Geometrical accuracy and fusion of multimodal vascular images: a phantom study

The aim of this work was to compare the geometrical accuracy of x-ray angiography, magnetic resonance imaging (MRI), x-ray computed tomography (XCT), and ultrasound imaging (B-mode and IVUS, or intravascular ultrasound) for measuring the lumen diameters of blood vessels. An image fusion method was also developed to improve these measurements. The images were acquired from a phantom that mimic vessels of known diameters. After acquisition, the multimodal images were coregistered by manual alignment of fiducial markers, and then by maximization of mutual information. The fusion method was performed by means of a fuzzy logic modeling approach followed by a combination process based on a possibilistic theory. The results showed (i) the better geometrical accuracy of XCT and IVUS compared to the other modalities, and (ii) the better accuracy and smaller variability of fused images compared to single modalities, with respect to most diameters investigated. For XCT, the error varied from 0.4% to 5.4%, depending on the vessel diameter that ranged from 0.93 to 6.24 mm. For IVUS, the error ranged from -0.3% to 1.7% but the smallest vessel (0.93 mm) could not be investigated because of the probe size. Compared to others fusion schemes, the XCT-MRI fused images provided the best results for both accuracy (from -1.6% to 0.2% for the three largest vessels) and robustness (mean relative error of 1.9%). To conclude, this work underlined both the usefulness of the multimodality vascular phantom as a validation tool and the utility of image fusion in the vascular context.     

Magnetic resonance angiography of collateral vessel growth in a rabbit femoral artery ligation model

Collateral vessel growth was visualized in a rabbit femoral artery ligation model by serial contrast-enhanced magnetic resonance angiography (MRA) at 1.5 T in comparison with X-ray angiography (XRA). XRA and MRA were performed directly after femoral artery ligation (day 0+) and after 7 and 21 days. XRA (in-plane resolution, 0.3x0.3 mm) was performed with arterial catheterization for fast injection of iodinated contrast agent just proximal to the aortic bifurcation. MRA (in-plane, 0.6x0.6 mm) was performed at 1.5 T with a five-element phased-array coil and slow injection of gadolinium-based MR contrast agent into an ear vein. Collateral vessel scores on two-dimensional XRA projections and on three-dimensional digitally subtracted rotational MRA maximum intensity projections were obtained by two observers and compared. Collateral vessel counts and minimal detectable vessel diameters for MRA and XRA were combined in a computational flow model to interpret differences in spatial detection limits between imaging modalities in terms of flow. Collateral vessel scores were significantly higher in the ligated limb at day 7 (P < 0.05) and more so at day 21 (P < 0.001), in comparison with day 0+ or in the non-ligated control limb on both XRA and MRA. Significantly more (smaller) vessels were visualized with XRA than with MRA, particularly on day 21 (P < 0.05). Inter-observer agreement was high for both XRA (kappa = 0.82) and MRA (kappa = 0.78). The flow model showed that collateral vessels with diameters > 0.3 mm scored by XRA as well as MRA represent nearly 100% of the total blood flow, whereas smaller (0.1-0.3 mm diameter) vessels that can only be detected with XRA contribute little to the blood flow. Serial contrast-enhanced MRA can non-invasively visualize sub-millimeter collateral vessels that represent nearly 100% of the restored blood flow, in a femoral artery ligation model.     

Branching Pattern of the Cerebral Arterial Tree

Quantitative data on branching patterns of the human cerebral arterial tree are lacking in the 1.0–0.1 mm radius range. We aimed to collect quantitative data in this range, and to study if the cerebral artery tree complies with the principle of minimal work (Law of Murray). To enable easy quantification of branching patterns a semi-automatic method was employed to measure 1,294 bifurcations and 2,031 segments on 7 T-MRI scans of two corrosion casts embedded in a gel. Additionally, to measure segments with a radius smaller than 0.1 mm, 9.4 T-MRI was used on a small cast section to characterize 1,147 bifurcations and 1,150 segments. Besides MRI, traditional methods were employed. Seven hundred thirty-three bifurcations were manually measured on a corrosion cast and 1,808 bifurcations and 1,799 segment lengths were manually measured on a fresh dissected cerebral arterial tree. Data showed a large variation in branching pattern parameters (asymmetry-ratio, area-ratio, length-radius-ratio, tapering). Part of the variation may be explained by the variation in measurement techniques, number of measurements and location of measurement in the vascular tree. This study confirms that the cerebral arterial tree complies with the principle of minimum work. These data are essential in the future development of more accurate mathematical blood flow models. Anat Rec, 302:1434–1446, 2019. © 2018 The Authors. The Anatomical Record published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists.     

