Computerized method for nonrigid MR-to-PET breast-image registration

We have developed and tested a new simple computerized finite element method (FEM) approach to MR-to-PET nonrigid breast-image registration. The method requires five-nine fiducial skin markers (FSMs) visible in MRI and PET that need to be located in the same spots on the breast and two on the flanks during both scans. Patients need to be similarly positioned prone during MRI and PET scans. This is accomplished by means of a low gamma-ray attenuation breast coil replica used as the breast support during the PET scan. We demonstrate that, under such conditions, the observed FSM displacement vectors between MR and PET images, distributed piecewise linearly over the breast volume, produce a deformed FEM mesh that reasonably approximates nonrigid deformation of the breast tissue between the MRI and PET scans. This method, which does not require a biomechanical breast tissue model, is robust and fast. Contrary to other approaches utilizing voxel intensity-based similarity measures or surface matching, our method works for matching MR with pure molecular images (i.e. PET or SPECT only). Our method does not require a good initialization and would not be trapped by local minima during registration process. All processing including FSMs detection and matching, and mesh generation can be fully automated. We tested our method on MR and PET breast images acquired for 15 subjects. The procedure yielded good quality images with an average target registration error below 4 mm (i.e. well below PET spatial resolution of 6-7 mm). Based on the results obtained for 15 subjects studied to date, we conclude that this is a very fast and a well-performing method for MR-to-PET breast-image nonrigid registration. Therefore, it is a promising approach in clinical practice. This method can be easily applied to nonrigid registration of MRI or CT of any type of soft-tissue images to their molecular counterparts such as obtained using PET and SPECT.     

Preliminary study on the use of nonrigid registration for thoraco-abdominal radiosurgery

The inclusion of organ deformation and movement in radiosurgery treatment planning is of increasing importance as research and clinical applications begin to take into consideration the effects of physiological processes, like breathing, on the shape and position of lesions. In this scenario, the challenge is to localize the target in toto (not only by means of marker sampling) and to calculate the dose distribution as the sum of all the contributions from the positions assumed by the target during the respiratory cycle. The aim of this work is to investigate the use of nonrigid registration for target tracking and dynamic treatment planning, i.e., treatment planning based not on one single CT scan but on multiple CT scans representative of the respiration. Twenty patients were CT scanned at end-inhale and end-exhale. An expert radiation oncologist identified the PTV in both examinations. The two CT data sets per patient were nonrigidly registered using a free-form deformation algorithm based on B-splines. The optimized objective function consisted of a weighted sum of a similarity criterion (Mutual Information) and a regularization factor which constrains the transformation to be locally rigid. Once the transformation was obtained and the registration validated, its parameters were applied to the target only. Finally, the deformed target was compared to the PTV delineated by the radiation oncologist in the other study. The results of this procedure show an agreement between the center of mass as well as volume of the target identified automatically by deformable registration and manually by the radiation oncologist. Moreover, obtained displacements were in agreement with body structure constraints and considerations usually accepted in radiation therapy practice. No significant influence of initial target volume on displacements was found. In conclusion, the proposed method seems to offer the possibility of using nonrigid registrations in radiosurgery treatment planning, even if more cases need to be investigated in order to give a statistical consistency to parameter setup and proposed considerations.     

Automatic nonrigid registration of whole body CT mice images

Three-dimensional intra- and intersubject registration of image volumes is important for tasks that include quantification of temporal/longitudinal changes, atlas-based segmentation, computing population averages, or voxel and tensor-based morphometry. While a number of methods have been proposed to address this problem, few have focused on the problem of registering whole body image volumes acquired either from humans or small animals. These image volumes typically contain a large number of articulated structures, which makes registration more difficult than the registration of head images, to which the majority of registration algorithms have been applied. This article presents a new method for the automatic registration of whole body computed tomography (CT) volumes, which consists of two main steps. Skeletons are first brought into approximate correspondence with a robust point-based method. Transformations so obtained are refined with an intensity-based nonrigid registration algorithm that includes spatial adaptation of the transformation's stiffness. The approach has been applied to whole body CT images of mice, to CT images of the human upper torso, and to human head and neck CT images. To validate the authors method on soft tissue structures, which are difficult to see in CT images, the authors use coregistered magnetic resonance images. They demonstrate that the approach they propose can successfully register image volumes even when these volumes are very different in size and shape or if they have been acquired with the subjects in different positions.     

