A stereotactic method for the three-dimensional registration of multi-modality biologic images in animals: NMR, PET, histology, and autoradiography

The objective of this work was to develop and then validate a stereotactic fiduciary marker system for tumor xenografts in rodents which could be used to co-register magnetic resonance imaging (MRI), PET, tissue histology, autoradiography, and measurements from physiologic probes. A Teflon fiduciary template has been designed which allows the precise insertion of small hollow Teflon rods (0.71 mm diameter) into a tumor. These rods can be visualized by MRI and PET as well as by histology and autoradiography on tissue sections. The methodology has been applied and tested on a rigid phantom, on tissue phantom material, and finally on tumor bearing mice. Image registration has been performed between the MRI and PET images for the rigid Teflon phantom and among MRI, digitized microscopy images of tissue histology, and autoradiograms for both tissue phantom and tumor-bearing mice. A registration accuracy, expressed as the average Euclidean distance between the centers of three fiduciary markers among the registered image sets, of 0.2 +/- 0.06 mm was achieved between MRI and microPET image sets of a rigid Teflon phantom. The fiduciary template allows digitized tissue sections to be co-registered with three-dimensional MRI images with an average accuracy of 0.21 and 0.25 mm for the tissue phantoms and tumor xenografts, respectively. Between histology and autoradiograms, it was 0.19 and 0.21 mm for tissue phantoms and tumor xenografts, respectively. The fiduciary marker system provides a coordinate system with which to correlate information from multiple image types, on a voxel-by-voxel basis, with sub-millimeter accuracy--even among imaging modalities with widely disparate spatial resolution and in the absence of identifiable anatomic landmarks.     

DRR and Portal Image Registration for Automatic Patient Positioning in Radiotherapy Treatment

Image processing turns out to be essential in the planning and verification of radiotherapy treatments. Before applying a radiotherapy treatment, a dosimetry planning must be performed. Usually, the planning is done by means of an X-ray volumetric analysis using computerized tomography, where the area to be radiated is marked out. During the treatment phase, it is necessary to place the patient under the particle accelerator exactly as considered in the dosimetry stage. Coarse alignment is achieved using fiduciary markers placed over the patient’s skin as external references. Later, fine alignment is provided by comparing a digitally reconstructed radiography (DRR) from the planning stage and a portal image captured by the accelerator in the treatment stage. The preprocessing of DRR and portal images, as well as the minimization of the non-shared information between both kinds of images, is mandatory for the correct operation of the image registration algorithm. With this purpose, mathematical morphology and image processing techniques have been used. The present work describes a fully automatic method to calculate more accurately the necessary displacement of the couch to place the patient exactly at the planned position. The proposed method to achieve the correct positioning of the patient is based on advanced image registration techniques. Preliminary results show a perfect match with the displacement estimated by the physician.     

Three-dimensional image registration of MR proximal femur images for the analysis of trabecular bone parameters

This study investigated the feasibility of automatic image registration of MR high-spatial resolution proximal femur trabecular bone images as well as the effects of gray-level interpolation and volume of interest (VOI) misalignment on MR-derived trabecular bone structure parameters. For six subjects in a short-term study, a baseline scan and a follow-up scan of the proximal femur were acquired on the same day. For ten subjects in a long-term study, a follow-up scan of the proximal femur was acquired 1 year after the baseline. An automatic image registration technique, based on mutual information, utilized a baseline and a follow-up scan to compute transform parameters that aligned the two images. In the short-term study, these parameters were subsequently used to transform the follow-up image with three different gray-level interpolators. Nearest-neighbor interpolation and B-spline approximation did not significantly alter bone parameters, while linear interpolation significantly modified bone parameters (p<0.01). Improvement in image alignment due to the automatic registration for the long-term and short-term study was determined by inspecting difference images and 3D renderings. This work demonstrates the first application of automatic registration, without prior segmentation, of high-spatial resolution trabecular bone MR images of the proximal femur. Additionally, inherent heterogeneity in trabecular bone structure and imprecise positioning of the VOI along the slice (anterior-posterior) direction resulted in significant changes in bone parameters (p<0.01). Results suggest that automatic mutual information registration using B-spline approximation or nearest neighbor gray-level interpolation to transform the final image ensures VOI alignment between baseline and follow-up images and does not compromise the integrity of MR-derived trabecular bone parameters used in this study.     

Robust feature-based registration using a Gaussian-weighted distance map and brain feature points for brain PET/CT images

Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method.     

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.     

