linearly stretched the histograms onto the entire display range on both CT and x-ray images. The average attraction ranges of this combination were 71.3 mm and 61.3 deg in the translation and rotation dimensions, respectively, and the average errors were 0.12 deg and 0.47 mm. Second, the combination of the CT image with tissue and bone information and the x-ray images with adaptive histogram equalization also showed subvoxel accuracy, especially the best in the translation dimensions. However, its attraction ranges were the smallest among the examined combinations (on average 36 mm and 19 deg). Last the bone-only information on the CT image did not show convergency property to the correct registration.     

A novel method for automatic detection of patient out-of-plane rotation by comparing a single portal image to a reference image

A novel method for detecting out-of-plane patient rotation by comparing a single portal image to its reference image is presented. Out-of-plane rotation results in an apparent distortion of the anatomy in a portal image. This distortion can be mathematically predicted with the magnification varying at each point in the image. While scaling of points at equal depth is invariant under in-plane rotation or translation, and changes equally in both dimensions for an axial shift of the patient, a change of scaling in only one dimension can be ascribed to an out-of-plane rotation. For the two conditions that are used in this study, it is shown that out-of-plane rotation yields a different scaling of the image in two perpendicular directions and therefore it is feasible to calculate the scale factors as a function of out-of-plane rotation. Conversely the recovery of scale factors in two different directions at the same time would enable the magnitude of the out-of-plane rotation to be recovered. The properties of the Fourier transform of the image are used to align the portal image with the reference image (a simulator image or first approved portal image) prior to the recovery of the scale factors. Correlating the Fourier transform of the portal image on a log-scale with that of the reference image enables the scale factors to be automatically extracted from a single portal image. In the two approaches investigated, out-of-plane rotations of up to 41 degrees and 20 degrees (respectively) have been recovered with a maximum error of 2.4 degrees. This technique could be used to automatically detect patient roll or tilt prior to or during a treatment session.     

Comparative evaluation of similarity measures for the rigid registration of multi-modal head images

Image registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity measure reaches an optimum. The numerous similarity measures that have been proposed in the past are differently sensitive to imaging modality, image content and differences in the image content, selection of the floating and target image, partial image overlap, etc. In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known 'gold standard' registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact of exchanging the floating and target image indicate that, especially in MR/PET registrations, the behavior of some similarity measures, such as mutual information, significantly depends on which image is the floating and which is the target.