Quantitative myocardial perfusion magnetic resonance imaging: the impact of pulsatile flow on contrast agent bolus dispersion

Myocardial blood flow (MBF) can be quantified using T1-weighted first-pass magnetic resonance imaging (MRI) in combination with a tracer-kinetic model, like MMID4. This procedure requires the knowledge of an arterial input function which is usually estimated from the left ventricle (LV). Dispersion of the contrast agent bolus may occur between the LV and the tissue of interest. The aim of this study was to investigate the dispersion under conditions of physiological pulsatile blood flow, and to simulate its effect on MBF quantification. The dispersion was simulated in coronary arteries using a computational fluid dynamics (CFD) approach. Simulations were accomplished on straight vessels with stenosis of different degrees and shapes. The results show that dispersion is more pronounced under resting conditions than during hyperemia. Stenosis leads to a reduction of dispersion. In consequence, dispersion results in a systematic MBF underestimation between -0.4% and -9.3%. The relative MBF error depends not only on the dispersion but also on the actual MBF itself. Since MBF under rest is more underestimated than under stress, myocardial perfusion reserve is overestimated between 0.1% and 4.5%. Considering other sources of errors in myocardial perfusion MRI, systematic errors of MBF by bolus dispersion are relatively small.     

一种普适的基于多尺度滤波和统计学混合模型的血管分割方法

血管的精确提取和定位,是实现心脑血管介入手术的关键。多尺度滤波算法可以增强血管目标,同时抑制背景噪声,但并没有把血管从图像背景中区分出来。基于统计学的分割算法,通过对直方图进行拟合实现血管的分类,但需要调整混合模型去拟合特定的图像直方图。为了克服上述问题,提出一种具有固定模型的普适的血管分割方法。首先,利用多尺度滤波算法进行图像预处理。其次,针对滤波增强后数据的直方图曲线,用由3个概率分布函数(1个高斯和2个指数)组成的混合模型进行拟合。期望最大化算法用于混合模型参数的估计。最后,通过最大后验概率分类算法将血管分离出来。为了验证上述方法的有效性,分别在仿真(phantom)数据、磁共振血管造影(MAR)数据和计算机断层血管造影(CTA)数据上进行实验测试。结果表明,所提出的方法在多套仿真数据上的分割误差低于0.3%,同时对于不同模态的血管图像具有很好的分割效果及较强的鲁棒性。     

Conversion of left ventricular endocardial positions from patient-independent co-ordinates into biplane fluoroscopic projections

Electrocardiographic body surface mapping is used clinically to guide catheter ablation of cardiac arrhythmias by providing an estimate of the site of origin of an arrhythmia. The localisation methods used in our group produce results in left-ventricular cylinder co-ordinates (LVCCs), which are patient-independent but hard to interpret during catheterisation in the electrophysiology laboratory. It is preferable to provide these results as three-dimensional (3D) co-ordinates which can be presented as projections in the biplane fluoroscopic views that are used routinely to monitor the catheter position. Investigations were carried out into how well LVCCs can be converted into fluoroscopic projections with the limited anatomical data available in contemporary clinical practice. Endocardial surfaces from magnetic resonance imaging (MRI) scans of 24 healthy volunteers were used to create an appropriate model of the left-ventricular endocardial wall. Methods for estimation of model parameters from biplane fluoroscopic images were evaluated using simulated biplane data created from these surfaces. In addition, the conversion method was evaluated, using 107 catheter positions obtained from eight patients, by computing LVCCs from biplane fluoroscopic images and reconstructing the 3D positions using the model. The median 3D distance between reconstructed positions and measured positions was 4.3 mm.     

