Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach

Purpose

Regional infarction identification is important for heart disease diagnosis and management, and myocardial deformation has been shown to be effective for this purpose. Although tagged and strain-encoded MR images can provide such measurements, they are uncommon in clinical routine. On the contrary, cardiac CT images are more available with lower costs, but they only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. The goal of this study is to verify the potential of contrast-enhanced CT images on computer-aided regional infarction identification.

Methods

We propose a biomechanical approach combined with machine learning algorithms. A hyperelastic biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images. The regional strains and CT image intensities are input to a classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions were used to study the performances of using different feature combinations and machine learning algorithms.

Results

Radial strain, circumferential strain, first principal strain, and image intensity were shown to be discriminative features. The highest identification accuracy (\(85\pm 14\) %) was achieved when combining radial strain with image intensity. Random forests gave better results than support vector machines on less discriminative features. Random forests also performed better when all strains were used together.

Conclusion

Although CT images cannot directly measure myocardial deformation, with the use of a biomechanical model, the estimated strains can provide promising identification results especially when combined with CT image intensity.

     

Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images

Purpose

A fully automated segmentation algorithm, progressive surface resolution (PSR), is presented in this paper to determine the closed surface of approximately convex blob-like structures that are common in biomedical imaging. The PSR algorithm was applied to the cortical surface segmentation of 460 vertebral bodies on 46 low-dose chest CT images, which can be potentially used for automated bone mineral density measurement and compression fracture detection.

Methods

The target surface is realized by a closed triangular mesh, which thereby guarantees the enclosure. The surface vertices of the triangular mesh representation are constrained along radial trajectories that are uniformly distributed in 3D angle space. The segmentation is accomplished by determining for each radial trajectory the location of its intersection with the target surface. The surface is first initialized based on an input high confidence boundary image and then resolved progressively based on a dynamic attraction map in an order of decreasing degree of evidence regarding the target surface location.

Results

For the visual evaluation, the algorithm achieved acceptable segmentation for 99.35 % vertebral bodies. Quantitative evaluation was performed on 46 vertebral bodies and achieved overall mean Dice coefficient of 0.939 (with max \(=\) 0.957, min \(=\) 0.906 and standard deviation \(=\) 0.011) using manual annotations as the ground truth.

Conclusions

Both visual and quantitative evaluations demonstrate encouraging performance of the PSR algorithm. This novel surface resolution strategy provides uniform angular resolution for the segmented surface with computation complexity and runtime that are linearly constrained by the total number of vertices of the triangular mesh representation.

     

3D CT imaging in clinical practice

Recent technological advances in multidetector computed tomography (MDCT) have enabled 3D diagnostic imaging with superb spatial and temporal resolution. This often provides useful information on precise detail and relationships.     

A method for going from 2D laparoscope to 3D acquisition of surface landmarks by a novel computer vision approach

Purpose

This paper presents a method to use the Smart Trocars—our new surgical instrument recognition system—or any accurate localization system of surgical instrument for acquiring intraoperative surface data. Complex laparoscopic surgeries need a proper guidance system which requires registering the preoperative data from a CT or MRI scan to the intraoperative patient state. The Smart Trocar can be used to localize the instruments when it comes to contact with the soft tissue surface.

Method

Two successive views through the laparoscope at different angles with the 3D localization of a fixed tool at one single location using the Smart Trocars can point out visible features during the surgery and acquire their location in 3D to provide a depth map in the region of interest. In other words, our method transforms a standard laparoscope system into a system with three-dimensional registration capability.

Result

This method was initially tested on a simulation for uncertainty assessment and then on a rigid model for verification with an accuracy within 2 mm distance. In addition, an in vivo experiment on pig model was also conducted to investigate how the method might be used during a physiologic respiratory cycle.

Conclusion

This method can be applied in a large number of surgical applications as a guidance system on its own or in conjunction with other navigation techniques. Our work encourages further testing with realistic surgical applications in the near future.

