An efficient tissue classifier for building patient-specific finiteelement models from X-ray CT images

The authors developed an efficient semiautomatic tissue classifier for X-ray computed tomography (CT) images which can be used to build patient- or animal-specific finite element (FE) models for bioelectric studies. The classifier uses a gray scale histogram for each tissue type and three-dimensional (3-D) neighborhood information. A total of 537 CT images from four animals (pigs) were classified with an average accuracy of 96.5% compared to manual classification by a radiologist. The use of 3-D, as opposed to 2-D, information reduced the error rate by 78%. Models generated using minimal or full manual editing yielded substantially identical voltage profiles. For the purpose of calculating voltage gradients or current densities in specific tissues, such as the myocardium, the appropriate slices need to be fully edited, however. The authors' classifier offers an approach to building FE models from image information with a level of manual effort that can be adjusted to the need of the application     

Signal compensation and compressed sensing for magnetization-prepared MR angiography

Magnetization-prepared acquisitions offer a trade-off between image contrast and scan efficiency for magnetic resonance imaging. Because the prepared signals gradually decay, the contrast can be improved by frequently repeating the preparation, which in turn significantly increases the scan time. A common solution is to perform the data collection progressing from low- to high-spatial-frequency samples following each preparation. Unfortunately, this leads to loss of spatial resolution, and thereby image blurring. In this work, a new technique is proposed that first corrects the signal decay in high-frequency data to mitigate the resolution loss and improve the image contrast without reducing the scan efficiency. The proposed technique then employs a sparsity-based nonlinear reconstruction to further improve the image quality. In addition to reducing the amplified high-frequency noise, this reconstruction extrapolates missing k-space samples in the case of undersampled compressed-sensing acquisitions. The technique is successfully demonstrated for noncontrast-enhanced flow-independent angiography of the lower extremities, an application that substantially benefits from both the signal compensation and the nonlinear reconstruction.     

Learning-based ventricle detection from cardiac MR and CT images

The objective of this work is to investigate the issue of automatically detecting regions of interest (ROI's) in medical images. It is assumed that the regions to be detected can be roughly segmented by a threshold based on a likelihood measure of the ROI, First, an analysis of the global histogram is used to compute a preliminary threshold that is likely near the optimal one. The histogram analysis is motivated by the analytical result of a bell image intensity model proposed in this work. Then, the preliminary threshold is used to segment the input image, resulting in an attention map, which contains an attention region that approximates the ROI as well as many spurious ones. Due to the nonoptimality of the preliminary threshold, it can happen that the attention region contains a part of, or more regions than, the ROI. Learning takes place in two stages: (1) learning for automatic selection of the preliminary threshold value and (2) learning for automatically selecting the ROI from the attention map while dynamically tuning the threshold according to the learned-likelihood function. Experiments have been conducted to approximately locate the endocardium boundaries of the left and right ventricles from gradient-echo magnetic resonance (MR) images. Cardiac computed tomography (CT) images have also been used for testing. The boundary of the segmented region provided by this algorithm is not very accurate and is meant to be used for further fine tuning based on other application-specific measures     

