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.     

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.     

Modeling friction, intrinsic curvature, and rotation of guide wires for simulation of minimally invasive vascular interventions

Obtaining the expertise to perform minimally invasive vascular interventions requires thorough training. In this paper, an algorithm for simulating minimally invasive vascular interventions for training purposes is presented and evaluated. The algorithm enables the simulation of completely straight guide wires as well as intrinsically curved ones based on applied translations and rotations. Friction between the guide wire and the vasculature is incorporated in the model. Quantitative validation is performed by comparing the simulated guide-wire position with the actual position as assessed by 3-D rotational X-ray imaging in physical experiments on a variety of vascular phantoms that truthfully represent human anatomy. The results show that for proper settings of the model's parameters, accurate simulations of guide-wire motion can be obtained, with an average precision of the guide-wire position of around 1.0 mm.     

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

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

Three-dimensional motion tracking of coronary arteries in biplane cineangiograms

A three-dimensional (3-D) method for tracking the coronary arteries through a temporal sequence of biplane X-ray angiography images is presented. A 3-D centerline model of the coronary vasculature is reconstructed from a biplane image pair at one time frame, and its motion is tracked using a coarse-to-fine hierarchy of motion models. Three-dimensional constraints on the length of the arteries and on the spatial regularity of the motion field are used to overcome limitations of classical two-dimensional vessel tracking methods, such as tracking vessels through projective occlusions. This algorithm was clinically validated in five patients by tracking the motion of the left coronary tree over one cardiac cycle. The root mean square reprojection errors were found to be submillimeter in 93% (54/58) of the image pairs. The performance of the tracking algorithm was quantified in three dimensions using a deforming vascular phantom. RMS 3-D distance errors were computed between centerline models tracked in the X-ray images and gold-standard centerline models of the phantom generated from a gated 3-D magnetic resonance image acquisition. The mean error was 0.69 (+/- 0.06) mm over eight temporal phases and four different biplane orientations.     

Geometrically correct 3-D reconstruction of intravascularultrasound images by fusion with biplane angiography-methods andvalidation

In the rapidly evolving field of intravascular ultrasound (IVUS), the assessment of vessel morphology still lacks a geometrically correct three-dimensional (3-D) reconstruction. The IVUS frames are usually stacked up to form a straight vessel, neglecting curvature and the axial twisting of the catheter during the pullback. Our method combines the information about vessel cross-sections obtained from IVUS with the information about the vessel geometry derived from biplane angiography. First, the catheter path is reconstructed from its biplane projections, resulting in a spatial model. The locations of the IVUS frames are determined and their orientations relative to each other are calculated using a discrete approximation of the Frenet-Serret formulas known from differential geometry. The absolute orientation of the frame set is established, utilizing the imaging catheter itself as an artificial landmark. The IVUS images are segmented, using our previously developed algorithm. The fusion approach has been extensively validated in computer simulations, phantoms, and cadaveric pig hearts.     

Predictive (un)distortion model and 3-D reconstruction by biplane snakes

This paper is concerned with the three-dimensional (3-D) reconstruction of coronary vessel centerlines and with how distortion of X-ray angiographic images affects it. Angiographies suffer from pincushion and other geometrical distortions, caused by the peripheral concavity of the image intensifier (II) and the nonlinearity of electronic acquisition devices. In routine clinical practice, where a field-of-view (FOV) of 17-23 cm is commonly used for the acquisition of coronary vessels, this distortion introduces a positional error of up to 7 pixels for an image matrix size of 512 x 512 and an FOV of 17 cm. This error increases with the size of the FOV. Geometrical distortions have a significant effect on the validity of the 3-D reconstruction of vessels from these images. We show how this effect can be reduced by integrating a predictive model of (un)distortion into the biplane snakes formulation for 3-D reconstruction. First, we prove that the distortion can be accurately modeled using a polynomial for each view. Also, we show that the estimated polynomial is independent of focal length, but not of changes in anatomical angles, as the II is influenced by the earth's magnetic field. Thus, we decompose the polynomial into two components: the steady and the orientation-dependent component. We determine the optimal polynomial degree for each component, which is empirically determined to be five for the steady component and three for the orientation-dependent component. This fact simplifies the prediction of the orientation-dependent polynomial, since the number of polynomial coefficients to be predicted is lower. The integration of this model into the biplane snakes formulation enables us to avoid image unwarping, which deteriorates image quality and therefore complicates vessel centerline feature extraction. Moreover, we improve the biplane snake behavior when dealing with wavy vessels, by means of using generalized gradient vector flow. Our experiments show that the proposed methods in this paper decrease up to 88% the reconstruction error obtained when geometrical distortion effects are ignored. Tests on imaged phantoms and real cardiac images are presented as well.     

