基于自由变形法的多模态医学图像的配准与融合

本研究提出了一种自动识别颈部PET-CT图像特征点的算法，它应用自由变形（FFD）方法以CT图像的特征点为参考使PET图像产生变形，再结合最大互信息法对颈部PET与CT图像进行非刚体配准，最后用改进的小波图像融合法把两者进行融合得出视觉效果比较理想的融合图像。经实际计算得出的变形PET图像与对应CT图像的互信息量大于原始PET图像，并且最后用改进的小波图像融合法得出的融合图像的信息量比一般小波融合大，由此证明本研究所用方法是有效的。     

A Nonrigid Registration of MR Breast Images Using Complex-valued Wavelet Transform

In this paper, a fast, slice-by-slice, nonrigid registration algorithm of dynamic magnetic resonance breast images is presented. The method is based on a multiresolution motion estimation of the breast using complex discrete wavelet transform (CDWT): the pyramid of oriented complex subimages is used to implement a hierarchical phase-matching-based motion estimation algorithm. The resulting motion estimate is nonrigid and pixel-independent. To assess the method performance, we computed the correlation coefficient and the normalized mutual information between pre- and postcontrast images with and without realignment. The indices increased after using our approach and the improvement was superior to rigid or affine registration. A set of clinical scores was also evaluated. The clinical validation demonstrated an increased readability in the subtraction images. In particular, CDWT registration allowed a best definition of breast and lesion borders and greater detail detectability.     

Matching PET and CT scans of the head and neck area: development of method and validation

Positron emission tomography (PET) provides important information on tumor biology, but lacks detailed anatomical information. Our aim in the present study was to develop and validate an automatic registration method for matching PET and CT scans of the head and neck. Three difficulties in achieving this goal are (1) nonrigid motions of the neck can hamper the use of automatic ridged body transformations; (2) emission scans contain too little anatomical information to apply standard image fusion methods; and (3) no objective way exists to quantify the quality of the match results. These problems are solved as follows: accurate and reproducible positioning of the patient was achieved by using a radiotherapy treatment mask. The proposed method makes use of the transmission rather than the emission scan. To obtain sufficient (anatomical) information for matching, two bed positions for the transmission scan were included in the protocol. A mutual information-based algorithm was used as a registration technique. PET and CT data were obtained in seven patients. Each patient had two CT scans and one PET scan. The datasets were used to estimate the consistency by matching PET to CT1, CT1 to CT2, and CT2 to PET using the full circle consistency test. It was found that using our method, consistency could be obtained of 4 mm and 1.3 degrees on average. The PET voxels used for registration were 5.15 mm, so the errors compared quite favorably with the voxel size. Cropping the images (removing the scanner bed from images) did not improve the consistency of the algorithm. The transmission scan, however, could potentially be reduced to a single position using this approach. In conclusion, the represented algorithm and validation technique has several features that are attractive from both theoretical and practical point of view, it is a user-independent, automatic validation technique for matching CT and PET scans of the head and neck, which gives the opportunity to compare different image enhancements.     

Robust and fast shell registration in PET and MR/CT brain images

A robust and fast hybrid method using a shell volume that consists of high contrast voxels with their neighbors is proposed for registering PET and MR/CT brain images. Whereas conventional hybrid methods find the best matched pairs from several manually selected or automatically extracted local regions, our method automatically selects a shell volume in the PET image, and finds the best matched corresponding volume using normalized mutual information (NMI) in overlapping volumes while transforming the shell volume into an MR or CT image. A shell volume not only can reduce irrelevant corresponding voxels between two images during optimization of transformation parameters, but also brings a more robust registration with less computational cost. Experimental results on clinical data sets showed that our method successfully aligned all PET and MR/CT image pairs without losing any diagnostic information, while the conventional registration methods failed in some cases.     

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.     

Evaluation of three-dimensional finite element-based deformable registration of pre- and intraoperative prostate imaging.

In this report we evaluate an image registration technique that can improve the information content of intraoperative image data by deformable matching of preoperative images. In this study, pretreatment 1.5 tesla (T) magnetic resonance (MR) images of the prostate are registered with 0.5 T intraoperative images. The method involves rigid and nonrigid registration using biomechanical finite element modeling. Preoperative 1.5 T MR imaging is conducted with the patient supine, using an endorectal coil, while intraoperatively, the patient is in the lithotomy position with a rectal obturator in place. We have previously observed that these changes in patient position and rectal filling produce a shape change in the prostate. The registration of 1.5 T preoperative images depicting the prostate substructure [namely central gland (CG) and peripheral zone (PZ)] to 0.5 T intraoperative MR images using this method can facilitate the segmentation of the substructure of the gland for radiation treatment planning. After creating and validating a dataset of manually segmented glands from images obtained in ten sequential MR-guided brachytherapy cases, we conducted a set of experiments to assess our hypothesis that the proposed registration system can significantly improve the quality of matching of the total gland (TG), CG, and PZ. The results showed that the method statistically-significantly improves the quality of match (compared to rigid registration), raising the Dice similarity coefficient (DSC) from prematched coefficients of 0.81, 0.78, and 0.59 for TG, CG, and PZ, respectively, to 0.94, 0.86, and 0.76. A point-based measure of registration agreement was also improved by the deformable registration. CG and PZ volumes are not changed by the registration, indicating that the method maintains the biomechanical topology of the prostate. Although this strategy was tested for MRI-guided brachytherapy, the preliminary results from these experiments suggest that it may be applied to other settings such as transrectal ultrasound-guided therapy, where the integration of preoperative MRI may have a significant impact upon treatment planning and guidance.     