Simultaneous reconstruction of activity and attenuation for PET/MR

Medical investigations targeting a quantitative analysis of the position emission tomography (PET) images require the incorporation of additional knowledge about the photon attenuation distribution in the patient. Today, energy range adapted attenuation maps derived from computer tomography (CT) scans are used to effectively compensate for image quality degrading effects, such as attenuation and scatter. Replacing CT by magnetic resonance (MR) is considered as the next evolutionary step in the field of hybrid imaging systems. However, unlike CT, MR does not measure the photon attenuation and thus does not provide an easy access to this valuable information. Hence, many research groups currently investigate different technologies for MR-based attenuation correction (MR-AC). Typically, these approaches are based on techniques such as special acquisition sequences (alone or in combination with subsequent image processing), anatomical atlas registration, or pattern recognition techniques using a data base of MR and corresponding CT images. We propose a generic iterative reconstruction approach to simultaneously estimate the local tracer concentration and the attenuation distribution using the segmented MR image as anatomical reference. Instead of applying predefined attenuation values to specific anatomical regions or tissue types, the gamma attenuation at 511 keV is determined from the PET emission data. In particular, our approach uses a maximum-likelihood estimation for the activity and a gradient-ascent based algorithm for the attenuation distribution. The adverse effects of scattered and accidental gamma coincidences on the quantitative accuracy of PET, as well as artifacts caused by the inherent crosstalk between activity and attenuation estimation are efficiently reduced using enhanced decay event localization provided by time-of-flight PET, accurate correction for accidental coincidences, and a reduced number of unknown attenuation coefficients. First results achieved with measured whole body PET data and reference segmentation from CT showed an absolute mean difference of 0.005 cm?1 (< 20%) in the lungs, 0.0009 cm?1 (< 2%) in case of fat, and 0.0015 cm?1 (< 2%) for muscles and blood. The proposed method indicates a robust and reliable alternative to other MR-AC approaches targeting patient specific quantitative analysis in time-of-flight PET/MR.     

Evaluation of iterative sparse object reconstruction from few projections for 3-D rotational coronary angiography

A 3-D reconstruction of the coronary arteries offers great advantages in the diagnosis and treatment of cardiovascular disease, compared to 2-D X-ray angiograms. Besides improved roadmapping, quantitative vessel analysis is possible. Due to the heart's motion, rotational coronary angiography typically provides only 5–10 projections for the reconstruction of each cardiac phase, which leads to a strongly undersampled reconstruction problem. Such an ill-posed problem can be approached with regularized iterative methods. The coronary arteries cover only a small fraction of the reconstruction volume. Therefore, the minimization of the ${mbi L}_1$ norm of the reconstructed image, favoring spatially sparse images, is a suitable regularization. Additional problems are overlaid background structures and projection truncation, which can be alleviated by background reduction using a morphological top-hat filter. This paper quantitatively evaluates image reconstruction based on these ideas on software phantom data, in terms of reconstructed absorption coefficients and vessel radii. Results for different algorithms and different input data sets are compared. First results for electrocardiogram-gated reconstruction from clinical catheter-based rotational X-ray coronary angiography are presented. Excellent 3-D image quality can be achieved.      

Finite-element modeling of bones from CT data: sensitivity to geometry and material uncertainties

The aim of this paper is to analyze how the uncertainties in modelling the geometry and the material properties of a human bone affect the predictions of a finite-element model derived from computed tomography (CT) data. A sensitivity analysis, based on a Monte Carlo method, was performed using three femur models generated from in vivo CT datasets, each subjected to two different loading conditions. The geometry, the density and the mechanical properties of the bone tissue were considered as random input variables. Finite-element results typically used in biomechanics research were considered as statistical output variables, and their sensitivity to the inputs variability assessed. The results showed that it is not possible to define a priori the influence of the errors related to the geometry definition process and to the material assignment process on the finite-element analysis results. The errors in the geometric representation of the bone are always the dominant variables for the stresses, as was expected. However, for all the variables, the results seemed to be dependent on the loading condition and to vary from subject to subject. The most interesting result is, however, that using the proposed method to build a finite-element model of a femur from a CT dataset of the quality typically achievable in the clinical practice, the coefficients of variation of the output variables never exceed the 9%. The presented method is hence robust enough to be used for investigating the mechanical behavior of bones with subject-specific finite-element models derived from CT data taken in vivo.     

