Three-dimensional guide-wire reconstruction from biplane image sequences for integrated display in 3-D vasculature

Using three-dimensional rotational X-ray angiography (3DRA), three-dimensional (3-D) information of the vasculature can be obtained prior to endovascular interventions. However, during interventions, the radiologist has to rely on fluoroscopy images to manipulate the guide wire. In order to take full advantage of the 3-D information from 3DRA data during endovascular interventions, a method is presented that yields an integrated display of the position of the guide wire and vasculature in 3-D. The method relies on an automated method that tracks the guide wire simultaneously in biplane fluoroscopy images. Based on the calibrated geometry of the C-arm, the 3-D guide-wire position is determined and visualized in the 3-D coordinate system of the vasculature. The method is evaluated in an intracranial anthropomorphic vascular phantom. The influence of the angle between projections, distortion correction of the projection images, and accuracy of geometry knowledge on the accuracy of 3-D guide-wire reconstruction from biplane images is determined. If the calibrated geometry information is used and the images are corrected for distortion, a mean distance to the reference standard of 0.42 mm and a tip distance of 0.65 mm is found, which means that accurate guide-wire reconstruction from biplane images can be performed.     

Guide-wire tracking during endovascular interventions

A method is presented to extract and track the position of a guide wire during endovascular interventions under X-ray fluoroscopy. The method can be used to improve guide-wire visualization in low-quality fluoroscopic images and to estimate the position of the guide wire in world coordinates. A two-step procedure is utilized to track the guide wire in subsequent frames. First, a rough estimate of the displacement is obtained using a template-matching procedure. Subsequently, the position of the guide wire is determined by fitting a spline to a feature image. The feature images that have been considered enhance line-like structures on: 1) the original images; 2) subtraction images; and 3) preprocessed images in which coherent structures are enhanced. In the optimization step, the influence of the scale at which the feature is calculated and the additional value of using directional information is investigated. The method is evaluated on 267 frames from ten clinical image sequences. Using the automatic method, the guide wire could be tracked in 96% of the frames, with a similar accuracy to three observers, although the position of the tip was estimated less accurately.     

Guide wire reconstruction and visualization in 3DRA using monoplane fluoroscopic imaging

A method has been developed that, based on the guide wire position in monoplane fluoroscopic images, visualizes the approximate guide wire position in the three-dimensional (3-D) vasculature, that is obtained prior to the intervention with 3-D rotational X-ray angiography (3DRA). The method assumes the position of the guide wire in the fluoroscopic images is known. A two-dimensional feature image is determined from the 3DRA data. In this feature image, the guide wire position is determined in a two-step approach: a mincost algorithm is used to determine a suitable position for the guide wire, and subsequently a snake optimization technique is applied to move the guide wire to a better position. The resulting guide wire can then be visualized in 3-D in combination with the 3DRA dataset. The reconstruction accuracy of the method has been evaluated using a 3DRA image of a vascular phantom filled with contrast, and monoplane fluoroscopic images of the same phantom without contrast and with a guide wire inserted. The evaluation has been performed for different projection angles, and with different parameters for the method. The final result does not appear to be very sensitive to the parameters of the method. The average mean error of the estimated 3-D guide wire position is 1.5 mm, and the average tip distance is 2.3 mm. The effect of inaccurate C-arm geometry information is also investigated. Small errors in geometry information (up to 1 degrees) will slightly decrease the 3-D reconstruction accuracies, with an error of at most 1 mm. The feasibility of this approach on clinical data is demonstrated.     

Automatic fusion of freehand endoscopic brain images to three-dimensional surfaces: creating stereoscopic panoramas

A major limitation of the use of endoscopes in minimally invasive surgery is the lack of relative context between the endoscope and its surroundings. The purpose of this work was to fuse images obtained from a tracked endoscope to surfaces derived from three-dimensional (3-D) preoperative magnetic resonance or computed tomography (CT) data, for assistance in surgical planning, training and guidance. We extracted polygonal surfaces from preoperative CT images of a standard brain phantom and digitized endoscopic video images from a tracked neuro-endoscope. The optical properties of the endoscope were characterized using a simple calibration procedure. Registration of the phantom (physical space) and CT images (preoperative image space) was accomplished using fiducial markers that could be identified both on the phantom and within the images. The endoscopic images were corrected for radial lens distortion and then mapped onto the extracted surfaces via a two-dimensional 2-D to 3-D mapping algorithm. The optical tracker has an accuracy of about 0.3 mm at its centroid, which allows the endoscope tip to be localized to within 1.0 mm. The mapping operation allows multiple endoscopic images to be "painted" onto the 3-D brain surfaces, as they are acquired, in the correct anatomical position. This allows panoramic and stereoscopic visualization, as well as navigation of the 3-D surface, painted with multiple endoscopic views, from arbitrary perspectives.     