基于Metropolis—SA算法的脑部磁共振血管造影图像分割

目的利用三维Markov随机场（MRF）模型分割脑部磁共振血管造影（MRA）。方法MRF的似然概率采用了瑞利分布和高斯混合分布函数,并利用最大期望（EM）算法精确估计出混合参数;先验概率采用Ising—MRF模型,并利用误差试探法估计出正则化参数。为避免利用迭代条件模式（ICM）进行图像分割时常陷入局部最优解,实验提出了基于Metropolis采样算法的模拟退火（SA）技术。结果实现了三维MRF的全局最优解,分割模型可分辨3个体素的细小血管。临床数据采用南方医院影像中心提供的患者TOF-MRA数据（1.5TGE MRI scanner）,空间分辨率0．43mm×0．43mm×0．50mm：原始数据的像素空间大小为512×512×128;实际采用的空间大小和分辨率分别为256×256×64和0．80mm×0．80mm×1．20mm。实验对每一套临床数据采用SA、ICM、MSA算法分别进行分割比较,分割结果存在有限差异,采用15步迭代计算的时间消耗分别为1029S、463S、560S。结论实验通过三维仿真数据分割结果表明,Metropolis—SA迭代求解算法能够实现更低的全局误差．并且实际脑部MRA数据的分割与最大密度投影相比较．反映出较好效果．     

虚拟现实系统在经枕髁入路显露颈静脉结节三维解剖研究中的应用

目的探讨虚拟现实系统在经枕髁入路显露颈静脉结节三维解剖研究中的应用。方法选取18例尸体头颅作为研究对象,采用头颅MRI和CT扫描,将混合造影剂乳胶依次灌注到静脉系统和动脉系统中,灌注后行2次头颅CT扫描。解剖两侧尸体头颅时根据枕髁入路,切除部分小脑半球显露颅神经和脑干,再次行头颅MRI扫描,将所扫描的影像数据输入虚拟现实系统,根据数据结果构建颈静脉孔区三维解剖模型,设计经枕髁入路显露颈静脉结节的手术路径,可选择颅盖和颅底的骨性标志点,采用相应的测量方式测验结果。比较不同手术路径解剖显露情况、手术解剖测量数据及各解剖结构在手术路径微创前后的变化。结果模拟手术路径直观地体现了神经、血管等随操作方向和角度等解剖结构变化。虚拟现实系统和尸体头颅测量结果一致,但是三维解剖模型数据测量无观察和测量角度限制。三维解剖影像模型显示,微创化后手术路径体积、路径中静脉窦体积及岩骨骨性结构小于微创化前,差异有统计学意义(P0.01);脑神经体积在微创化前后差异无显著性(P0.05)。结论经枕髁入路微创化手术路径在限定靶点的情况下显露解剖结构随之变化,也减少对重要神经血管结构损伤,值得临床推广和应用。     

A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs

Three-dimensional reconstruction from volumetric medical images (e.g. CT, MRI) is a well-established technology used in patient-specific modelling. However, there are many cases where only 2D (planar) images may be available, e.g. if radiation dose must be limited or if retrospective data is being used from periods when 3D data was not available. This study aims to address such cases by proposing an automated method to create 3D surface models from planar radiographs. The method consists of (i) contour extraction from the radiograph using an Active Contour (Snake) algorithm, (ii) selection of a closest matching 3D model from a library of generic models, and (iii) warping the selected generic model to improve correlation with the extracted contour. This method proved to be fully automated, rapid and robust on a given set of radiographs. Measured mean surface distance error values were low when comparing models reconstructed from matching pairs of CT scans and planar X-rays (2.57-3.74mm) and within ranges of similar studies. Benefits of the method are that it requires a single radiographic image to perform the surface reconstruction task and it is fully automated. Mechanical simulations of loaded bone with different levels of reconstruction accuracy showed that an error in predicted strain fields grows proportionally to the error level in geometric precision. In conclusion, models generated by the proposed technique are deemed acceptable to perform realistic patient-specific simulations when 3D data sources are unavailable.     