Three-dimensional trajectory assessment of an IVUS transducer from single-plane cineangiograms: a phantom study

The recovery of the three-dimensional (3-D) path of the transducer used during an intravascular ultrasound (IVUS) examination is of primary importance to assess the exact 3-D shape of the vessel under study. Traditionally, the reconstruction is done by simply stacking the images during the pullback, or more recently using biplane angiography to recover the vessel curvature. In this paper, we explain, how single-plane angiography can be used with two projection models, to perform this task. Two types of projection geometry are analyzed: weak-perspective and full-perspective. In weak-perspective projection geometry, the catheter path can be reconstructed without prior transducer depth information. With full-perspective projection geometry, precise depth location of reference points are needed in order to minimize the error of the recovered transducer angle of incidence. The transducer angulation reconstruction is based on the foreshortening effect as seen from the X-ray images. By comparing the measured to the true transducer length, we are able to get its incidence angle. The transducer trajectory is reconstructed by stitching together the different estimated angulations obtained from each image in a cineangiogram sequence. The method is described and validated on two helical vessel phantoms, giving on average a reconstructed path that is less than 2 mm distant from the true path when using full-perspective projection.     

Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

The lungs exchange air with the external environment via the pulmonary airways. Computed tomography (CT) scanning can be used to obtain detailed images of the pulmonary anatomy, including the airways. These images have been used to measure airway geometry, study airway reactivity, and guide surgical interventions. Prior to these applications, airway segmentation can be used to identify the airway lumen in the CT images. Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming. We describe a fully automatic technique for segmenting the airway tree in three-dimensional (3-D) CT images of the thorax. We use grayscale morphological reconstruction to identify candidate airways on CT slices and then reconstruct a connected 3-D airway tree. After segmentation, we estimate airway branchpoints based on connectivity changes in the reconstructed tree. Compared to manual analysis on 3-mm-thick electron-beam CT images, the automatic approach has an overall airway branch detection sensitivity of approximately 73%.     

In vivo measurement of 3-D skeletal kinematics from sequences ofbiplane radiographs: Application to knee kinematics

Current noninvasive or minimally invasive methods for evaluating in vivo knee kinematics are inadequate for accurate determination of dynamic joint function due to limited accuracy and/or insufficient sampling rates. A three-dimensional (3-D) model-based method is presented to estimate skeletal motion of the knee from high-speed sequences of biplane radiographs. The method implicitly assumes that geometrical features cannot be detected reliably and an exact segmentation of bone edges is not always feasible. An existing biplane radiograph system was simulated as two separate single-plane radiograph systems. Position and orientation of the underlying bone was determined for each single-plane view by generating projections through a 3-D volumetric model (from computed tomography), and producing an image (digitally reconstructed radiograph) similar (based on texture information and rough edges of bone) to the two-dimensional radiographs. The absolute 3-D pose was determined using known imaging geometry of the biplane radiograph system and a 3-D line intersection method. Results were compared to data of known accuracy, obtained from a previously established bone-implanted marker method. Difference of controlled in vitro tests was on the order of 0.5 mm for translation and 1.4 degrees for rotation. A biplane radiograph sequence of a canine hindlimb during treadmill walking was used for in vivo testing, with differences on the order of 0.8 mm for translation and 2.5 degrees for rotation.     

Binary reconstruction of the heart chambers from biplane angiographic image sequences

The aim of this work is the three-dimensional (3-D) reconstruction of the left or right heart chamber from digital biplane angiograms. The approach used, the binary reconstruction, exploits the density information of subtracted ventriculograms from two orthogonal views in addition to the ventricular contours. The ambiguity of the problem is largely reduced by incorporating a priori knowledge of human ventricles. A model-based reconstruction program is described that is applicable to routinely acquired biplane ventriculographic studies. Prior to reconstruction, several geometric and densitometric imaging errors are corrected. The finding of corresponding density profiles and anatomical landmarks is supported by a biplane image pairing procedure that takes the movement of the gantry system into account. Absolute measurements are based on geometric isocenter calibration and a slice-wise density calibration technique. The reconstructed ventricles allow 3-D visualization and regional wall motion analysis independently of the gantry setting. The method is applied to clinical angiograms and tested in left- and right-ventricular phantoms yielding a well shape conformity even with few model information. The results indicate that volumes of binary reconstructed ventricles are less projection-dependent compared to volume data derived by purely contour-based methods. A limitation is that the heart chamber must not be superimposed by other dye-filled structures in both projections.     