全脑CT灌注成像在邻近大血管颅内肿瘤中的临床应用

目的探讨全脑CT灌注(WBCTP)成像在邻近大血管颅内肿瘤中的临床应用价值。方法对40例经手术病理证实为邻近大血管颅内肿瘤患者行WBCTP检查,WBCTP检查获得达峰时间(TTP)、平均通过时间(MTT)、脑血流量(CBF)、脑血容量(CBV)参数的三维灌注图,同时获得动态CT血管成像(4D-CTA)图像。三维CT血管成像(3D-CTA)图像包括容积再现(VR)图像,最大密度投影(MIP)图像,和通过后处理重建的肿瘤与3D-CTA的融合图像。分析颅内肿瘤患者的CTP和CTA表现。结果所有WBCTP图像均能满足评价标准,在三维灌注图上确定异常灌注区。脑膜瘤(n=20)与胶质瘤(n=18)的CBF和CBV值比较差异有统计学意义(P﹤0.05)。有35例患者的CTA图能找到供血动脉和引流静脉。23例肿瘤毗邻邻近大动脉,13例肿瘤毗邻邻近静脉窦,4例患者同时累及大动脉及静脉窦。大动脉受压移位有20例,被肿瘤包裹7例,静脉窦被受压移位有12例,上矢状窦受侵并致管腔变窄5例。结论WBCTP可以实现对邻近大血管颅内肿瘤的全面评价,对颅内肿瘤的诊断及评价肿瘤与周围大血管的关系有重要价值,从而指导临床治疗。     

Region of interest reconstruction from truncated data in circular cone-beam CT

The circular scanning trajectory is one of the most widely adopted data-acquisition configurations in computed tomography (CT). The Feldkamp, Davis, Kress (FDK) algorithm and its various modifications have been developed for reconstructing approximately three-dimensional images from circular cone-beam data. When data contain transverse truncations, however, these algorithms may reconstruct images with significant truncation artifacts. It is of practical significance to develop algorithms that can reconstruct region-of-interest (ROI) images from truncated circular cone-beam data that are free of truncation artifacts and that have an accuracy comparable to that obtained from nontruncated cone-beam data. In this work, we have investigated and developed a backprojection-filtration (BPF)-based algorithm for ROI-image reconstruction from circular cone-beam data containing transverse truncations. Furthermore, we have developed a weighted BPF algorithm to exploit "redundant" information in data for improving image quality. In an effort to validate and evaluate the proposed BPF algorithms for circular cone-beam CT, we have performed numerical studies by using both computer-simulation data and experimental data acquired with a radiotherapy cone-beam CT system. Quantitative results in these studies demonstrate that the proposed BPF algorithms for circular cone-beam CT can reconstruct ROI images free of truncation artifacts.     

Pulmonary airways: 3-D reconstruction from multislice CT and clinical investigation

In the framework of computer-aided diagnosis, this paper proposes a novel functionality for the computerized tomography (CT)-based investigation of the pulmonary airways. It relies on an energy-based three-dimensional (3-D) reconstruction of the bronchial tree from multislice CT acquisitions, up to the sixth- to seventh-order subdivisions. Global and local analysis of the reconstructed airways is possible by means of specific visualization modalities, respectively, the CT bronchography and the virtual bronchoscopy. The originality of the 3-D reconstruction approach consists in combining axial and radial propagation potentials to control the growth of a subset of low-order airways extracted from the CT volume by means of a robust mathematical morphology operator-the selective marking and depth constrained (SMDC) connection cost. The proposed approach proved to be robust with respect to a large spectrum of airway pathologies, including even severe stenosis (bronchial lumen obstruction/collapse). Validated by expert radiologists, examples of airway 3-D reconstructions are presented and discussed for both normal and pathological cases. They highlight the interest in considering CT bronchography and virtual bronchoscopy as complementary tools for clinical diagnosis and follow-up of airway diseases.     

Ex vivo assessment of trabecular bone structure from three-dimensional projection reconstruction MR micro-images

Magnetic resonance (MR) imaging has recently been proposed for assessing osteoporosis and predicting fracture risks. However, accurate acquisition techniques and image analysis protocols for the determination of the trabecular bone structure are yet to be defined. The aim of this study was to assess the potential of projection reconstruction (PR) MR microscopy in the analysis of the three-dimensional (3-D) architecture of trabecular bone and in the prediction of its biomechanical properties. High-resolution 3-D PR images (41 x 41 x 82 microm3 voxels) of 15 porcine trabecular bone explants were analyzed to determine the trabecular bone volume fraction (Vv), the mean trabecular thickness (Tb.Th), and the mean trabecular separation (Tb.Sp) using the method of directed secants. These parameters were then compared with those derived from 3-D conventional spin-echo microimages. In both cases, segmentation of the high-resolution images into bone and bone marrow was obtained using a spatial adaptive threshold. The contemporary inclusion of Vv, Tb.Th and 1/Tb.Sp in a multiple regression analysis significantly improved the prediction of Young's modulus (YM). The parameters derived from the PR spin-echo images were found to be stronger predictors of YM (R2 = 0.94, p = 0.004) than those derived from conventional spin-echo images (R2 = 0.79, p = 0.051). Our study indicates that projection reconstruction MR microscopy appears to be more accurate than the conventional Fourier transform method in the quantification of trabecular bone structure and in the prediction of its bioimechanical properties. The proposed PR approach should be readily adaptable to the in vivo MRI studies of osteoporosis.     

