External landmark and head-shape-based functional data normalization

Functional data, as obtained from magnetoencephalography (MEG) techniques, is frequently transformed to a template brain space for pooling data across the population. This transformation is often performed via an intermediate magnetic resonance image (MRI) of the subject’s brain that is first registered to the template brain MRI. However, in many instances, it is difficult, expensive or undesirable to acquire MRIs, and since reconstructed functional data is lower resolution, the full information in MRIs is not required for registration. We present here two alternative options for computing the transformation of functional data to a common template space. These alternatives compute the registration based on external landmarks placed on the head, and the external shape of the head, features that are considerably simpler and inexpensive to acquire than MRIs. We present quantification of the accuracy of using these alternative features for registration, and show that they give accuracy for functional data registration that is sufficient given the functional data resolution, and is comparable to existing methods that are commonly used.     

乳腺癌保乳术后锥形束CT引导全乳精确放疗的最佳配准模板选择

目的 筛选乳腺癌保乳术后锥形束CT(CBCT)图像引导全乳精确放疗的最佳配准模板。 方法 2006年4月到2009年7月在我院行乳腺癌保乳术后全乳精确放疗的12例患者入组。应用Varian 23EX直线加速器机载锥形束CT获取CBCT图像。在计划CT图像的乳腺术腔及周围腺体标注6个易辨认的点(直径为1 mm)作为参考。分别以乳腺轮廓、手术银夹、乳腺腺体、乳腺相邻肋骨以及乳腺同侧肺外轮廓为模板进行手动配准并记录配准时间。配准后立即在CBCT图像上对应标注上述6个参考点,并测量计划CT与CBCT图像上所标注对应参考点之间的距离,计算每位患者每种配准模板下的配准误差。应用SPSS 13.0统计软件对配准误差进行单项方差分析。结果 以乳腺轮廓、手术银夹、乳腺腺体、乳腺相邻肋骨以及乳腺同侧肺外轮廓为模板,配准误差分别为(0.60 ± 0.20)、 (0.43 ± 0.15)、 (0.49 ± 0.19)、(0.69 ± 0.36)和(0.94 ± 0.49)cm, 配准所需要的时间分别为(3.8 ± 1.1)、 (3.0 ± 0.9)、 (4.7 ± 1.7)、 (4.3 ± 1.3)和(4.5 ± 1.3) min。单因素方差分析显示,乳腺轮廓、乳腺腺体、手术银夹3种配准模板的配准误差之间差异无统计学意义(t = 0.48~1.36,P＞0.05),乳腺相邻肋骨与乳腺同侧肺外轮廓两种配准模板之间差异也无统计学意义,手术银夹与后两者之间差异有统计学意义(t = 2.08~4.08,P＜0.05)。结论 以乳腺相邻肋骨与乳腺同侧肺外轮廓两种模板配准准确性差,应综合考虑患者的个体情况以手术银夹、乳腺腺体或乳腺轮廓为模板进行配准。若同时考虑时间及准确性,以手术银夹配准最佳。     

Registration accuracy of<Superscript> 153</Superscript>Gd transmission images of the brain

Purpose The aim of the study was to determine the accuracy of non-rigid nine-parameter image registrations based on ¹⁵³Gd transmission computed tomography (TCT) images as compared with those based on ^99mTc-ethyl cysteinate dimer (ECD) images and to assess whether normalised mutual information (NMI) or count difference (CD) should be used.Methods TCT and ECD data were acquired in 25 randomly selected patients. Emission images were registered to an ECD template with a CD cost function. The same registration parameters were applied to the transmission images to create a TCT template. All TCT images were registered to the TCT template and the same registration parameters were applied to the ECD images. The procedure was repeated with NMI as cost function. Accuracy of both ECD-based and TCT-based registrations was assessed by comparing the normalisation parameter values and regional activities in the spatially normalised ECD images, using a mixed-model analysis of variance (ANOVA). Scheffé post hoc tests were performed.Results No significant differences were found between ECD/CD, ECD/NMI and TCT/CD, suggesting that ECD registration can be done with either CD or NMI, and that TCT registration using CD is equally as accurate as ECD registration. The accuracy of TCT registration with NMI was lower, with discrepancies occurring in the frontal inferior region and the cerebellum. The analysis of normalisation parameters indicated that z-scaling is underestimated and yz-rotation overestimated with TCT/NMI registration.Conclusion We conclude that ECD registrations with CD or NMI are as accurate as TCT registrations with CD and that TCT registrations with NMI should be avoided.     