     

脑CT血管造影仿真外视镜成像技术与临床价值

目的 探讨脑CT血管造影（CTA）仿真外视镜重建技术、方法与临床价值。方法 采用仿真外视镜和最大强度投影（MIP）、表现遮盖显示（SSD）、仿真内镜（VE）等多种后处理技术，对8例脑CTA进行后处理重建成像，并与DSA作对照。结果 仿真外视镜、MIP、SSD重建均获得了脑动脉整体三维血管解剖，以仿真外视镜成像揭示脑血管及其病变效果最佳。仿真外视镜重建时间最少，其对颅底血管结构的展示以轴位效果最佳，能清晰、准确展示动脉瘤与载瘤动脉、脑动脉与邻近颅底骨尤其垂体窝等骨性解剖结构的毗邻关系，无须剪辑去除颅骨影像，且颅骨、血管影像相得益彰。VE仅揭示血管内腔解剖信息。结论 仿真外视镜成像是脑CTA颇为有效，无创性三维显示脑血管及其病变的重建技术，具有极大的临床应用价值。     

Drug targeting of airway surface liquid: A pharmacological MRI approach

Pharmacological MRI at 4.7 T was used to investigate the secretory response to Sylvestris pine oil stimuli in the rat airways, with the aim of developing an in vivo model in a small laboratory animal. The availability of such a model would greatly facilitate the drug discovery process using compounds active on airway surface liquid (ASL) production, and would make it possible to obtain information on chemoreceptoral mechanisms and to test the effects of environmental substances on the airways. T1- and T2-weighted images were acquired in the trachea and larynx before and at various times after exposure to pine oil. Several post-processing procedures were tested in order to improve the visibility of the secretory response and to measure the enhancement of the signal intensity of ASL. A semiautomatic application software was written to localize and to measure the volume involved in the secretory response to a compound administration. A significant effect of the pine oil administration on the secretory response was founded in trachea (p<0.01) and in the salivary glands (p<0.01). 3D reconstructions of MRI data and virtual endoscopy permitted a quick visualization of tracheal morphology and localization of the greatest response to stimulus. The study demonstrated that, despite technical problems due to the air/tissue interface and to the small dimensions of the experimental animals, the secretory response can be evaluated and the pharmacological MRI (phMRI) of the rat airways is feasible. The potential and the limitations of phMRI investigation in drug targeting of ASL are discussed.     

Target identification of anticancer natural products using a chemical proteomics approach

In recent years, there has been a strong demand worldwide for the identification and development of potential anticancer drugs based on natural products. Natural products have been explored for their diverse biological and therapeutic applications from ancient time. In order to enhance the efficacy and selectivity and to minimize the undesired side effects of anti cancer natural products (ANPs), it is essential to understand their target proteins and their mechanistic pathway. Chemical proteomics is one of the most powerful tools to connect ANP target identification and quantification where labeling and non-labeling based approaches have been used. Herein, we have discussed the various strategies to systemically develop selective ANP based chemical probes to characterise their specific and non-specific target proteins using a chemical proteomic approach in various cancer cell lysates.

Natural products are one of the most effective therapeutic candidates in cancer treatment. In this review, we briefly discuss the target identification of anticancer natural products in different cancer cell lines through a chemical proteomics approach.     

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume

Purpose

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.

Methods

This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.

Results

A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.