Modelling and evaluation of surgical performance using hidden Markov models

Minimally invasive surgery has become very widespread in the last ten years. Since surgeons experience difficulties in learning and mastering minimally invasive techniques, the development of training methods is of great importance. While the introduction of virtual reality-based simulators has introduced a new paradigm in surgical training, skill evaluation methods are far from being objective. This paper proposes a method for defining a model of surgical expertise and an objective metric to evaluate performance in laparoscopic surgery. Our approach is based on the processing of kinematic data describing movements of surgical instruments. We use hidden Markov model theory to define an expert model that describes expert surgical gesture. The model is trained on kinematic data related to exercises performed on a surgical simulator by experienced surgeons. Subsequently, we use this expert model as a reference model in the definition of an objective metric to evaluate performance of surgeons with different abilities. Preliminary results show that, using different topologies for the expert model, the method can be efficiently used both for the discrimination between experienced and novice surgeons, and for the quantitative assessment of surgical ability.     

Catheter Kinematics for Intracardiac Navigation

Steerable catheters are utilized frequently in minimally invasive cardiac interventions. Despite their extensive applications, the properties of the steerable section of such devices have not been thoroughly investigated. In this paper, the kinematics of the distal shaft of the catheters is modeled, and the catheter's reachable workspace and its singular configurations are studied. The modeling is empirically validated through experiments with actual catheters mounted on a specialized robot. The statistical analysis of the experiments verify the effectiveness of the proposed model in estimating the catheter's tip position. In the experiments, the modeling error does not exceed 2.66 plusmn 1.96 mm, and the mean absolute error in position coordinates is less than 1.55 mm. In addition, the linear relationship between the model and the measured position vectors is demonstrated, and the significance of the modeling goodness of fit is established. Based on the precision and computational effectiveness of the method, the applicability of the modeling to control and simulation purposes is postulated.     

Adhesive,Flexible, and Robust Polysaccharide Nanosheets Integrated for Tissue‐Defect Repair

Recent developments in nanotechnology have led to a method for producing free‐standing polymer nanosheets as a macromolecular organization. Compared with bulk films, the large aspect ratio of such nanosheets leads to unique physical properties, such as transparency, noncovalent adhesion, and high flexibility. Here, a biomedical application of polymer nanosheets consisting of biocompatible and biodegradable polysaccharides is reported. Micro‐scratch and bulge tests indicate that the nanosheets with a thickness of tens of nanometers have sufficient physical adhesiveness and mechanical strength for clinical use. A nanosheet of 75 nm thickness, a critical load of 9.1 × 10⁴ N m^?1, and an elastic modulus of 9.6 GPa is used for the minimally invasive repair of a visceral pleural defect in beagle dogs without any pleural adhesion caused by wound repair. For the first time, clinical benefits of sheet‐type nano‐biomaterials based on molecular organization are demonstrated, suggesting that novel therapeutic tools for overlapping tissue wounds will be possible without the need for conventional surgical interventions.     

Videoendoscopic distortion correction and its application to virtual guidance of endoscopy

Modern video-based endoscopes offer physicians a wide-angle field of view (FOV) for minimally invasive procedures. Unfortunately, inherent barrel distortion prevents accurate perception of range. This makes measurement and distance judgment difficult and causes difficulties in emerging applications, such as virtual guidance of endoscopic procedures. Such distortion also arises in other wide FOV camera circumstances. This paper presents a distortion-correction technique that can automatically calculate correction parameters, without precise knowledge of horizontal and vertical orientation. The method is applicable to any camera-distortion correction situation. Based on a least-squares estimation, our proposed algorithm considers line fits in both FOV directions and gives a globally consistent set of expansion coefficients and an optimal image center. The method is insensitive to the initial orientation of the endoscope and provides more exhaustive FOV correction than previously proposed algorithms. The distortion-correction procedure is demonstrated for endoscopic video images of a calibration test pattern, a rubber bronchial training device, and real human circumstances. The distortion correction is also shown as a necessary component of an image-guided virtual-endoscopy system that matches endoscope images to corresponding rendered three-dimensional computed tomography views.     