Constrained registration for motion compensation in atrial fibrillation ablation procedures

Fluoroscopic overlay images rendered from preoperative volumetric data can provide additional anatomical details to guide physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are often compromised by cardiac and respiratory motion, motion compensation methods are needed to keep the overlay images in sync with the fluoroscopic images. So far, these approaches have either required simultaneous biplane imaging for 3-D motion compensation, or in case of monoplane X-ray imaging, provided only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose an approach that facilitates a full 3-D motion compensation even if only monoplane X-ray images are available. To this end, we use a training phase that employs a biplane sequence to establish a patient specific motion model. Afterwards, a constrained model-based 2-D/3-D registration method is used to track a circumferential mapping catheter. This device is commonly used for AFib catheter ablation procedures. Based on the experiments on real patient data, we found that our constrained monoplane 2-D/3-D registration outperformed the unconstrained counterpart and yielded an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 1.6 mm. The unconstrained 2-D/3-D registration technique yielded a similar 2-D performance, but the 3-D tracking error increased to 3.2 mm mostly due to wrongly estimated 3-D motion components in X-ray view direction. Compared to the conventional 2-D monoplane method, the proposed method provides a more seamless workflow by removing the need for catheter model re-initialization otherwise required when the C-arm view orientation changes. In addition, the proposed method can be straightforwardly combined with the previously introduced biplane motion compensation technique to obtain a good trade-off between accuracy and radiation dose reduction.     

A quantitative analysis of 3-D coronary modeling from two or more projection images

A method is introduced to examine the geometrical accuracy of the three-dimensional (3-D) representation of coronary arteries from multiple (two and more) calibrated two-dimensional (2-D) angiographic projections. When involving more then two projections, (multiprojection modeling) a novel procedure is presented that consists of fully automated centerline and width determination in all available projections based on the information provided by the semi-automated centerline detection in two initial calibrated projections. The accuracy of the 3-D coronary modeling approach is determined by a quantitative examination of the 3-D centerline point position and the 3-D cross sectional area of the reconstructed objects. The measurements are based on the analysis of calibrated phantom and calibrated coronary 2-D projection data. From this analysis a confidence region (alpha degrees approximately equal to [35 degrees - 145 degrees]) for the angular distance of two initial projection images is determined for which the modeling procedure is sufficiently accurate for the applied system. Within this angular border range the centerline position error is less then 0.8 mm, in terms of the Euclidean distance to a predefined ground truth. When involving more projections using our new procedure, experiments show that when the initial pair of projection images has an angular distance in the range alpha degrees approximately equal to [35 degrees - 145 degrees], the centerlines in all other projections (gamma = 0 degrees - 180 degrees) were indicated very precisely without any additional centering procedure. When involving additional projection images in the modeling procedure a more realistic shape of the structure can be provided. In case of the concave segment, however, the involvement of multiple projections does not necessarily provide a more realistic shape of the reconstructed structure.     

Reconstruction of vascular networks using three-dimensional models

Reconstructing vasculature in three dimensions is a challenging problem. Early approaches concentrated on coronary vasculature in X-ray images, recent work uses magnetic resonance imagery of cerebral vasculature. In both cases a priori information has been used, and often the way this is represented has proven limiting to the scope of applications supported. For example, a particular representation may be useful only for X-ray images. This paper addresses two issues: (1) representing a collection of vasculature and (2) the reconstruction of individual vasculature from images. The authors' representation learns the variations in branching structures and vessel shapes that occur between individuals. It supports a vascular catalogue containing three-dimensional (3-D) anatomical models. The representation is task independent: here the authors use it to reconstruct vasculature from images. Their algorithm has 4 features to which they draw attention: (1) it is not premised wholly upon X-ray images (though that is the authors' focus here); (2) it produces several feasible solutions rather than one; (3) it can generalize from the catalogue to reconstruct instances not yet learned; (4) it exhibits polynomial time complexity, reasonable memory consumption, and is reliable. Both the authors' representation and reconstruction algorithm are new and useful approaches. In support of these claims, they present results gathered from X-rays of both simulated and real vasculature     

Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models

In this paper, we present an original method for the three-dimensional (3-D) reconstruction of the scoliotic rib cage from a planar and a conventional pair of calibrated radiographic images (postero-anterior with normal incidence and lateral). To this end, we first present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers (PPCA). This method is based on the stochastic expectation maximization (SEM) algorithm. Parameters of this mixture model are used to constrain the 3-D biplanar reconstruction problem of scoliotic rib cage. More precisely, the proposed PPCA mixture model is exploited for dimensionality reduction and to obtain a set of probabilistic prior models associated with each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood, for each considered class-conditional prior model, the proposed 3-D reconstruction is stated as an energy function minimization problem, which is solved with an exploration/selection algorithm. The optimal 3-D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3-D method of reconstruction has been successfully tested and validated on a database of 20 pairs of biplanar radiographic images of scoliotic patients, yielding very promising results. As an alternative to computed tomography-scan 3-D reconstruction this scheme has the advantage of low radiation for the patient, and may also be used for diagnosis and evaluation of deformity of a scoliotic rib cage. The proposed method remains sufficiently general to be applied to other reconstruction problems for which a database of objects to be reconstructed is available (with two or more radiographic views).     