Three brain SPECT region-of-interest templates in elderly people: normative values, hemispheric asymmetries, and a comparison of single- and multihead cameras.

The purpose of this study was to generate anatomically guided region-of-interest (ROI) brain SPECT templates based on scans of elderly healthy volunteers. We describe normal tracer uptake and hemispheric asymmetries for each of 3 camera systems and compare these characteristics among systems. METHODS: 99mTc-hexamethyl propyleneamine oxime SPECT scans were acquired from 28 elderly healthy volunteers (mean age [+/-SD], 70.3 +/- 6.5 y) on a single-head rotating gamma camera (n = 15) or on dual- (n = 18) or triple-head (n = 13) cameras. The average number of counts in each ROI was calculated and referenced to counts in a cerebellar ROI, providing semiquantitative regional cerebral blood flow (rCBF) ratios. For the templates and ROI map, base images of a healthy volunteer were obtained with each camera. Data from individuals scanned with 2 cameras on the same day (n = 15) were used to evaluate rCBF differences across cameras. For each camera, averaged SPECT templates were made using automated image registration. The base volunteer's T1-weighted MR image was converted to stereotactic space with dimensions similar to those of the SPECT templates, and 79 bilateral ROIs were defined. To obtain ROI rCBF ratios, we aligned individual images to their appropriate template and then to this modified MR image. RESULTS: The ROI coefficients of variation indicated that the fit of the ROIs was acceptable (0.07-0.35). Mean rCBF ratios ranged from 0.57 to 1.0, 0.67 to 1.01, and 0.63 to 1.00 for single-, dual-, and triple-head cameras, respectively. The cuneus, occipital cortex, occipital pole, middle temporal gyrus, and posterior middle frontal gyrus showed consistent hemispheric asymmetry (right side greater than left side in 83%-100% of individuals). Mean rCBF ratios did not differ between dual- and triple-head cameras, whereas the ratios for single- and dual-head cameras differed significantly (39 ROIs differed), even after smoothing and filtering the dual-head images to the level of the single-head images. CONCLUSION: The use of SPECT templates based on elderly healthy volunteers is an important feature of this technique because most available templates have used young individuals. Another important feature is the use of MR image-based ROIs. These procedures are versatile because they use more than 1 camera. They can easily be implemented in clinical and research settings to detect camera-specific, abnormal deviations in rCBF ROI ratios and asymmetry magnitudes in diseases associated with aging, such as stroke and dementia.     

Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images

RATIONALE AND OBJECTIVES: To establish the range of normal values for quantitative CT-based measures of lung structure and function, the authors developed a method for matching pulmonary structures across individuals and creating a normative human lung atlas. MATERIALS AND METHODS: A computerized human lung atlas was synthesized from computed tomographic (CT) images from six subjects by means of three-dimensional image registration. The authors identified a set of reproducible feature points for each CT image and used these points to establish correspondences across subjects, used a landmark- and intensity-based consistent image registration algorithm to register a template image volume from the population to the rest of the pulmonary CT volumes in the population, averaged these transformations, and constructed an atlas by deforming the template with the average transformation. RESULTS: The effectiveness of the authors' method was evaluated and visualized by means of both gray-level and segmented CT images. The method reduced the average landmark registration error from 10.5 mm to 0.4 mm and the average relative volume overlap error from 0.7 to 0.11 for the six data sets studied. CONCLUSION: The method, and the computerized human lung atlas constructed and visualized by the authors with this method, provides a basis for establishing regional ranges of normative values for structural and functional measures of the human lung.     