Crystal identification in positron emission tomography using nonrigid registration to a Fourier-based template

Modern positron emission tomography (PET) detectors are typically made from 2D modular arrays of scintillation crystals. Their characteristic flood field response (or flood histogram) must be segmented in order to correctly determine the crystal of annihilation photon interaction in the system. Crystal identification information thus generated is also needed for accurate system modeling as well as for detailed detector characterization and performance studies. In this paper, we present a semi-automatic general purpose template-guided scheme for the segmentation of flood histograms. We first generate a template image that exploits the spatial frequency information in the given flood histogram using Fourier-space analysis. This template image is a lower order approximation of the flood histogram, and can be segmented with horizontal and vertical lines drawn midway between adjacent peaks in the histogram. The template is then registered to the given flood histogram by a diffeomorphic polynomial-based warping scheme that is capable of iteratively minimizing intensity differences. The displacement field thus calculated is applied to the segmentation of the template resulting in a segmentation of the given flood histogram. We evaluate our segmentation scheme for a photomultiplier tube based PET detector, a detector with readout by a position-sensitive avalanche photodiode (PSAPD) and a detector consisting of a stack of photomultiplier tubes and scintillator arrays. Further, we quantitatively compare the performance of the proposed method to that of a manual segmentation scheme using reconstructed images of a line-source phantom. We also present an adaptive method for distortion reduction in flood histograms obtained for PET detectors that use PSAPDs.     

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.     

Unwarping confocal microscopy images of bee brains by nonrigid registration to a magnetic resonance microscopy image

Confocal microscopy (CM) is a powerful image acquisition technique that is well established in many biological applications. It provides 3-D acquisition with high spatial resolution and can acquire several different channels of complementary image information. Due to the specimen extraction and preparation process, however, the shapes of imaged objects may differ considerably from their in vivo appearance. Magnetic resonance microscopy (MRM) is an evolving variant of magnetic resonance imaging, which achieves microscopic resolutions using a high magnetic field and strong magnetic gradients. Compared to CM imaging, MRM allows for in situ imaging and is virtually free of geometrical distortions. We propose to combine the advantages of both methods by unwarping CM images using a MRM reference image. Our method incorporates a sequence of image processing operators applied to the MRM image, followed by a two-stage intensity-based registration to compute a nonrigid coordinate transformation between the CM images and the MRM image. We present results obtained using CM images from the brains of 20 honey bees and a MRM image of an in situ bee brain.     

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.     

An A4 size registration sheet for general practice

A registration sheet has been designed and tested for use with A4 sized medical records in general practice. Duplicate copies provide both practice and age-sex registers with no further clerical work. A method of using it for temporary residents is also suggested.     

A clinical conundrum: Unilateral decerebrate rigidity

A case is described in which, following a motorcycle accident resulting in a closed head injury, a young woman was rendered unconscious. On examination she was found to have unilateral decerebrate rigidity. Further clinical and radiological examination revealed that there was no damage to the limbs, vertebral column, or spinal cord. Readers are expected to be able to make an anatomical diagnosis on the basis of the facts presented. Clin. Anat. 11:282–283, 1998. © 1998 Wiley-Liss, Inc.     

Pacemaker registration electronic-card. A proposal for a computerised system of storing of the pacemaker registration card

This paper describes an example of a computerised system dedicated to store an Emergency Health Card into a compact and portable memory support such as the CMOS RAM CARD. Details are given from the system used and of the program developed for this task. In particular, patient, pacemaker and lead data are stored by the prototype system. The acquisition layout is similar to that of the European pacemaker registration card but some other sections are added. The possibilities of an introduction of a card system like this on E.E.C. countries is discussed.     