利用相关比相似性测度多分辨率配准MR和PET医学图像的方法

采用了全新的相关比相似性测度作为配准的测度准则，提出了有效的磁共振(MR)和正电子发射端层扫描(PET)临床医学图像配准方法。具体设计时，采用了加速的多分辨率的配准方案，对方案中涉及的几何变换选取、重采样、多分辨率体数据表达及最优化方法进行了详细的设计分析。最后，利用多分辨率配准方法，对MR和PET临床医学图像进行配准，给出了令人满意的效果，同时和基于体素灰度的直接配准法相比，配准速度也有了很大提高。     

Incorporation of Preprocedural PET into CT-Guided Radiofrequency Ablation of Hepatic Metastases: a Nonrigid Image Registration Validation Study

This study evaluates the accuracy of augmenting initial intraprocedural computed tomography (CT) during radiofrequency ablation (RFA) of hepatic metastases with preprocedural positron emission tomography (PET) through a hardware-accelerated implementation of an automatic nonrigid PET–CT registration algorithm. The feasibility of augmenting intraprocedural CT with preprocedural PET to improve localization of CT-invisible but PET-positive tumors with images from actual RFA was explored. Preprocedural PET and intraprocedural CT images from 18 cases of hepatic RFA were included. All PET images in the study originated from a hybrid PET/CT scanner, and PET–CT registration was performed in two ways: (1) direct registration of preprocedural PET with intraprocedural CT and (2) indirect registration of preprocedural CT (i.e., the CT of hybrid PET/CT scan) with intraprocedural CT. A hardware-accelerated registration took approximately 2 min. Calculated registration errors were 7.0 and 8.4 mm for the direct and indirect methods, respectively. Overall, the direct registration was found to be statistically not distinct from that performed by a group of clinical experts. The accuracy, execution speed, and compactness of our implementation of nonrigid image registration suggest that existing PET can be overlaid on intraprocedural CT, promising a novel, technically feasible, and clinically viable approach for PET augmentation of CT guidance of RFA.     

Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with normalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient.     

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。     

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。     

Spatial shrinkage/expansion patterns between breast density measured in two MRI scans evaluated by non-rigid registration

Breast MRI acquires many images from the breast, and computer-aided algorithms and display tools are often used to assist the radiologist's interpretation. Women with lifetime risk greater than 20% of developing breast cancer are recommended to receive annual screening MRI, but the current breast MRI computer-aided-diagnosis systems do not provide the necessary function for comparison of images acquired at different times. The purpose of this work was to develop registration methods for evaluating the spatial change pattern of fibroglandular tissue between two breast MRI scans of the same woman taken at different times. The registration method is based on rigid alignment followed by a non-rigid Demons algorithm. The method was tested on three different subjects who had different degrees of changes in the fibroglandular tissue, including two patients who showed different spatial shrinkage patterns after receiving neoadjuvant chemotherapy before surgery, and one control case from a normal volunteer. Based on the transformation matrix, the collapse of multiple voxels on the baseline images to one voxel on the follow-up images is used to calculate the shrinkage factor. Conversely, based on the reverse transformation matrix the expansion factor can be calculated. The shrinkage/expansion factor, the deformation magnitude and direction, as well as the Jacobian determinate at each location can be displayed in a 3D rendering view to show the spatial changes between two MRI scans. These different parameters show consistent results and can be used for quantitative evaluation of the spatial change patterns. The presented registration method can be further developed into a clinical tool for evaluating therapy-induced changes and for early diagnosis of breast cancer in screening MRI.     

Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.     

Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images

We present a technique for modeling liver motion during the respiratory cycle using intensity-based nonrigid registration of gated magnetic resonance (MR) images. Three-dimensional MR images of the abdomens of four volunteers were acquired at end-inspiration, end-expiration, and eight time points in between using respiratory gating. The deformation fields between the images were computed using intensity-based rigid and nonrigid registration algorithms. Global motion is modeled by a rigid transformation while local motion is modeled by a free-form deformation based on B-splines. Much of the liver motion was cranial-caudal translation, which was captured by the rigid transformation. However, there was still substantial residual deformation (approximately 10 mm averaged over the entire liver in four volunteers, and 34 mm at one place in the liver of one volunteer). The computed organ motion model can potentially be used to determine an appropriate respiratory-gated radiotherapy window during which the position of the target is known within a specified excursion.     