A three-dimensional representation of an athletic female knee joint using magnetic resonance imaging

Intense interest in knee joint mechanics has resulted in the development of numerous models to predict forces acting at the knee. However, few models have accounted for the unique geometric characteristics of the knee joint's articular surfaces when predicting the mechanical response of the joint. The purpose of this study was to simulate accurately the complex geometric characteristics of the tibiofemoral joint for input into a finite element model representing the knee joint of athletic females. The right knee of an athletic female with no history of knee joint trauma was imaged using a 0.5 T magnetic resonance imaging (MRI) unit. Twelve cross-sectional slices of the knee were scanned in each of three orthogonal planes (coronal, sagittal and axial) at slice intervals of 6 mm, 7 m, and 8 mm respectively. A scan plan (two coronal images and an axial image) was also generated to enable calculation of the orthogonal scans with respect to one another. Select anatomical reference points representing cancellous and compact bone, major ligament attachment areas, and articular cartilage of the distal femur and proximal tibia were digitized from the processed shadowgraphs. The processed digitized data were input into a computer graphics program which was the pre- and post-processing software for the finite element analysis package. Contours of the cancellous and compact bone of the tibial and femoral condyles were generated using beta and cubic spline curves. Bezier quadratic and cubic polynomials were used to reconstruct the tibial and femoral shafts. Accuracy of the model was verified by comparing the shape and proportionality of the simulated tibia and femur with the MRI images from which the model was generated and with anatomical literature. Comparisons demonstrated that subtle variations in the complex geometry of the tibiofemoral joint could be accurately simulated using data obtained from MRI scans of an intact knee. Refinements of the imaging and digitizing procedures were proposed to provide even greater accuracy in modelling the anatomy of the tibiofemoral joint.     

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1?cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation CBCT sampled on the segmentation contour candidate. The cost function measured the goodness of fit of the segmentation on the validation image and was minimized using a simplex optimizer. For each validation CBCT image, the segmentations were done five times using a different reference CBCT. The one with the lowest cost function was selected as the final bladder segmentation. Volume- and distance-based metrics and the accuracy of plan selection were evaluated to quantify the performance. Two to four PCA modes were needed to represent the bladder shape variation with less than 0.1?cm average residual error for the training data of each patient. The automatically segmented bladders had a 78.5% mean conformity index with the manual delineations. The mean SD of the local residual error over all patients was 0.24?cm. The agreement of plan selection between automatic and manual bladder segmentations was 77.5%. PCA is an efficient method to describe patient-specific bladder deformation. The statistical-shape-based segmentation approach is robust to handle the relatively poor CBCT image quality and allows for fast and reliable automatic segmentation of the bladder on CBCT for selecting the appropriate plan from a library of plans.     

Evaluation of the effect of myocardial segmentation errors on myocardial blood flow estimates from DCE-MRI

Quantitative analysis of cardiac dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) perfusion datasets is dependent on the drawing (manually or automatically) of myocardial contours. The required accuracy of these contours for myocardial blood flow (MBF) estimation is not well understood. This study investigates the relationship between myocardial contour errors and MBF errors. Myocardial contours were manually drawn on DCE-MRI perfusion datasets of healthy volunteers imaged in systole. Systematic and random contour errors were simulated using spline curves and the resulting errors in MBF were calculated. The degree of contour error was also evaluated by two recognized segmentation metrics. We derived contour error tolerances in terms of the maximum deviation (MD) a contour could deviate radially from the 'true' contour expressed as a fraction of each volunteer's mean myocardial width (MW). Significant MBF errors were avoided by setting tolerances of MD ≤ 0.4?MW, when considering the whole myocardium, MD ≤ 0.3?MW, when considering six radial segments, and MD ≤ 0.2?MW for further subdivision into endo- and epicardial regions, with the exception of the anteroseptal region, which required greater accuracy. None of the considered segmentation metrics correlated with MBF error; thus, both segmentation metrics and MBF errors should be used to evaluate contouring algorithms.     

冠状动脉血管的二维提取和运动跟踪

提出了一种基于图像脊提取和snake模型的复合式方法来实现X射线造影图像序列中冠状动脉血管的二维提取和运动跟踪,并分别对临床采集图像序列和模拟图像进行了实验.结果说明,与经典模型相比本算法自动化程度和精度都提高许多.     