CT重建中滤波器的设计与实现

CT重建算法主要包括解析法和迭代法,因为解析法重建速度快,所需数据存储空间较小,所以在实际中的应用更为广泛。在商业CT中,几乎毫无例外地采用卷积反投影重建算法,滤波算子是这一算法中一个非常重要的部分。目前CT重建算法的研究热点—方面是改进算法提高图像重建速度,另一研究内容主要集中在预处理滤波器的设计与实现,以便得到边缘清晰、平滑及噪声较少的图像。本文从滤波器的设计定义出发,分析讨论了它的性质,并在此基础上提出来一种新的滤波器。经过实验验证,新的滤波器取得比较理想的结果。     

Markov random field modeling for three-dimensional reconstruction of the left ventricle in cardiac angiography

This paper reports on a method for left ventricle three-dimensional (3-D) reconstruction from two orthogonal ventriculograms. The proposed algorithm is voxel-based and takes into account the conical projection geometry associated with the biplane image acquisition equipment. The reconstruction process starts with an initial ellipsoidal approximation derived from the input ventriculograms. This model is subsequently deformed in such a way as to match the input projections. To this end, the object is modeled as a 3-D Markov-Gibbs random field, and an energy function is defined so that it includes one term that models the projections compatibility and another one that includes the space-time regularity constraints. The performance of this reconstruction method is evaluated by considering the reconstruction of mathematically synthesized phantoms and two 3-D binary databases from two orthogonal synthesized projections. The method is also tested using real biplane ventriculograms. In this case, the performance of the reconstruction is expressed in terms of the projection error, which attains values between 9.50% and 11.78 % for two biplane sequences including a total of 55 images.     

Dielectric profiles reconstruction via the quadratic approach in 2-D geometry from multifrequency and multifrequency/multiview data

Deals with the reconstruction of the contrast function of a dielectric cylinder with rectangular cross section starting from the knowledge of the electric scattered far field produced under the incidence of plane waves. We analyze the set of the reconstructable Fourier harmonics of the unknown permittivity contrast function with linear and quadratic approaches. This set depends on the ranges of the wavenumbers /spl beta/, of the angles of incidence /spl theta//sub i/ of the impinging plane waves, and of the observation angles /spl theta//sub o/. We discuss a simple way to describe such a dependence, which allows us to find out that the set of the retrievable harmonics for the quadratic approach contains that for the linear one. Moreover, our investigation points out how increasing the amount of independent data through a multifrequency/multiview measurement scheme allows us to enlarge the set of the retrievable unknown harmonics with respect to a multifrequency/single-view one. Our analysis is confirmed by numerical results. Memory storage requirements and processing time consumption for the quadratic approach are greatly reduced thanks to the massive use of the fast Fourier transform algorithm.     

Multiresolution morphological fusion of MR and CT images of thehuman brain

A hierarchical image fusion scheme is presented which preserves the details of the input images regardless of their scale. The technique is demonstrated by fusing images of the human brain derived from magnetic resonance (MR) and computed tomography (CT) scanners. Results are given to show that fused images preserve a more complete representation of anatomical and pathological structures, providing information that cannot be obtained by processing the images at a single scale     

The radon-split method for helical cone-beam CT and its application to nongated reconstruction

The mathematical analysis of exact filtered back-projection algorithms is strictly related to Radon inversion. We show how filter-lines can be defined for the helical trajectory, which serve for the extraction of contributions of particular kinds of Radon-planes. Due to the Fourier-slice theorem, Radon-planes with few intersections with the helix are associated with low-frequency contributions to transversal slices. This insight leads to different applications of the new method. The application presented here enables the incorporation of an arbitrary amount of redundant data in an approximate way. This means that the back-projection is not restricted to an n-Pi interval. A detailed mathematical analysis, in which we demonstrate how the defined filter-lines work, concludes this paper.     