Characterization and registration of 3D ultrasound for use in permanent breast seed implant brachytherapy treatment planning

PurposePermanent breast seed implant (PBSI) brachytherapy is a novel technique for early-stage breast cancer. Computed tomography (CT) images are used for treatment planning and freehand 2D ultrasound for implant guidance. The multimodality imaging approach leads to discrepancies in target identification. To address this, a prototype 3D ultrasound (3DUS) system was recently developed for PBSI. In this study, we characterize the 3DUS system performance, establish QA baselines, and develop and test a method to register 3DUS images to CT images for PBSI planning.Methods and Materials3DUS system performance was characterized by testing distance and volume measurement accuracy, and needle template alignment accuracy. 3DUS-CT registration was achieved through point-based registration using a 3D-printed model designed and constructed to provide visible landmarks on both images and tested on an in-house made gel breast phantom.ResultsThe 3DUS system mean distance measurement accuracy was within 1% in axial, lateral, and elevational directions. A volumetric error of 3% was observed. The mean needle template alignment error was 1.0° ± 0.3 ° and 1.3 ± 0.5 mm. The mean 3DUS-CT registration error was within 3 mm when imaging at the breast centre or across all breast quadrants.ConclusionsThis study provided baseline data to characterize the performance of a prototype 3DUS system for PBSI planning and developed and tested a method to obtain accurate 3DUS-CT image registration for PBSI planning. Future work will focus on system validation and characterization in a clinical context as well as the assessment of impact on treatment plans.     

Development of a computerized method for identifying view position and orientation for chest radiographs by using a template matching technique

For implementation of computer-aided diagnostic systems for chest radiographs in the clinical environment, it is necessary to correctly identify two view positions [posteroanterior (PA) and lateral views] and four orientations (upward, downward, leftward, and rightward) for chest radiographs. In picture archiving and communication system (PACS), the information on the view position and orientation for chest radiographs is often not recorded or is recorded incorrectly. The purpose of this study was to develop a computerized method for correctly identifying view position and orientation for chest radiographs by using a template matching technique. Our basic approach is to find the most similar template to an unknown chest image by examining the similarity of the unknown chest image with a number of templates for PA and lateral views at the four image orientations, and to determine simultaneously the view position and orientation for the unknown chest image. All upward templates were produced from the PA or lateral images for upward orientation for various patient sizes. By rotating the upward templates, we produced the templates for downward, leftward, and rightward orientations. To evaluate the similarity of a test image with all templates, the cross-correlation values of the test image with all templates were obtained. The view position and image orientation for the unknown image was considered to be identical to those for the template with which the largest cross-correlation value was obtained. Our results indicated that all cases of 200 PA and 200 lateral views were correctly identified in terms of the view position and image orientation.     

Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI.

Patient motion and image distortion induced by eddy currents cause artifacts in maps of diffusion parameters computed from diffusion-weighted (DW) images. A novel and comprehensive approach to correct for spatial misalignment of DW imaging (DWI) volumes acquired with different strengths and orientations of the diffusion sensitizing gradients is presented. This approach uses a mutual information-based registration technique and a spatial transformation model containing parameters that correct for eddy current-induced image distortion and rigid body motion in three dimensions. All parameters are optimized simultaneously for an accurate and fast solution to the registration problem. The images can also be registered to a normalized template with a single interpolation step without additional computational cost. Following registration, the signal amplitude of each DWI volume is corrected to account for size variations of the object produced by the distortion correction, and the b-matrices are properly recalculated to account for any rotation applied during registration. Both qualitative and quantitative results show that this approach produces a significant improvement of diffusion tensor imaging (DTI) data acquired in the human brain.     

Automated analysis of small animal PET studies through deformable registration to an atlas

Purpose

This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.

Methods

A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.

Results

The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6?mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10?% in most of the organs considered.

Conclusion

The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.     