磁共振三维血管成像技术评估股骨颈骨折股骨头血运状态

背景：国内外临床上判断股骨颈骨折后股骨头局部血运的方法较多,但使用时缺点较多,尤其是不能准确判断股骨颈骨折后周围2,3级血管的情况。 目的：利用磁共振三维血管成像技术评估股骨颈骨折股骨头血运状况,为手术方式的选择提供依据。 方法：选择2008-07/12宜春学院临床医学院收治的未行磁共振及三维血管成像检查16例股骨颈骨折患者,行闭合复位两枚双头加压螺钉固定(对照组)。选择2009-01/2011-02收治的行磁共振及三维血管成像检查的股骨颈骨折患者33例,根据检查结果对患侧旋股内侧动脉情况良好的30例行闭合复位两枚双头加压螺钉固定(实验组),另3例行髋关节置换。 结果与结论：对照组中11例骨性愈合,5例出现股骨颈吸收,需行二次手术;实验组中29例骨性愈合,1例需行二次手术。说明磁共振三维成像技术能够清晰地显示骨颈骨折周围2~3级血管成像,指导手术选择,对预后做出较准确判断,减少二次手术的发生。     

Brain volumetry: an active contour model-based segmentation followed by SVM-based classification

In this paper a novel automatic approach to identify brain structures in magnetic resonance imaging (MRI) is presented for volumetric measurements. The method is based on the idea of active contour models and support vector machine (SVM) classifiers. The main contributions of the presented method are effective modifications on brain images for active contour model and extracting simple and beneficial features for the SVM classifier. The segmentation process starts with a new generation of active contour models, i.e., vector field convolution (VFC) on modified brain images. VFC results are brain images with the least non-brain regions which are passed on to the SVM classification. The SVM features are selected according to the structure of brain tissues, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). SVM classifiers are trained for each brain tissue based on the set of extracted features. Although selected features are very simple, they are both sufficient and tissue separately effective. Our method validation is done using the gold standard brain MRI data set. Comparison of the results with the existing algorithms is a good indication of our approach's success.     

A method for accurate modelling of the crystal response function at a crystal sub-level applied to PET reconstruction

Positron emission tomography (PET) images suffer from low spatial resolution and signal-to-noise ratio. Accurate modelling of the effects affecting resolution within iterative reconstruction algorithms can improve the trade-off between spatial resolution and signal-to-noise ratio in PET images. In this work, we present an original approach for modelling the resolution loss introduced by physical interactions between and within the crystals of the tomograph and we investigate the impact of such modelling on the quality of the reconstructed images. The proposed model includes two components: modelling of the inter-crystal scattering and penetration (interC) and modelling of the intra-crystal count distribution (intraC). The parameters of the model were obtained using a Monte Carlo simulation of the Philips GEMINI GXL response. Modelling was applied to the raw line-of-response geometric histograms along the four dimensions and introduced in an iterative reconstruction algorithm. The impact of modelling interC, intraC or combined interC and intraC on spatial resolution, contrast recovery and noise was studied using simulated phantoms. The feasibility of modelling interC and intraC in two clinical (18)F-NaF scans was also studied. Measurements on Monte Carlo simulated data showed that, without any crystal interaction modelling, the radial spatial resolution in air varied from 5.3 mm FWHM at the centre of the field-of-view (FOV) to 10 mm at 266 mm from the centre. Resolution was improved with interC modelling (from 4.4 mm in the centre to 9.6 mm at the edge), or with intraC modelling only (from 4.8 mm in the centre to 4.3 mm at the edge), and it became stationary across the FOV (4.2 mm FWHM) when combining interC and intraC modelling. This improvement in resolution yielded significant contrast enhancement, e.g. from 65 to 76% and 55.5 to 68% for a 6.35 mm radius sphere with a 3.5 sphere-to-background activity ratio at 55 and 215 mm from the centre of the FOV, respectively, without introducing additional noise. Patient images confirmed the usefulness of interC and intraC modelling for improving spatial resolution and contrast. Based on Monte Carlo simulated data, we conclude that four-dimensional modelling of the inter- and intra-crystal interactions during the reconstruction process yields a significantly improved contrast to noise ratio and the stationarity of the spatial resolution in the reconstructed images.     