Urban object reconstruction using airborne laser elevation image and aerial image

Creating three-dimensional (3-D) models of real urban objects is an important goal in a wide variety of applications. This paper describes a method that utilizes airborne laser elevation images and aerial images for the 3-D reconstruction of urban objects. Our modeling approach uses the vertical geometric pattern analysis of elevation images. These patterns correspond to object contours and, thus, enable the extraction of the object. In addition, to provide realistic textured details, textures are cut from aerial images and mapped onto 3-D models. Our texture-mapping approach can avoid geometry mismatching and enable the automatic registration to determine the most reliable correspondence between projected outlines of 3-D models and contours of real objects shown in aerial images. Edge pairs, which are matched with projected outlines, are detected from aerial images. In order to minimize mismatching, we apply the voting technique based on the generalized Hough transform. Experimental results show that 3-D reconstruction of urban objects is generally successful.     

Optimal CT scanning plan for long-bone 3-D reconstruction

Digital computed tomographic (CT) data are widely used in three-dimensional (3-D) construction of bone geometry and density features for 3-D modelling purposes. During in vivo CT data acquisition the number of scans must be limited in order to protect patients from the risks related to X-ray absorption. The aim of this work is to automatically define, given a finite number of CT slices, the scanning plan which returns the optimal 3-D reconstruction of a bone segment from in vivo acquired CT images. An optimization algorithm based on a Discard-Insert-Exchange technique has been developed. In the proposed method the optimal scanning sequence is searched by minimizing the overall reconstruction error of a two-dimensional (2-D) prescanning image: an anterior-posterior (AP) X-ray projection of the bone segment. This approach has been validated in vitro on 3 different femurs. The 3-D reconstruction errors obtained through the optimization of the scanning plan on the 3-D prescanning images and on the corresponding 3-D data sets have been compared. 2-D and 3-D data sets have been reconstructed by linear interpolation along the longitudinal axis. Results show that direct 3-D optimization yields root mean square reconstruction errors which are only 4%-7% lower than the 2-D-optimized plan, thus proving that 2-D-optimization provides a good suboptimal scanning plan for 3-D reconstruction. Further on, 3-D reconstruction errors given by the optimized scanning plan and a standard radiological protocol for long bones have been compared. Results show that the optimized plan yields 20%-50% lower 3-D reconstruction errors     

A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine

This paper presents a new and accurate three-dimensional (3-D) reconstruction technique for the scoliotic spine from a pair of planar and conventional (postero-anterior with normal incidence and lateral) calibrated radiographic images. The proposed model uses a priori hierarchical global knowledge, both on the geometric structure of the whole spine and of each vertebra. More precisely, it relies on the specification of two 3-D statistical templates. The first, a rough geometric template on which rigid admissible deformations are defined, is used to ensure a crude registration of the whole spine. An accurate 3-D reconstruction is then performed for each vertebra by a second template on which nonlinear admissible global, as well as local deformations, are defined. Global deformations are modeled using a statistical modal analysis of the pathological deformations observed on a representative scoliotic vertebra population. Local deformations are represented by a first-order Markov process. This unsupervised coarse-to-fine 3-D reconstruction procedure leads to two separate minimization procedures efficiently solved in our application with evolutionary stochastic optimization algorithms. In this context, we compare the results obtained with a classical genetic algorithm (GA) and a recent Exploration Selection (ES) technique. This latter optimization method with the proposed 3-D reconstruction model, is tested on several pairs of biplanar radiographic images with scoliotic deformities. The experiments reported in this paper demonstrate that the discussed method is comparable in terms of accuracy with the classical computed-tomography-scan technique while being unsupervised and while requiring only two radiographic images and a lower amount of radiation for the patient.     

Intrabody three-dimensional position sensor for an ultrasound endoscope

To avoid or reduce the X-ray exposure in endoscopic examinations and therapy, as an alternative to the conventional two-dimensional X-ray fluoroscopy we are developing an intrabody navigation system that can directly measure and visualize the three-dimensional (3-D) position of the tip and the trace of an ultrasound endoscope. The proposed system can identify the 3-D location and direction of the endoscope probe inserted into the body to furnish endoscopic images. A marker transducer(s) placed on the surface of the body transmits ultrasound pulses, which are visualized as a marker synchronized to the scanning of the endoscope. The position (direction and distance of the marker transducer(s) outside the body relative to the scanning probe inside the body) of the marker is detected and measured in the scanned image of the ultrasound endoscope. Further, an optical localizer locates the marker transducer(s) with six degrees of freedom. Thus, the proposed method performs inside-body 3-D localization by utilizing the inherent image reconstruction function of the ultrasound endoscope, and is able to be used with currently available commercial ultrasound image scanners. The system may be envisaged as a kind of global positioning system for intrabody navigation.