Quantification of small cerebral ventricular volume changes in treated growth hormone patients using nonrigid registration

Nonrigid registration can automatically quantify small changes in volume of anatomical structures over time by means of segmentation propagation. Here, we use a nonrigid registration algorithm based on optimising normalized mutual information to quantify small changes in brain ventricle volume in magnetic resonance (MR) images of a group of five patients treated with growth hormone replacement therapy and a control group of six volunteers. The lateral ventricles are segmented from each subject image by registering with the brainweb image which has this structure delineated. The mean (standard deviation) volume change measurements are 1.09 (0.73) cm3 for the patient group and 0.08 (0.62) cm3 for the volunteer group; this difference is statistically significant at the 1% level. We validate our volume measurements by determining the precision from three consecutive scans of five volunteers and also comparing the measurements to previously published volume change estimates obtained by visual inspection of difference images. Results demonstrate a precision of sigma < or = 0.52 cm3 (n = 5) and a rank correlation coefficient with assessed difference images of p = 0.7 (n = 11). To determine the level of shape correspondence we manually segmented subject's ventricles and compared them to the propagations using a voxel overlap similarity index, this gave a mean similarity index of 0.81 (n = 7).     

Interventional 4-D C-arm CT perfusion imaging using interleaved scanning and partial reconstruction interpolation

Tissue perfusion measurement during catheter-guided stroke treatment in the interventional suite is currently not possible. In this work, we present a novel approach that uses a C-arm angiography system capable of computed tomography (CT)-like imaging (C-arm CT) for this purpose. With C-arm CT one reconstructed volume can be obtained every 4-6 s which makes it challenging to measure the flow of an injected contrast bolus. We have developed an interleaved scanning (IS) protocol that uses several scan sequences to increase temporal sampling. Using a dedicated 4-D reconstruction approach based on partial reconstruction interpolation (PRI) we can optimally process our data. We evaluated our combined approach (IS-PRI) with simulations and a study in five healthy pigs. In our simulations, the cerebral blood flow values (unit: ml/100 g/min) were 60 (healthy tissue) and 20 (pathological tissue). For one scan sequence the values were estimated with standard deviations of 14.3 and 2.9, respectively. For two interleaved sequences the standard deviations decreased to 3.6 and 1.5, respectively. We used perfusion CT to validate the in vivo results. With two interleaved sequences we achieved promising correlations ranging from r=0.63 to r=0.94. The results suggest that C-arm CT tissue perfusion imaging is feasible with two interleaved scan sequences.     

Analysis of the hemispheric asymmetry using fractal dimension of a skeletonized cerebral surface

We investigated hemispheric asymmetry using the fractal dimension (FD) of the skeletonized cerebral surface. Sixty-two T1-weighted magnetic resonance imaging volumes from normal Korean adults were used. The skeletonization of binary volume data, which corresponded to the union of the gray matter and cerebrospinal flow classified by fuzzy clustering, was performed slice by slice in the sagittal direction, and then skeletonized slices were integrated into the three-dimensional (3-D) hemisphere. Finally, the FD of the 3-D skeletonized cerebral surface was calculated using the box-counting method. We measured the FD of the skeletonized cerebral surface and the volumes of intracranial gray matter and white matter for the whole hemispheres and obtained the hemispheric asymmetries of each measurement. The FD, the gray matter, and the white matter volumes for the whole hemispheres decreased in the old group. The asymmetry of the FD revealed a significant right-greater-than-left asymmetry showed rightward, but did not change according to age and gender. None of the intracranial gray matter or white matter volumes showed any significant asymmetric changes. It could be said that the FD of the skeletonized cerebral surface is a novel measure of cerebral asymmetry.     

3-D active appearance models: segmentation of cardiac MR and ultrasound images

A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R2 = 0.94, 0.97, 0.82, respectively. For echocardiographic analysis, the area correlation was R2 = 0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting.     

Mathematical modeling of the heart using magnetic resonance imaging

A hybrid three-dimensional solid mathematical model of cardiac ventricular geometry developed using magnetic resonance (MR) images of an in vivo canine heart is discussed. The modeling techniques were validated using MR images of an ex vivo heart and direct measurements of cardiac geometry and mass properties. A spin-echo MR sequence with in-plane resolution of 1.0 mm was used to image the canine heart in eleven short-axis planes at contiguous 5-mm intervals. Contour points on the epicardial, left ventricle (LV), and right ventricle (RV) boundaries were selected manually at each slice level. A boundary representation geometric model was constructed by fitting third-order nonuniform rational B-spline surfaces through each set of surface points. Compared to the anatomic specimen (AS), volume errors of the ex vivo model were 0.3, 1.5, and 5.8% for the LV cavity, RV cavity, and total enclosed volumes, respectively. Comparison of cross-sectional areas of the AS and the model at ten levels demonstrated mean model errors of 4.1, 2.5, and 2.9% for the LV, RV, and epicardial boundaries, respectively     