Physiologically based modeling of 3-D vascular networks and CT scan angiography

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.     

Generation and visualization of four-dimensional MR angiography data using an undersampled 3-D projection trajectory

Time-resolved contrast-enhanced magnetic resonance (MR) angiography (CE-MRA) has gained in popularity relative to X-ray Digital Subtraction Angiography because it provides three-dimensional (3-D) spatial resolution and it is less invasive. We have previously presented methods that improve temporal resolution in CE-MRA while providing high spatial resolution by employing an undersampled 3-D projection (3D PR) trajectory. The increased coverage and isotropic resolution of the 3D PR acquisition simplify visualization of the vasculature from any perspective. We present a new algorithm to develop a set of time-resolved 3-D image volumes by preferentially weighting the 3D PR data according to its acquisition time. An iterative algorithm computes a series of density compensation functions for a regridding reconstruction, one for each time frame, that exploit the variable sampling density in 3D PR. The iterative weighting procedure simplifies the calculation of appropriate density compensation for arbitrary sampling patterns, which improve sampling efficiency and, thus, signal-to-noise ratio and contrast-to-noise ratio, since it is does not require a closed-form calculation based on geometry. Current medical workstations can display these large four-dimensional studies, however, interactive cine animation of the data is only possible at significantly degraded resolution. Therefore, we also present a method for interactive visualization using powerful graphics cards and distributed processing. Results from volunteer and patient studies demonstrate the advantages of dynamic imaging with high spatial resolution.     

Automatic registration of CT and MR brain images using correlation of geometrical features

Describes an automated approach to register CT and MR brain images. Differential operators in scale space are applied to each type of image data, so as to produce feature images depicting "ridgeness". The resulting CT and MR feature images show similarities which can be used for matching. No segmentation is needed and the method is devoid of human interaction. The matching is accomplished by hierarchical correlation techniques. Results of 2-D and 3-D matching experiments are presented. The correlation function ensures an accurate match even if the scanned volumes to be matched do not completely overlap, or if some of the features in the images are not similar.     

锥束CT系统的3D Shepp-Logan体模仿真及其投影数据重建

为了验证锥束CT成像算法的性能,提出了使用Fortran语言编写的3D Shepp-Logan体模作为算法性能验证的参考模型,并详细介绍了3DShepp-Logan体模的参数设置及编程方法。然后,直接加入到正投影程序中得到投影数据。最后,利用得到的投影数据进行了三维医学图像重建的仿真实验。经过实验验证,表明了使用Fortran语言编写的3DSheppLogan体模来验证算法性能是准确可行的。     

An automatic technique for finding and localizing externallyattached markers in CT and MR volume images of the head

An image processing technique is presented for finding and localizing the centroids of cylindrical markers externally attached to the human head in computed tomography (CT) and magnetic resonance (MR) image volumes. The centroids can be used as control points for image registration. The technique, which is fast, automatic, and knowledge-based, has two major steps. First, it searches the entire image volume to find one voxel inside each marker-like object. The authors call this voxel a “candidate” voxel, and they call the object a candidate marker. Second, it classifies the voxels in a region surrounding the candidate voxel as marker or nonmarker voxels using knowledge-based rules and calculates an intensity-weighted centroid for each true marker. The authors call this final centroid the “fiducial” point of the marker. The technique was developed on 42 scans of six patients-one CT and six MR scans per patient. There are four markers attached to each patient for a total of 168 marker images. For the CT images the false marker rate was zero. For MR the false marker rate was 1.4% (Two out of 144 markers). To evaluate the accuracy of the fiducial points, CT-MR registration was performed after correcting the MR images for geometrical distortion. The fiducial registration accuracy averaged 0.4 mm and was better than 0.6 mm for each of the eighteen image pairs