Conclusion

A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

     

A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images

Constructing a 3D bone surface model from a limited number of calibrated 2D X-ray images (e.g. 2) and a 3D point distribution model is a challenging task, especially, when we would like to construct a patient-specific surface model of a bone with pathology. One of the key steps for such a 2D/3D reconstruction is to establish correspondences between the 2D images and the 3D model. This paper presents a 2D/3D correspondence building method based on a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformations to find a fraction of best matched 2D point pairs between features extracted from the X-ray images and those extracted from the 3D model. The estimated point pairs are then used to set up a set of 3D point pairs such that we turn a 2D/3D reconstruction problem to a 3D/3D one, whose solutions are well studied. Incorporating this 2D/3D correspondence building method, a 2D/3D reconstruction scheme combining a statistical instantiation with a regularized shape deformation has been developed. Comprehensive experiments on clinical datasets and on images of cadaveric femurs with both non-pathologic and pathologic cases are designed and conducted to evaluate the performance of the 2D/3D correspondence building method as well as that of the 2D/3D reconstruction scheme. Quantitative and qualitative evaluation results are given, which demonstrate the validity of the present method and scheme.     

3D strain imaging using a rectilinear 2D array

Under mechanical compression, tissue movements are inherently three-dimensional. 2-D strain imaging can suffer from decorrelation noise caused by out-of-plane tissue movement in elevation. With 3-D strain imaging, all tissue movements can be estimated and compensated, hence minimizing out-of-plane decorrelation noise. Promising 3-D strain imaging results have been shown using 1-D arrays with mechanical translation in elevation. However, the relatively large slice thickness and mechanical translation can degrade image quality. Using 2-D arrays, an improved elevational resolution can be achieved with electronic focusing. Furthermore, scanning with 2-D arrays is also done electronically, which eliminates the need for mechanical translation. In this paper, we demonstrate the feasibility of 3-D strain imaging using a 4 cm x 4 cm ultrasonic sparse rectilinear 2-D array operating at 5MHz. The signal processing combinations of 2-D or 3-D beamforming followed by 2-D or 3-D strain imaging are studied and compared to each other to evaluate the performance of our 3-D strain imaging system. 3-D beamforming followed by 3-D strain imaging showed best performance in all experiments.     

CT angiography in highly calcified arteries: 2D manual vs. modified automated 3D approach to identify coronary stenoses

Background Two-dimensional axial and manually-oriented reformatted images are traditionally used to analyze coronary data provided by multidetector-row computed tomography angiography (MDCTA). While apparently more accurate in evaluating calcified vessels, 2D methods are time-consuming compared with automated 3D approaches. The purpose of this study was to evaluate the performance of a modified automated 3D approach (using manual vessel isolation and different window and level settings) in a population with high calcium scores who underwent coronary half-millimeter 16-detector-row CT angiography (16×0.5-MDCTA).Methods ECG-gated 16×0.5-MDCTA (16×0.5 mm cross-sections, 0.35×0.35×0.35 mm³ isotropic voxels, 400 ms rotation) was performed after injection of iopamidol (120-ml, 300 mg/ml) in 19 consecutive patients (11 male, 62±10 years-old). Native arteries were independently evaluated for ≥50%-stenoses using both manual 2D and modified automated 3D approaches. Stents and bypass grafts were excluded. Conventional coronary angiography was visually analyzed by 2 observers.Results Median Agatston calcium score was 434. Sensitivities, specificities, positive and negative predictive values for detection of ≥50% coronary stenoses using the 2D and modified 3D approaches were, respectively: 74%/63%, 76%/80%, 45%/34%, and 91%/93% (p=NS for all comparisons). Overall diagnostic accuracies were 75 and 78%, respectively (p=NS). Uninterpretable vessels were, respectively: 37% (77/209) and 35% (73/209) – p=NS. Time to analyze a single study was 160±23 and 53±11 min, respectively (p<0.01).Conclusions This modified automated 3D approach is equivalent to and significantly less time consuming than the traditional manual 2D method for evaluation of ≥50%-stenoses by 16×0.5-MDCTA in native coronary arteries of patients with high calcium scores.     

A 3D interactive multi-object segmentation tool using local robust statistics driven active contours

Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: first, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction-this not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we provide the reader reproducible experiments that demonstrate the capability of the proposed segmentation tool on several public available data sets.     

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.     