Volume registration using needle paths and point landmarks for evaluation of interventional MRI treatments

We created a method for three-dimensional (3-D) registration of medical images (e.g., magnetic resonance imaging (MRI) or computed tomography) to images of physical tissue sections or to other medical images and evaluated its accuracy. Our method proved valuable for evaluation of animal model experiments on interventional-MRI guided thermal ablation and on a new localized drug delivery system. The method computes an optimum set of rigid body registration parameters by minimization of the Euclidean distances between automatically chosen correspondence points, along manually selected fiducial needle paths, and optional point landmarks, using the iterative closest point algorithm. For numerically simulated experiments, using two needle paths over a range of needle orientations, mean voxel displacement errors depended mostly on needle localization error when the angle between needles was at least 20 degrees. For parameters typical of our in vivo experiments, the mean voxel displacement error was < 0.35 mm. In addition, we determined that the distance objective function was a useful diagnostic for predicting registration quality. To evaluate the registration quality of physical specimens, we computed the misregistration for a needle not considered during the optimization procedure. We registered an ex vivo sheep brain MR volume with another MR volume and tissue section photographs, using various combinations of needle and point landmarks. Mean registration error was always < or = 0.54 mm for MR-to-MR registrations and < or = 0.52 mm for MR to tissue section registrations. We also applied the method to correlate MR volumes of radio-frequency induced thermal ablation lesions with actual tissue destruction. In this case, in vivo rabbit thigh volumes were registered to photographs of ex vivo tissue sections using two needle paths. Mean registration errors were between 0.7 and 1.36 mm over all rabbits, the largest error less than two MR voxel widths. We conclude that our method provides sufficient spatial correspondence to facilitate comparison of 3-D image data with data from gross pathology tissue sections and histology.     

Automated graph-based analysis and correction of cortical volumetopology

The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.     

Knowledge-based interpretation of MR brain images

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5+/-0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.     

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     

Determination of intracranial tumor volumes in a rodent brain using magnetic resonance imaging, Evans blue, and histology: a comparative study

The measurement of tumor volumes is a practical and objective method of assessing the efficacy of a therapeutic agent. However, the relative accuracy of different methods of assessing tumor volume has been unclear. Using T1-weighted, gadolinium-enhanced magnetic resonance Imaging (T1-MRI), Evans Blue infusion and histology we measured intracranial tumor volumes in a rodent brain tumor model (RT2) at days 10, 16 and 18 after implantation of cells in the caudate putamen. There is a good correlation between tumor volumes comparing T1-MRI and Evans Blue (r2 = 0.99), T1-MRI and Histology (r2 = 0.98) and histology and Evans Blue (r2 = 0.93). Each of these methods is reliable in estimating tumor volumes in laboratory animals. There was significant uptake of gadolinium and Evans Blue in the tumor suggesting a wide disruption of the blood-brain barrier.     

Investigation of intraoperative brain deformation using a 1.5-Tinterventional MR system: preliminary results

All image-guided neurosurgical systems that the authors are aware of assume that the head and its contents behave as a rigid body. It is important to measure intraoperative brain deformation (brain shift) to provide some indication of the application accuracy of image-guided surgical systems, and also to provide data to develop and validate nonrigid registration algorithms to correct for such deformation. The authors are collecting data from patients undergoing neurosurgery in a high-field (1.5 T) interventional magnetic resonance (MR) scanner. High-contrast and high-resolution gradient-echo MR image volumes are collected immediately prior to surgery, during surgery, and at the end of surgery, with the patient intubated and lying on the operating table in the operative position. Here, the authors report initial results from six patients: one freehand biopsy, one stereotactic functional procedure, and four resections. The authors investigate intraoperative brain deformation by examining threshold boundary overlays and difference images and by measuring ventricular volume. They also present preliminary results obtained using a nonrigid registration algorithm to quantify deformation. They found that some cases had much greater deformation than others, and also that, regardless of the procedure, there was very little deformation of the midline, the tentorium, the hemisphere contralateral to the procedure, and ipsilateral structures except those that are within 1 cm of the lesion or are gravitationally above the surgical site     

Automated segmentation of human brain MR images aided by fuzzy information granulation and fuzzy inference

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.     