A novel four-dimensional image registration method for radionuclide therapy dosimetry

A novel method for registering sequential SPECT scans (4DRRT) is described, whereby all sequential scans acquired in the course of a therapy or a pre-therapy tracer study may be registered in one pass. The method assumes that a monoexponential decay function can be fitted to the series of sequential SPECT scans. Multiple volumes, presenting with different decay rates, are fitted with different mono-exponential functions. The MSSE (mean sum of squared errors in the least-squares fit algorithm), over the volume used for registration, is the cost function minimized at registration. Simulated data were used to assess the effect of thresholding, smoothing, noise and the multi-exponential nature of the four-dimensional (4D) SPECT studies on the performance of 4DRRT, resulting in three-dimensional (3D) residual registration errors <3.5 mm. The 4DRRT method was then compared to the following 3D registration methods: the correlation coefficient, the sum of absolute differences, the variance of image ratios and the mutual information. The comparisons, using both simulated and clinical data, were based on the standard deviation of the effective decay time distribution, generated from the registered 4D dataset, and showed that image registration using 4DRRT is simpler and more robust compared to the 3D techniques, especially when multiple tumour sites with different decay rates are present.     

A method of image registration for small animal, multi-modality imaging

Many research institutions have a full suite of preclinical tomographic scanners to answer biomedical questions in vivo. Routine multi-modality imaging requires robust registration of images generated by various tomographs. We have implemented a hardware registration method for preclinical imaging that is similar to that used in the combined positron emission tomography (PET)/computed tomography (CT) scanners in the clinic. We designed an imaging chamber which can be rigidly and reproducibly mounted on separate microPET and microCT scanners. We have also designed a three-dimensional grid phantom with 1288 lines that is used to generate the spatial transformation matrix from software registration using a 15-parameter perspective model. The imaging chamber works in combination with the registration phantom synergistically to achieve the image registration goal. We verified that the average registration error between two imaging modalities is 0.335 mm using an in vivo mouse bone scan. This paper also estimates the impact of image misalignment on PET quantitation using attenuation corrections generated from misregistered images. Our technique is expected to produce PET quantitation errors of less than 5%. The methods presented are robust and appropriate for routine use in high throughput animal imaging facilities.     

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。     

带有预处理的计划CT和在线CT的形变配准方法

CT扫描中,水溶性碘造影的存在使得计划CT和在线CT图像中血管内的HU值出现非常大的偏差,从而导致计划CT和在线CT图像错配。针对该问题,本研究提出了一种基于预处理的计划CT和在线CT形变配准方法。首先,根据CT图像组织和结构的信息,利用阈值分割方法分割出血管,并将所有分割中最大的联通区域作为初始分割的强化血管;其次,利用分割得到的强化血管区域外扩5 mm,作为外扩的强化血管,并将血管用固定的HU值进行填充;最后,对完成填充后的图像利用Demons算法进行形变配准。实验结果显示本文提出的带有预处理的形变配准方法,可以较好地解决水溶性碘造影剂引起的CT错配问题。     

Analytic regularization for landmark-based image registration

Landmark-based registration using radial basis functions (RBF) is an efficient and mathematically transparent method for the registration of medical images. To ensure invertibility and diffeomorphism of the RBF-based vector field, various regularization schemes have been suggested. Here, we report a novel analytic method of RBF regularization and demonstrate its power for Gaussian RBF. Our analytic formula can be used to obtain a regularized vector field from the solution of a system of linear equations, exactly as in traditional RBF, and can be generalized to any RBF with infinite support. We statistically validate the method on global registration of synthetic and pulmonary images. Furthermore, we present several clinical examples of multistage intensity/landmark-based registrations, where regularized Gaussian RBF are successful in correcting locally misregistered areas resulting from automatic B-spline registration. The intended ultimate application of our method is rapid, interactive local correction of deformable registration with a small number of mouse clicks.