Implicit reconstruction of vasculatures using bivariate piecewise algebraic splines

Vasculature geometry reconstruction from volumetric medical data is a crucial task in the development of computer guided minimally invasive vascular surgery systems. In this paper, a technique for reconstructing the geometry of vasculatures using bivariate implicit splines is developed. With the proposed technique, an implicit geometry representation of the vascular tree can be accurately constructed based on the voxels extracted directly from the surface of a certain vascular structure in a given volumetric medical dataset. Experimental results show that the geometric representation built using our method can faithfully represent the morphology and topology of vascular structures. In addition, both the qualitative and the quantitative validations have been performed to show that the reconstructed vessel geometry is of high accuracy and smoothness. An virtual angioscopy system has been implemented to indicate one of the strengths of our proposed method.     

一种基于动态识别邻域的免疫网络分类算法及其性能分析

针对传统免疫网络分类算法在记忆细胞确定上缺乏有效的指导,该文提出一种基于动态识别邻域的免疫网络分类算法。算法采用核函数表示机制来描述抗体-抗原之间的亲和度;利用抗原对构造动态识别邻域来指导抗体群体的进化,并选择邻域中距离对偶抗原最近的抗体为记忆细胞。算法被应用于多分类问题及高维分类问题来进行算法性能分析,同时,算法被应用于多个标准数据集的分类来评估算法的整体性能。分类结果表明该算法对于标准测试数据集有良好的分类性能,这说明基于动态识别邻域的训练方法能够有效地指导记忆细胞的生成,显著地改善分类器的性能。     

Predicting target displacements using ultrasound elastography and finite element modeling

Soft tissue displacements during minimally invasive surgical procedures may cause target motion and subsequent misplacement of the surgical tool. A technique is presented to predict target displacements using a combination of ultrasound elastography and finite element (FE) modeling. A cubic gelatin/agar phantom with stiff targets was manufactured to obtain pre- and post-loading ultrasound radio frequency (RF) data from a linear array transducer. The RF data were used to compute displacement and strain images, from which the distribution of elasticity was reconstructed using an inverse FE-based approach. The FE model was subsequently used to predict target displacements upon application of different boundary and loading conditions to the phantom. The influence of geometry was investigated by application of the technique to a breast-shaped phantom. The distribution of elasticity in the phantoms as determined from the strain distribution agreed well with results from mechanical testing. Upon application of different boundary and loading conditions to the cubic phantom, the FE model-predicted target motion were consistent with ultrasound measurements. The FE-based approach could also accurately predict the displacement of the target upon compression and indentation of the breast-shaped phantom. This study provides experimental evidence that organ geometry and boundary conditions surrounding the organ are important factors influencing target motion. In future work, the technique presented in this paper could be used for preoperative planning of minimally invasive surgical interventions.     

基于光纤光栅的软体机器人末端力测量方法研究

手术机器人的末端操作力测量是实现机器人精准控制的关键,对保证手术操作的安全性至关重要。文中针对微创手术软体机器人末端三维力测量的实际需求,提出一种基于光纤光栅的微创软体机器人末端三维力的测量方法。基于光纤光栅传感原理,分析光纤传感器植入在软体机器人中的传感特性,建立基于最小二乘法线性标定和基于伯恩斯坦多项式非线性补偿的软体机器人末端力解耦模型,研究光纤光栅中心波长漂移量和软体机器人末端三维力之间的关系。并通过实验测试和对比分析验证了基于线性标定和非线性解耦算法的光纤传感软体机器人末端力测量性能研究结果表明:光纤光栅传感的可重复性平均为1.5 pm,末端力在XYZ三个方向上的测量精度误差均低于满量程的5%,且残差分布大部分集中在可靠区间,具有良好的重复性。所提出的基于光纤光栅的软体机器人末端力解耦算法为微创手术软体机器人的末端力精确测量提供了有效的方法,在生物医学等软体机器人的末端力测量中具有应用前景。     

An Optimal Sliding Choke Antenna for Hepatic Microwave Ablation

Microwave ablation (MWA) is a minimally invasive technique increasingly used for thermal therapy of liver tumors. Effective MWA requires efficient interstitial antennas that destroy tumors and a margin of healthy tissue, in situ, while minimizing damage to the rest of the organ. Previously, we presented a method for optimizing MWA antenna designs by coupling finite element method models of antennas with a real-coded, multiobjective genetic algorithm. We utilized this procedure to optimize the design of a minimally invasive choke antenna that can be used to create near-spherical ablation zones of adjustable size (radius 1-2 cm) by adjusting treatment durations and a sliding structure of the antenna. Computational results were validated with experiments in ex vivo bovine liver. The optimization procedure yielded antennas with reflection coefficients below -30 dB, which were capable of creating spherical ablation zones up to 2 cm in radius using 100 W input power at 2.45 GHz with treatment durations under 2 min.     