Accuracy of the conductance catheter for measurement of ventricularvolumes seen clinically: effects of electric field homogeneity andparallel conductance

The conductance-volume method is an important clinical tool which allows the assessment of left ventricular function in vivo. However, the accuracy of this method is limited by the homogeneity of electric field the conductance catheter produces and the parallel conductance of surrounding structures. This paper examines these sources of error in volumes seen clinically, The characteristics of electric field within a chamber were examined using computer simulation. Nonconductive and conductive models were constructed and experimental measurements obtained using both single-field (SF) and dual-field (DF) excitation. Results from computer simulations and in vitro measurements were compared to validate the proposed theoretical model of conductance-volume method. The effects of field homogeneity and significance of parallel conductance in volume measurement were then determined. The results of this study show that DF provide a more accurate measure of intraventricular volume than SF, especially at larger volumes. However, both significantly underestimate true volume at larger volumes. In addition, the parallel conductance due to the chamber wall is significant at small volumes, but diminishes at larger volumes. Furthermore, the effect of parallel conductance beyond the chamber wall may be negligible. This study demonstrates the limitations in applying current conductance technology to patients with dilated hearts     

Estimation of current leakage in left and right ventricular conductance volumetry using a dynamic finite element model

Leakage of electric current through cardiac structures surrounding the ventricle is a primary source of error during ventricular volume measurements using a conductance catheter. This error can be represented as a leakage volume, VL. VL is generally estimated by a saline-bolus method, and is assumed constant throughout the cardiac cycle. However, dynamic changes in ventricular volume and cardiac wall thickness could change VL. To estimate VL, a dynamic finite element model of the heart was developed based on MR images. Conductance measurements were simulated using a modeled conductance catheter, and true VL was calculated. VL varied from 22.7 ml (end-systole) to 26.4 ml (end-diastole) in the left ventricle and from 19.9 ml (end-systole) to 26.9 ml (end-diastole) in the right ventricle. The saline-bolus method underestimated VL in both the left (VL = 19.4 ml) and the right (VL = 4.1 ml) ventricular volume measurements. VL increased linearly with the ratio of blood to tissue resistivity, and changed minimally with catheter position. These results indicate that VL has to be estimated dynamically throughout the cardiac cycle to obtain accurate cardiac volume measurements. The results also show that the saline bolus method does not estimate current leakage accurately, especially in the right ventricular volume measurement.     

AD590在节能型药材仓库控制系统中的应用

利用美国Analog Devices公司生产的单片集成模拟型温度传感器AD590价格低、体积小、不需辅助电源、线性好、外围电路简单等优点,实现节能型药材仓库库内外温差的采集,与传统的测两点温度分别进行处理后求差值相比,可获得较高的精度,简化系统的软件设计,提高系统的可靠性.库内温湿度的控制采用分布式控制系统,对图书馆、档案室等其它温控系统,亦具有广泛地推广应用价值.     

Semantic image retrieval in magnetic resonance brain volumes

Practitioners in the area of neurology often need to retrieve multimodal magnetic resonance (MR) images of the brain to study disease progression and to correlate observations across multiple subjects. In this paper, a novel technique for retrieving 2-D MR images (slices) in 3-D brain volumes is proposed. Given a 2-D MR query slice, the technique identifies the 3-D volume among multiple subjects in the database, associates the query slice with a specific region of the brain, and retrieves the matching slice within this region in the identified volumes. The proposed technique is capable of retrieving an image in multimodal and noisy scenarios. In this study, support vector machines (SVM) are used for identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions. In order to achieve reliable image retrieval performance in the presence of misalignments, an image registration-based retrieval framework is developed. The proposed retrieval technique is tested on various modalities. The test results reveal superior robustness performance with respect to accuracy, speed, and multimodality.     

凋亡神经元线粒体超微结构的形态计量学分析

目的:观察并分析人大脑皮层凋亡神经元线粒体超微结构的形态计量学变化.方法:取21例脑外科手术患者的额叶大脑皮质超薄切片中的正常神经元和凋亡神经元的电镜照片各80张,分为对照组与凋亡组.采用形态计量学方法对两组神经元的细胞体、细胞核、线粒体及细胞质基质灰度进行分析.结果:与正常神经元相比,凋亡神经元线粒体的体密度、面密度、数密度、比膜面明显增大(P＜0.01),比表面无明显改变(P＞0.05),线粒体基质与细胞质基质灰度之差明显增大(P＜0.01).结论:凋亡神经元线粒体未发生明显肿胀或增生,但其内膜和嵴的面积明显增加,基质密度降低.     

Real-time volume measurements in the isolated beating rat heart

A new technique, utilizing scattered X-rays, has been developed that is capable of measuring isolated working rat heart left ventricular (LV) volumes continuously throughout the cardiac cycle. The technique was validated by comparing cardiac outputs measured via fluid collections (r=0.959, n=10). The new methodology will permit real-time measurements of pressure-volume relationships, stroke work, and indexes of diastolic function in the isolated working rat heart     

Robust white matter lesion segmentation in FLAIR MRI

This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recovery (FLAIR) MRI. The method computes the volume of lesions with subvoxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important. Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representation to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9% and 83% overlap, respectively), which outperforms other methods. Lesion load studies are included that automatically analyze WML volumes for each brain hemisphere separately. This technique does not require any distributional assumptions/parameters or training samples and is applied on a single MR modality, which is a major advantage compared to the traditional methods.     

MR Image Segmentation Using a Power Transformation Approach

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.