Automatic Prospective Registration of High-Resolution Trabecular Bone Images of the Tibia

Magnetic Resonance Imaging (MRI) longitudinal studies conducted to assess changes in tibia bone quality impose strict requirements on the reproducibility of the prescribed region acquired. Registration, the process of aligning two images, is commonly performed on the images after acquisition. However, techniques to improve image registration precision by adjusting scanning parameters prospectively, prior to image acquisition, would be preferred. We have adapted an automatic prospective mutual information based registration algorithm to a MRI longitudinal study of trabecular bone of the tibia and compared it to a post-scan manual registration. Qualitatively, image alignment due to the prospective registration is shown in 2D subtraction images and 3D surface renderings. Quantitatively, the registration performance is demonstrated by calculating the sum of the squares of the subtraction images. Results show that the sum of the squares is lower for the follow up images with prospective registration by an average of 19.37% ± 0.07 compared to follow up images with post-scan manual registration. Our study found no significant difference between the trabecular bone structure parameters calculated from the post-scan manual registration and the prospective registration images (p > 0.05). All coefficient of variation values for all trabecular bone structure parameters were within a 2–4.5% range which are within values previously reported in the literature. Results suggest that this algorithm is robust enough to be used in different musculoskeletal imaging applications including the hip as well as the tibia.     

Technical note: a physical phantom for assessment of accuracy of deformable alignment algorithms

The purpose of this study was to investigate the feasibility of a simple deformable phantom as a QA tool for testing and validation of deformable image registration algorithms. A diagnostic thoracic imaging phantom with a deformable foam insert was used in this study. Small plastic markers were distributed through the foam to create a lattice with a measurable deformation as the ground truth data for all comparisons. The foam was compressed in the superior-inferior direction using a one-dimensional drive stage pushing a flat "diaphragm" to create deformations similar to those from inhale and exhale states. Images were acquired at different compressions of the foam and the location of every marker was manually identified on each image volume to establish a known deformation field with a known accuracy. The markers were removed digitally from corresponding images prior to registration. Different image registration algorithms were tested using this method. Repeat measurement of marker positions showed an accuracy of better than 1 mm in identification of the reference marks. Testing the method on several image registration algorithms showed that the system is capable of evaluating errors quantitatively. This phantom is able to quantitatively assess the accuracy of deformable image registration, using a measure of accuracy that is independent of the signals that drive the deformation parameters.     

Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice

We are investigating imaging techniques to study the tumor response to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional information. High-resolution magnetic resonance imaging (MRI) can provide anatomical and morphological changes. Image registration can combine MRI and PET images for improved tumor monitoring. In this study, we acquired high-resolution MRI and microPET 18F-fluorodeoxyglucose (FDG) images from C3H mice with RIF-1 tumors that were treated with Pc 4-based PDT. We developed two registration methods for this application. For registration of the whole mouse body, we used an automatic three-dimensional, normalized mutual information algorithm. For tumor registration, we developed a finite element model (FEM)-based deformable registration scheme. To assess the quality of whole body registration, we performed slice-by-slice review of both image volumes; manually segmented feature organs, such as the left and right kidneys and the bladder, in each slice; and computed the distance between corresponding centroids. Over 40 volume registration experiments were performed with MRI and microPET images. The distance between corresponding centroids of organs was 1.5 +/- 0.4 mm which is about 2 pixels of microPET images. The mean volume overlap ratios for tumors were 94.7% and 86.3% for the deformable and rigid registration methods, respectively. Registration of high-resolution MRI and microPET images combines anatomical and functional information of the tumors and provides a useful tool for evaluating photodynamic therapy.     

Algorithms for radiological image registration and their clinical application

This paper reviews recent work in radiological image registration and provides a classification of image registration by type of transformation and by methods employed to compute the transformation. The former includes transformation of 2D images to 2D images of the same individual, transformation of 3D images to 3D images of the same individual, transformation of images to an atlas or model, transformation of images acquired from a number of individuals, transformations for image guided interventions including 2D to 3D registration and finally tissue deformation in image guided interventions. Recent work on computing transformations for registration using corresponding landmark based registration, surface based registration and voxel similarity measures, including entropy based measures, are reviewed and compared. Recently fully automated algorithms based on voxel similarity measures and, in particular, mutual information have been shown to be accurate and robust at registering images of the head when the rigid body assumption is valid. Two approaches to modelling soft tissue deformation for applications in image guided interventions are described. Validation of complex processing tasks such as image registration is vital if these algorithms are to be used in clinical practice. Three alternative validation strategies are presented. These methods are finding application outside the original domain of radiological imaging.     