Local sphere-based co-registration for SAM group analysis in subjects without individual MRI

Synthetic aperture magnetometry (SAM) is a powerful MEG source localization method to analyze evoked as well as induced brain activity. To gain structural information of the underlying sources, especially in group studies, individual magnetic resonance images (MRI) are required for co-registration. During the last few years, the relevance of MEG measurements on understanding the pathophysiology of different diseases has noticeable increased. Unfortunately, especially in patients and small children, structural MRI scans cannot always be performed. Therefore, we developed a new method for group analysis of SAM results without requiring structural MRI data that derives its geometrical information from the individual volume conductor model constructed for the SAM analysis. The normalization procedure is fast, easy to implement and integrates seamlessly into an existing landmark based MEG-MRI co-registration procedure. This new method was evaluated on different simulated points as well as on a pneumatic index finger stimulation paradigm analyzed with SAM. Compared with an established MRI-based normalization procedure (SPM2) the new method shows only minor errors in single subject results as well as in group analysis. The mean difference between the two methods was about 4 mm for the simulated as well as for finger stimulation data. The variation between individual subjects was generally higher than the error induced by the missing MRIs. The method presented here is therefore sufficient for most MEG group studies. It allows accomplishing MEG studies with subject groups where MRI measurements cannot be performed. O. Steinstraeter and I. K. Teismann contributed equally to this work.     

XLIF入路相关的生殖股神经应用解剖及影像学研究

目的　通过标本解剖和影像学研究,找出生殖股神经（genitofemoral nerve,GFN）在各腰椎间隙的走行位置, 结合腰椎前大血管和腰神经根的位置,得出极外侧入路腰椎椎体间融合术（extreme lateral interbody fusion,XLIF）在各腰椎间隙的安全入路范围。 方法　解剖16例成人尸体标本,暴露GFN并观察其与腰大肌的位置关系。在36例腰椎磁共振扫描影像上观察腰大肌和腹部大血管的位置并测量相关解剖数据,通过分析计算得出各腰椎间隙GFN的走行位置和XLIF入路的安全范围。 结果　生殖股神经在腰大肌内段走行于GFN出椎间孔处与腰大肌穿出点连线上方,呈向前的抛物线或近似直线。GFN在L3/4椎间隙或以上位置穿出腰大肌的样本,GFN距椎体后缘的距离为（34.0±6.02）mm,位置分布在Moro分区位于A区和I区。GFN在L3/4椎间隙以下位置穿出腰大肌的样本,GFN距椎体后缘的距离不小于（16.0±2.16）mm,位置分布在I区、II区和III区。 结论　XLIF入路的安全区域在L2/3椎间隙为II区、III区,在L4/5椎间隙为II区。GFN穿出腰大肌的位置在L3/4椎间隙或以上者,L3/4椎间隙的安全区为II区;在L3/4椎间隙以下者,在L3/4椎间隙从任何分区进入都有损伤血管或神经的可能。     

An Approach for Detecting Cerebral Artery Stenosis Based on MRA Images

Cerebral artery stenosis is an important cause and risk factor to ischemic cerebrovascular disease. This paper describes a simple and effective method for the detection of cerebral artery stenosis in magnetic resonance angiography(MRA).Because of the complex structure of the brain blood vessels, the fast and accurate segmentation method is needed. Here, we used the information of the maximum intensity projection(MIP) image to get 3 D vascular structure. As skeleton was one of useful measures for charactering region-based shape features, we extracted 3 D-skeleton of blood vessels by using fast marching method(FMM). Finally, the accurate cross-sectional areas based on cerebrovascular skeleton were computed to determine the location and extent of vascular stenosis. The results showed that our method could effectively detect the cerebral artery stenosis.     

Segmentation,modelling and reconstruction of arterial bifurcations in digital angiography

The paper presents a method to model an arterial bifurcation from a pair of X-ray angiographic images. It is the initial step of a reconstruction process aiming at detecting and quantifying abnormal sites located on bifurcations. The method proposed consists of two steps. First, each image is independently segmented to extract the vessels in the images. The algorithm uses dynamic programming first to find the bifurcation centrelines from the original images, and secondly to extract vessel edges from the morphological gradient images, under a constraint of parallelism with the previously detected centrelines. Then, a three-dimensional bifurcation model is built by adapting cylinders around the three-dimensional bifurcation centrelines. These cylinders are obtained as a stack of binary orientable ellipses fitted to the projection densities in the corresponding cross-sections. Results obtained on simulated data, phantom and femoral bifurcations are displayed.