Elastic registration for retinal images based on reconstructed vascular trees

The vascular tree of the retina is likely the most representative and stable feature for eye fundus images in registration. Based on the reconstructed vascular tree, we propose an elastic matching algorithm to register pairs of fundus images. The identified vessels are thinned and approximated using short line segments of equal length that results a set of elements. The set of elements corresponding to one vascular tree are elastically deformed to optimally match the set of elements of another vascular tree, with the guide of an energy function to finally establish pixel relationship between both vascular trees. The mapped positions of pixels in the transformed retinal image are computed to be the sum of their original locations and corresponding displacement vectors. For the purpose of performance comparison, a weak affine model based fast chamfer matching technique is proposed and implemented. Experiment results validated the effectiveness of the elastic matching algorithm and its advantage over the weak affine model for registration of retinal fundus images.     

基于训练序列的OFDM系统定时同步改进算法

本文从OFDM系统模型和同步技术作为切入点重点分析研究了基于训练序列的OFDM系统定时同步算法,针对基于SC算法构造的训练序列帧结构及所采用的定时估计算法会造成定时同步位置具有峰值平台、定位点模糊、定时同步不够精确的缺点,提出了一种基于SC算法的改进型训练序列帧结构及改进算法。该算法对SC算法中训练序列前后两部分的搬移重复结构进行改进,构造了前后两部分呈中心对称的训练序列帧结构。改进算法不再采用SC算法中训练序列的后半部分来定义能量函数,而是采用整个训练序列长度来定义能量函数,从而构建同步度量函数,找到最佳定时同步估计点并完成定时同步。理论分析和仿真结果表明,改进算法解决了SC算法定时同步估计位置模糊、定时同步不精确的问题,改进后的算法能够确保定时同步的精确性。      

Guiding the surgical gesture using an electro-tactile stimulus array on the tongue: a feasibility study

Under conventional "open-" surgery, the physician has to take care of the patient, interact with other clinicians and check several monitoring devices. Nowadays, the computer assisted surgery proposes to integrate 3-D cameras in the operating theatre in order to assist the surgeon in performing minimally invasive surgical punctures. The cameras localize the needle and the computer guides the surgeon towards an intracorporeal clinically defined target. A visualization system (screen) is employed to provide the surgeon with indirect visual spatial information about the intracorporeal positions of the needle. The present work proposes to use another sensory modality to guide the surgeon, thus keeping the visual modality fully dedicated to the surgical gesture. For this, the sensory substitution paradigm using the Bach-y-Rita's "Tongue Display Unit" (TDU) is exploited to provide to the surgeon information of the position tool. The TDU device is composed of a 6 x 6 matrix of electrodes transmitting electrotactile information on the tongue surface. The underlying idea consists in transmitting information about the deviation of the needle movement with regard to a preplanned "optimal" trajectory. We present an experiment assessing the guidance effectiveness of an intracorporeal puncture under TDU guidance with respect to the performance evidenced under a usual visual guidance system.     

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly.     

Towards a real-time minimally-invasive vascular intervention simulation system

Recently, foundations rooted in physics have been laid down for the goal of simulating the propagation of a guide wire inside the vasculature. At the heart of the simulation lies the fundamental task of energy minimization. The energy comes from interaction with the vessel wall and the bending of the guide wire. For the simulation to be useful in actual training, obtaining the smallest possible optimization time is key. In this paper, we, therefore, study the influence of using different optimization techniques: a semianalytical approximation algorithm, the conjugate-gradients algorithm, and an evolutionary algorithm (EA), specifically the GLIDE algorithm. Simulation performance has been measured on phantom data. The results show that a substantial reduction in time can be obtained while the error is increased only slightly if conjugate gradients or GLIDE is used.     

A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use.

Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.     

A Comparison of a Similarity-Based and a Feature-Based 2-D–3-D Registration Method for Neurointerventional Use

Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.