Development of computerized method for automated classification of the body parts in digital radiographs

PURPOSE: In picture archiving and communication systems (PACS), the information on the body parts included in radiographs is often not or incorrectly recorded in an image header. In order to apply the computer-aided diagnosis (CAD) system in the PACS environment, the body parts in radiographs need to be recognized correctly by computer. The purpose of this study is to develop a computerized method for correctly classifying the body parts in digital radiographs based on a template matching technique.METHODS/MATERIALS: The image database used in this study was 1032 digital radiographs (14 x 17 inches) obtained with a computed radiography, and included 505 chest of postetroanterior view, 39 chest of lateral view, 241 abdomen, 108 pelvis, 10 upper limbs, 125 lower limbs, and 4 thoracic spine. In this method, test images were classified into four body parts, i.e., (1) chest, (2) abdomen, (3) pelvis, and (4) upper/lower limbs and thoracic spine. This computerized method was tested with 852 images, since 180 images were employed for creation of 98 templates, which represented the average radiographs for various body parts. Our approach was to examine the similarity of a given test image with templates by use of the cross-correlation values as the similarity measures. The body part of the test image was identified as the body part in the template yielding the maximum correlation value. Our method consisted of the following five steps. First, test images were classified into one of three groups; i.e. 1) chest and abdomen, 2) pelvis, and 3) upper/lower limbs and thoracic spine by using the templates obtained from images with the average size and position. Second, the remaining uncertain images were classified by using additional templates in various directions. Third, the chest and abdomen group was separated into two subgroups; i.e.chest and abdomen. Fourth, in order to classify some uncertain images, templates were shifted horizontally and vertically. Fifth, outer pixels of templates were eliminated to avoid the misclassification due to x-ray collimation. RESULTS: Our preliminary results indicated that the body parts for 850 cases (99.8%) were correctly classified with our method. CONCLUSIONS: This method would be useful for automated identification of the body parts in radiographs when various CAD systems would be implemented in the PACS environment.     

Role of standardized and study‐specific human brain diffusion tensor templates in inter‐subject spatial normalization

Purpose:

To investigate the effect of standardized and study‐specific human brain diffusion tensor templates on the accuracy of spatial normalization, without ignoring the important roles of data quality and registration algorithm effectiveness.

Materials and Methods:

Two groups of diffusion tensor imaging (DTI) datasets, with and without visible artifacts, were normalized to two standardized diffusion tensor templates (IIT2, ICBM81) as well as study‐specific templates, using three registration approaches. The accuracy of inter‐subject spatial normalization was compared across templates, using the most effective registration technique for each template and group of data.

Results:

It was demonstrated that, for DTI data with visible artifacts, the study‐specific template resulted in significantly higher spatial normalization accuracy than standardized templates. However, for data without visible artifacts, the study‐specific template and the standardized template of higher quality (IIT2) resulted in similar normalization accuracy.

Conclusion:

For DTI data with visible artifacts, a carefully constructed study‐specific template may achieve higher normalization accuracy than that of standardized templates. However, as DTI data quality improves, a high‐quality standardized template may be more advantageous than a study‐specific template, because in addition to high normalization accuracy, it provides a standard reference across studies, as well as automated localization/segmentation when accompanied by anatomical labels. J. Magn. Reson. Imaging 2013;37:372–381. © 2012 Wiley Periodicals, Inc.     

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     

Mammographic Mass Detection Using a Mass Template

Objective

The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using templates.

Materials and Methods

Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass template was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass template to determine whether there was a shape (part of a ROI) similar to the mass in the template. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass. To test the system''s efficiency, we applied this process to 52 mammogram images from the Mammographic Image Analysis Society (MIAS) database.

Results

Three hundred and thirty-two ROI were identified using the ROI specification methods. These ROI were classified using three templates whose diameters were 10, 20 and 30 pixels. The results of this experiment showed that using the templates with these diameters achieved sensitivities of 93%, 90% and 81% with 1.3, 0.7 and 0.33 false positives per image respectively.

Conclusion

These results indicate that the detection performance of this template based algorithm is satisfactory, and may improve the performance of computer-aided analysis of mammographic images and early diagnosis of mammographic masses.     

Three-dimensional reconstruction and fusion for multi-modality spinal images.

Medical diagnosis can benefit from the complementary information in different modality images. Multi-modal image registration and fusion is an essential task in numerous three-dimensional (3D) medical image-processing applications. Registered images are not only providing more correlative information to aid in diagnosis, but also assisting with the planning and monitoring of both surgery and radiotherapy. This research is directed at registering different images captured from Computed Tomography (CT) and Magnetic Resonance (MR) imaging devices, respectively, to acquire more thorough information for disease diagnosis. Because MR bone model segmentation is difficult, this research used a 3D model obtained from CT images. This model accomplishes image registration by optimizing the gradient information accumulated around the bony boundary areas with respect to the 3D model. This system involves pre-processing, 2D segmentation, 3D registration, fusion and sub-system rendering. This method provides desired image operation, robustness verification, and multi-modality spinal image registration accuracy. The proposed system is useful in observing the foramen and nerve root. Because the registration can be performed without external markers, a better choice for clinical usage is provided for lumbar spine diagnosis.     

Joint registration and segmentation of neuroanatomic structures from brain MRI

RATIONALE AND OBJECTIVES: Segmentation of anatomic structures from magnetic resonance brain scans can be a daunting task because of large inhomogeneities in image intensities across an image and possible lack of precisely defined shape boundaries for certain anatomical structures. One approach that has been quite popular in the recent past for these situations is the atlas-based segmentation. The atlas, once constructed, can be used as a template and can be registered nonrigidly to the image being segmented thereby achieving the desired segmentation. The goal of our study is to segment these structures with a registration assisted image segmentation technique. MATERIALS AND METHODS: We present a novel variational formulation of the registration assisted image segmentation problem which leads to solving a coupled set of nonlinear Partial Differential Equations (PDEs) that are solved using efficient numeric schemes. Our work is a departure from earlier methods in that we can simultaneously register and segment in three dimensions and easily cope with situations where the source (atlas) and target images have very distinct intensity distributions. RESULTS: We present several examples (20) on synthetic and (3) real data sets along with quantitative accuracy estimates of the registration in the synthetic data case. CONCLUSION: The proposed atlas-based segmentation technique is capable of simultaneously achieve the nonrigid registration and the segmentation; unlike previous methods of solution for this problem, our algorithm can accommodate for image pairs having very distinct intensity distributions.     

一种通过脑电信号实时检测双眼竞争的方法

目的 为实时准确地通过脑电信号分析双眼竞争中的意识交替感知,提供新的客观检测途径.方法 实验中,被试者的双眼分别给予闪烁频率略有不同的两幅图片刺激,产生双眼竞争现象,即被试者意识到的是两幅图片的交替转换;同时,被试者的脑电中也会产生和闪烁频率相同的稳态视觉诱发电位成份,该电位随感知交替变化.利用记录的多通道脑电信号,首先用一小段已知转换点的信号,训练两个能够捕捉切换点附近信号特性的检测模板,再用模板在整段信号上滑动,进行典型相关分析,给出其它未知的切换点,由此达到客观检测双眼竞争中主观意识的目的.结果 在仿真数据和真实实验数据的分析中,用本文提出方法得到的客观检测结果,和主观报告的匹配程度都好于用已有的谱分析方法.结论 这种基于模板检测方法,相比已有的谱分析方法具有更好的抗噪性能,并能够充分利用谐波的信息.在客观检测双眼竞争中具有实用价值.     

Segmentation of cell nuclei in heterogeneous microscopy images: A reshapable templates approach

Histological tissue images typically exhibit very sophisticated spatial color patterns. It is of great clinical importance to extract qualitative and quantitative information from these images. As an ad hoc solution, various unsupervised approaches address the object detection and segmentation problem which are suitable for limited classes of histology images. In this paper, we propose a general purpose localization and segmentation method which utilizes reshapable templates. The method combines both pixel- and object-level features for detecting regions of interest. Segmentation is carried out in two levels including both the coarse and fine ones. A set of simple-shaped templates is used for coarse segmentation. A content based template reshaping algorithm is proposed for fine segmentation of target objects. Experimentation was done using a publicly available image data set which contains 7931 manually labeled cells of heterogeneous histology images. The experiments have demonstrated acceptable level of detection and segmentation results for the proposed approach (precision = 0.904, recall = 0.870 and Zijdenbos similarity index = 73%). Thus, the prototype software developed based on proposed method can be considered as a potential tool for pathologists in clinical process.     

A convolutional neural network Cascade for plantar pressure images registration

BackgroundPlantar pressure image (PPI) recorded in high spatial and temporal resolution is very useful in clinical gait analysis. For functional analysis of PPI, image registration is often performed to maximally correlate source image with a template image. Previous methods estimate the registration parameters by iteratively optimizing different objective functions. These methods are often computational expensive to achieve satisfactory registration accuracy.Research questionCan we develop a single PPI registration technique that performs more rapidly than previous methods, and that also maintains adequate PPI correspondence as defined by various (dis)similarity metrics?MethodsA cascaded convolutional neural network (CNN) was proposed for the registration of PPIs. Our model was trained to learn a regression from the difference between the template and misaligned images to the registration parameters. The registration performance was evaluated by three different metrics, i.e. the mean squared error (MSE), the exclusive or (XOR), and the mutual information (MI). For comparison, four previous methods were also implemented. These included the principal axes (PA) method, the center of pressure trajectory (COP) method, the MSE method, and the XOR method.ResultsExperimental results on a dataset with 71 PPI template-source pairs showed that the proposed CNN-based method could obtain comparable registration accuracy to the MSE and XOR method. With regards to the registration speed, registration durations (mean ± sd in seconds) per image pair were: MSE (30.584 ± 2.171), XOR (24.245 ± 1.596), PA (0.016 ± 0.003), COP (25.614 ± 0.341), and the proposed model (0.054 ± 0.007).SignificanceOur findings indicate that the proposed registration approach can achieve high accuracy but less computational time. Thus, it is more practical to utilize our pre-trained CNN-based model to develop near-real time applications for plantar pressure images registration.     

Virtual multi-fracture craniofacial reconstruction using computer vision and graph matching

The problem of computer vision-guided reconstruction of a fractured human mandible from a computed tomography (CT) image sequence exhibiting multiple broken fragments is addressed. The problem resembles 3D jigsaw puzzle assembly and hence is of general interest for a variety of applications dealing with automated reconstruction or assembly. The specific problem of automated multi-fracture craniofacial reconstruction is particularly challenging since the identification of opposable fracture surfaces followed by their pairwise registration needs to be performed expeditiously in order to minimize the operative trauma to the patient and also limit the operating costs. A polynomial time solution using graph matching is proposed. In the first phase of the proposed solution, the opposable fracture surfaces are identified using the Maximum Weight Graph Matching algorithm. The pairs of opposable fracture surfaces, identified in the first stage, are registered in the second phase using the Iterative Closest Point (ICP) algorithm. Correspondence for a given pair of fracture surfaces, needed for the Closest Set computation in the ICP algorithm, is established using the Maximum Cardinality Minimum Weight bipartite graph matching algorithm. The correctness of the reconstruction is constantly monitored by using constraints derived from a volumetric matching procedure guided by the computation of the Tanimoto Coefficient.     

基于“3面9点”配准方案的PET+MRI异机二维图像融合尝试

目的:尝试一种基于体表定位的二维图像配准方法,实现PET和MRI异机图像的精确融合。方法:输入PET/MRI原始数据后采用数字化格式转换,设计"3面9点"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:以肺癌患者的胸部和髋部为实例交叉试验PET+MRI二维图像的异机融合,生成同时呈现胸髋解剖结构和代谢状况的互补影像。结论:在同机设备成本昂贵、不易普及的条件下,这种异机融合无疑是现有同机成像的必要补充。