评价缺血心肌存活的影像学方法及其计算机分析技术

对缺血心肌的存活作出定量评价有助于冠状动脉血流重建术的病人选择和手术预后。介绍了临床上常见的用于心肌存活评价的超声、核素、磁共振等影像学方法,并针对心肌存活评价介绍了心肌图像的计算机配准和融合等技术在这方面的应用。     

Small animal simultaneous PET/MRI: initial experiences in a 9.4 T microMRI

We developed a non-magnetic positron-emission tomography (PET) device based on the rat conscious animal PET that operates in a small-animal magnetic resonance imaging (MRI) scanner, thereby enabling us to carry out simultaneous PET/MRI studies. The PET detector comprises 12 detector blocks, each being a 4 × 8 array of lutetium oxyorthosilicate crystals (2.22 × 2.22 × 5 mm(3)) coupled to a matching non-magnetic avalanche photodiode array. The detector blocks, housed in a plastic case, form a 38 mm inner diameter ring with an 18 mm axial extent. Custom-built MRI coils fit inside the positron-emission tomography (PET) device, operating in transceiver mode. The PET insert is integrated with a Bruker 9.4 T 210 mm clear-bore diameter MRI scanner. We acquired simultaneous PET/MR images of phantoms, of in vivo rat brain, and of cardiac-gated mouse heart using [(11)C]raclopride and 2-deoxy-2-[(18)F]fluoro-D-glucose PET radiotracers. There was minor interference between the PET electronics and the MRI during simultaneous operation, and small effects on the signal-to-noise ratio in the MR images in the presence of the PET, but no noticeable visual artifacts. Gradient echo and high-duty-cycle spin echo radio frequency (RF) pulses resulted in a 7% and a 28% loss in PET counts, respectively, due to high PET counts during the RF pulses that had to be gated out. The calibration of the activity concentration of PET data during MR pulsing is reproducible within less than 6%. Our initial results demonstrate the feasibility of performing simultaneous PET and MRI studies in adult rats and mice using the same PET insert in a small-bore 9.4 T MRI.     

评价缺血心肌存活的影像学方法及其计算机分析技术

对缺血心肌的存活作出定量评价有助于冠状动脉血流重建术的病人选择和手术预后.介绍了临床上常见的用于心肌存活评价的超声、核素、磁共振等影像学方法,并针对心肌存活评价介绍了心肌图像的计算机配准和融合等技术在这方面的应用.     

Quantitative evaluation of free-form deformation registration for dynamic contrast-enhanced MR mammography

In this paper, we present an evaluation study of a set of registration strategies for the alignment of sequences of 3D dynamic contrast-enhanced magnetic resonance breast images. The accuracy of the optimal registration strategies was determined on unseen data. The evaluation is based on the simulation of physically plausible breast deformations using finite element methods and on contrast-enhanced image pairs without visually detectable motion artifacts. The configuration of the finite element model was chosen according to its ability to predict in vivo breast deformations for two volunteers. We computed transformations for ten patients with 12 simulated deformations each. These deformations were applied to the postcontrast image to model patient motion occurring between pre- and postcontrast image acquisition. The original precontrast images were registered to the corresponding deformed postcontrast images. The performance of several registration configurations (rigid, affine, B-spline based nonrigid, single-resolution, multi-resolution, and volume-preserving) was optimized for five of the ten patients. The images were most accurately aligned with volume-preserving single-resolution nonrigid registration employing 40 or 20 mm control point spacing. When tested on the remaining five patients the optimal configurations reduced the average mean registration error from 1.40 to 0.45 mm for the whole breast tissue and from 1.20 to 0.32 mm for the enhancing lesion. These results were obtained on average within 26 (81) min for 40 (20) mm control point spacing. The visual appearance of the difference images from 30 patients was significantly improved after 20 mm volume-preserving single-resolution nonrigid registration in comparison to no registration or rigid registration. No substantial volume changes within the region of the enhancing lesions were introduced by this nonrigid registration.     

EEG电极与MRI头模型的配准方法

脑功能成像研究中 ,脑电信号与磁共振图像融合分析是一种重要的研究方法。在实现融合分析时首先要解决的问题是电极与头模型的配准 ,目前有三种基本的配准方法 :基准点法、电极可视化方法和表面配准法。本文对这三种方法作了介绍 ,并对这三种方法的特点作了评述。     

Bilateral symmetry analysis of breast MRI

Mammographic interpretation often uses symmetry between left and right breasts to indicate the site of potential tumour masses. This approach has not been applied to breast images obtained from MRI. We present an automatic technique for breast symmetry detection based on feature extraction techniques which does not require any efforts to co-register breast MRI data. The approach applies computer-vision techniques to detect natural biological symmetries in breast MR scans based on three objective measures of similarity: multiresolution non-orthogonal wavelet representation, three-dimensional intensity distributions and co-occurrence matrices. Statistical distributions that are invariant to feature localization are computed for each of the extracted image features. These distributions are later compared against each other to account for perceptual similarity. Studies based on 51 normal MRI scans of randomly selected patients showed that the sensitivity of symmetry detection rate approached 94%. The symmetry analysis procedure presented in this paper can be applied as an aid in detecting breast tissue changes arising from disease.