Fusion of Color Doppler and Magnetic Resonance Images of the Heart

This study was designed to establish and analyze color Doppler and magnetic resonance fusion images of the heart, an approach for simultaneous testing of cardiac pathological alterations, performance, and hemodynamics. Ten volunteers were tested in this study. The echocardiographic images were produced by Philips IE33 system and the magnetic resonance images were generated from Philips 3.0-T system. The fusion application was implemented on MATLAB platform utilizing image processing technology. The fusion image was generated from the following steps: (1) color Doppler blood flow segmentation, (2) image registration of color Doppler and magnetic resonance imaging, and (3) image fusion of different image types. The fusion images of color Doppler blood flow and magnetic resonance images were implemented by MATLAB programming in our laboratory. Images and videos were displayed and saved as AVI and JPG. The present study shows that the method we have developed can be used to fuse color flow Doppler and magnetic resonance images of the heart. We believe that the method has the potential to: fill in information missing from the ultrasound or MRI alone, show structures outside the field of view of the ultrasound through MR imaging, and obtain complementary information through the fusion of the two imaging methods (structure from MRI and function from ultrasound).     

基于4D-CT最大呼吸气相位图像获取三维肺通气的信息

背景：传统的肺功能成像技术存在诸多不便,利用4D-CT中蕴含的通气信息进行功能图像的快速提取对肺部疾病的诊断和治疗有非常重要的意义。 目的：探讨基于三维变形图像配准算法从4D-CT最大吸气相位和最大呼气相位图像中获取肺通气的三维分布的可行性。 方法：利用电影模式采集自由呼吸状态下的胸部CT图像并利用已开发的4D-CT软件进行四维重建,得到吸气末和呼气末双相位CT图像,依次进行肺组织分割、利用基于体积的变形图像配准算法进行三维图像配准、量化分析三维空间象素的位移矢量,最后得到通气度量图即肺功能区的三维分布图。 结果与结论：利用三维变形图像配准算法,实现了从4D-CT最大吸气相位和最大呼气相位图像中获取在任意横断位、冠状位和矢状位的肺通气分布。     

人脑MRI和PET图像的融合方法

目的：研究MRI和PET医学图像融合的方法并且应用在人脑神经解剖中。方法：以Chamfer matching为基本配准方法和以模糊数学为理论基础的融合方法。结果：使得融合后图像有很强的抗配准偏差能力,并且能极大程度地保留原来解剖性信息图像和功能性图像的信息。能根据融合算子的不同组合,得到倾向于不同检查仪器结果的图像。结论：通过本文方法得出的融合后图像,能在人脑解剖结构上显示出该区域的功能状态,对于了解该结构的存在、变异、病变提供了很大的便利。对人脑神经解剖学研究和临床应用都有很大的理论和实践意义。     

Spatio-temporal (2D+T) non-rigid registration of real-time 3D echocardiography and cardiovascular MR image sequences

In this paper we describe a method to non-rigidly co-register a 2D slice sequence from real-time 3D echocardiography with a 2D cardiovascular MR image sequence. This is challenging because the imaging modalities have different spatial and temporal resolution. Non-rigid registration is required for accurate alignment due to imprecision of cardiac gating and natural motion variations between cardiac cycles. In our approach the deformation field between the imaging modalities is decoupled into temporal and spatial components. First, temporal alignment is performed to establish temporal correspondence between a real-time 3D echocardiography frame and a cardiovascular MR frame. Spatial alignment is then performed using an adaptive non-rigid registration algorithm based on local phase mutual information on each temporally aligned image pair. Experiments on seven volunteer datasets are reported. Evaluation of registration errors based on expert-identified landmarks shows that the spatio-temporal registration algorithm gives a mean registration error of 3.56 ± 0.49 and 3.54 ± 0.27 mm for the short and long axis sequences, respectively.     

Fusion of coregistered cross-modality images using a temporally alternating display method

As an aid to the interpretation of functional images, cross-modality coregistration of functional and anatomical images has grown rapidly. Various ways of easily interpreting and visualising coregistered images have previously been investigated; for their display, an intensity-weighted temporally alternating method is used. For brain images, geometric registration involves the automatic alignment method, using the head scalp boundary extracted from the sinogram of a PET emission scan and a surface-matching algorithm; images of the chest or abdomen are registered semi-automatically using a paired point matching algorithm. For the simultaneous display of geometrically registered images, rapid image switching is applied; both images are written with independent colour scales. The rapidly alternating display of two images, synchronised with monitor scanning, induces the fusion of images in the human visual perception system. The accuracy of registration of PET and MRI images is within 2 mm for two point sets. A resulting image is intensified by weighting the display time and/or controlling the intensity map of each image with the degree of interest. This method may be useful for the interpretation and visualisation of coregistered images.     

Three-dimensional reconstruction of median eminence microvascular modules

To test the hypothesis that the median eminence microvasculature has a direct regulatory role in the hormonal communication between the brain and the pituitary gland, it is necessary to determine whether the physical means for such control (e.g. smooth musele sphineters strategically located in the capillary plexus) actually exists. Our approach is to search for such structures in transmission electron micrographs of thin serial sections of the median eminence. The complexity of these images and the anticipated need to include large numbers of them in the study led us to consider computer reconstruction for this problem. We report here the successful three-dimensional reconstruction of capillary modules using digital image processing techniques for capillary feature detection extraction, for construction of montages (mosaies) of overlapping images of the same section, and for automatic image registration by two independent methods without the use of fiducial marks. These tasks have been performed manually in nearly all the published neurobiological reconstructions: here they are performed by programs using only the mathematical properties of the images. Methods like those deseribed here provide the only practical means for executing large scale reconstructions and gaining significant new information about the regulation of blood flow in this region of the brain.     

Temporal subtraction of dual-energy chest radiographs

Temporal subtraction and dual-energy imaging are two enhanced radiography techniques that are receiving increased attention in chest radiography. Temporal subtraction is an image processing technique that facilitates the visualization of pathologic change across serial chest radiographic images acquired from the same patient; dual-energy imaging exploits the differential relative attenuation of x-ray photons exhibited by soft-tissue and bony structures at different x-ray energies to generate a pair of images that accentuate those structures. Although temporal subtraction images provide a powerful mechanism for enhancing visualization of subtle change, misregistration artifacts in these images can mimic or obscure abnormalities. The purpose of this study was to evaluate whether dual-energy imaging could improve the quality of temporal subtraction images. Temporal subtraction images were generated from 100 pairs of temporally sequential standard radiographic chest images and from the corresponding 100 pairs of dual-energy, soft-tissue radiographic images. The registration accuracy demonstrated in the resulting temporal subtraction images was evaluated subjectively by two radiologists. The registration accuracy of the soft-tissue-based temporal subtraction images was rated superior to that of the conventional temporal subtraction images. Registration accuracy also was evaluated objectively through an automated method, which achieved an area-under-the-ROC-curve value of 0.92 in the distinction between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. By combining dual-energy soft-tissue images with temporal subtraction, misregistration artifacts can be reduced and superior image quality can be obtained.     

基于互信息的多分辨率三维脑图像配准方法

在3D多模医学图像的配准方法中，最大互信息法精度高，鲁棒性强，使用范围广，本文将归一化互信息作为相似性测度，采用不同的采样范围和采样子集，使用Powell多参数优化法和Brent一维搜索算法对3DCT，MR和PET脑图像进行了刚体配准，为了加快配准速度，使用了多分辨的金字塔方法，对PET图像采用基于坐标的阈值选取方法对图像进行分割预算法，消除了大部分放射状背景伪影，美国万德贝尔大学对结果进行的评估证明配准精度可达亚体元级。     

A hybrid framework for registration of carotid ultrasound images combining iconic and geometric features

Stroke is the third major cause of death worldwide behind heart disease and cancer. Carotid atherosclerosis is the most frequent cause of ischemic stroke. Early diagnosis of carotid plaque and serial monitoring of its size with the help of imaging modalities can help to prevent the atherosclerotic complications. The main difficulty is inevitable variability of patient’s head positions during image acquisitions. The time series registration of carotid images helps to improve the monitoring, characterization, and quantification of the disease by suppressing the global movements of the patient. In this work, a novel hybrid registration technique has been proposed and evaluated for registration of carotid ultrasound images taken at different times. The proposed hybrid method bridges the gap between the feature-based and intensity-based registration methods. The feature-based iterative closest point algorithm is used to provide a coarse registration which is subsequently refined by the intensity-based algorithm. The proposed framework uses rigid transformation model coupled with mutual information (MI) similarity measure and Powell optimizer. For quantitative evaluation, different registration approaches have been compared using four error metrics: visual information fidelity, structural similarity index, correlation coefficient, and MI. Qualitative evaluation has also been done using the visual examination of the registered image pairs.     

基于最大互信息的人脑MR-PET图像配准方法

利用最大互信息法进行多模医学图像配准近来成为医学图像处理领域的热点。MR和PET图像配准对研究神经组织的结构关系和引导神经外科手术有着重要的指导意义。本文描述了一种基于互信息的人脑MR－PET图像配准方法。我们将这种方法应用于图像的几何对准并给出了初步的评估结果。由于不需要对不同成像模式下的图像灰度间的关系作任何假设,最大互信息法是一种稳健性强,可广泛应用于基于体素的多模图像的配准方法。