Reliability and correlation analysis of computed methods to convert conventional 2D radiological hindfoot measurements to a 3D setting using weightbearing CT

Purpose

The exact radiographic assessment of the hindfoot alignment remains challenging. This is reflected in the different measurement methods available. Weightbearing CT (WBCT) has been demonstrated to be more accurate in hindfoot measurements. However, current measurements are still performed in 2D. This study wants to assess the use of computed methods to convert the former uniplanar hindfoot measurements obtained after WBCT towards a 3D setting.

Methods

Forty-eight patients, mean age of 39.6 ± 13.2 years, with absence of hindfoot pathology were included. A WBCT was obtained, and images were subsequently segmented and analyzed using computer-aided design operations. In addition to the hindfoot angle (HA), other ankle and hindfoot parameters such as the anatomical tibia axis, talocalcaneal axis (TCA), talocrural angle, tibial inclination (TI), talar tilt, and subtalar vertical angle were determined in 2D and 3D.

Results

The mean \(\hbox {HA}_{2\mathrm{D}}\) was \(0.79^{\circ }\) of valgus ± 3.2 and the \(\hbox {HA}_{\mathrm{3D}}\) was \(8.08^{\circ }\) of valgus ± 6.5. These angles differed significantly from each other with a \(P<0.001\). The correlation between both showed to be good by \(\hbox {a}\) Pearson correlation coefficient (r) of 0.72 (\(P < 0.001\)). The \(\hbox {ICC}_{\mathrm{3D}}\) showed to be excellent when compared to the \(\hbox {ICC}_{\mathrm{2D}}\), which was good. Similar findings were obtained in other angles. The highest correlation was seen between the \(\hbox {TI}_{\mathrm{2D}}\) and \(\hbox {TI}_{\mathrm{3D}}\) (r = 0.83, \(P < 0.001\)) and an almost perfect agreement in the \(\hbox {TCA}_\mathrm{3D}\) (\(\hbox {ICC}_{\mathrm{3D}}=0.99\)).

Conclusion

This study shows a good and reliable correlation between the \(\hbox {HA}_{\mathrm{2D}}\) and \(\hbox {HA}_{\mathrm{3D}}\). However, the \(\hbox {HA}_{\mathrm{3D}}\) overcomes the shortcomings of inaccuracy and provides valuable spatial data that could be incorporated during computer-assisted surgery to assess the multiplanar correction of a hindfoot deformity.

     

螺旋CT 2D/3D重建技术在跟骨骨折中的应用价值

目的探讨螺旋CT2D/3D重建技术在跟骨骨折中的运用价值。方法26例跟骨骨折患者均先行螺旋二维轴面断层容积扫描,通过工作站用ADW3.1软件进行2D/3D重建。结果26例患者二维/三维CT扫描均能显示跟骨骨折。结论结合使用2D/3D重建技术,相互补充,能提供正确骨折分型,提高临床治疗效果,降低致残率。     

CT三维最小类内散度多分类支持向量机在肺结节识别中的应用

目的分析CT三维最小类内散度多分类支持向量机(MC-SVM)对肺结节的识别能力及优点。方法选择2012年1月至2014年1月确诊的肺结节病患者50例,根据基于三维矩阵模式的感兴趣体(VOI)的构成,分为结节样和非结节样;采用自动提取算法提取感兴趣区(ROI),分为结节ROI和非结节ROI;采用受试者工作特征(ROC)曲线比较大规模训练人工神经网络(MTANN)、基于矩阵模式的模糊最小二乘SVM(matFLSSVM)、三维矩阵模式MC-SVM和三维最小类内散度MC-SVM的识别精度,同时比较各种方法在不同截断点时的真阳性率和假阳性率。结果三维最小类内散度MC-SVM的识别精度、真阳性率均高于其他算法,而假阳性率低于其他算法(P0.05)。结论三维最小类内散度MC-SVM对肺结节的识别精度较高,值得临床推广应用。     

Chest wall segmentation in automated 3D breast ultrasound scans

In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm.