Quantitative coronary angiography with deformable spline models

Angiographic assessment of coronary stenoses: a review of the techniques

This article reviews the fundamental techniques to quantify the physiological severity of (coronary) stenoses. Although a wide survey of different techniques and applications is provided, the focus of this review is on: 1) the assessment of the immediate effect of the stenoses on blood flow (i.e., the hemodynamic severity), and not on the assessment of the pathology of the vessel itself; 2) the flow reserve methods to defining the physiological severity of stenoses; and 3) the determination of blood flow and tissue perfusion by X-ray angiography (a short survey of other imaging modalities is provided as well). Although the practical implementation of the techniques is illustrated by applying them to coronary stenoses, most of the issues involved are of interest in other application areas (using other imaging modalities) as well. This review consists of four parts. The first part deals with the definition of stenoses severity; the second part with tracer kinetic theory necessary to determine flows by imaging; the third part focusses on (cardiac) imaging modalities, with an emphasis on X-ray angiography; and the last part illustrates the practical implementation of the techniques in cardiology.

A MAP framework for tag line detection in SPAMM data using Markov random fields on the B-spline solid

Magnetic resonance (MR) tagging is a technique for measuring heart deformations through creation of a stripe grid pattern on cardiac images. In this paper, we present a maximum a posteriori (MAP) framework for detecting tag lines using a Markov random field (MRF) defined on the lattice generated by three-dimensional (3-D) and four-dimensional (4-D) (3-D + t) uniform sampling of B-spline models. In the 3-D case, MAP estimation is cast for detecting present tag features in the current image given an initial solid from the previous frame (the initial undeformed solid is manually positioned by clicking on corner points of a cube). The method also allows the parameters of the solid model, including the number of knots and the spline order, to be adjusted within the same framework. Fitting can start with a solid with less knots and lower spline order and proceed to one with more knots and/or higher order so as to achieve more accuracy and/or higher order of smoothness. In the 4-D case, the initial model is considered to be the linear interpolation of a sequence of optimal solids obtained from 3-D tracking. The same framework proposed for the 3-D case can once again be applied to arrive at a 4-D B-spline model with a higher temporal order.

Quantification of spinal cord atrophy from magnetic resonance images via a B-spline active surface model

A method is presented that aims at segmenting and measuring the surface of the spinal cord from MR images in order to detect and quantify atrophy. A semiautomatic segmentation with very little intervention from an operator is proposed. It is based on the optimization of a B-spline active surface. The method allows for the computation of orthogonal cross-sections at any level along the cord, from which measurements are derived, such as cross-sectional area or curvature. An evaluation of the accuracy and reproducibility of the method is presented.

Automatic axis generation for virtual bronchoscopic assessment of major airway obstructions

Virtual bronchoscopy (VB) has emerged as a paradigm for more effective 3D CT image evaluation. Systematic evaluation of a 3D CT chest image using VB techniques, however, requires precomputed guidance data. This guidance data takes the form of central axes, or centerlines, through the major airways. We propose an axes-generation algorithm for VB assessment of 3D CT chest images. For a typical high-resolution 3D CT chest image, the algorithm produces a series of airway-tree axes, corresponding airway cross-sectional area measurements, and a segmented airway tree in a few minutes on a standard PC. Results for phantom and human airway-obstruction cases demonstrate the efficacy of the algorithm. Also, the algorithm is demonstrated in the context of VB-based 3D CT assessment.

Modeling the 3D coronary tree for labeling purposes

Coronary artery diseases are usually revealed using X-ray angiographies. Such images are complex to analyze because they provide a 2D projection of a 3D object. Medical diagnosis suffers from inter- and intra-clinician variability. Therefore, reliable software for the 3D reconstruction and labeling of the coronary tree is strongly desired. It requires the matching of the vessels in the different available angiograms, and an approach which identifies the arteries by their anatomical names is a way to solve this difficult problem. This paper focuses on the automatic labeling of the left coronary tree in X-ray angiography. Our approach is based on a 3D topological model, built from the 3D anthropomorphic phantom, Coronix. The phantom is projected under different angles of view to provide a data base of 2D topological models. On the other hand, the vessel skeleton is extracted from the patient's angiogram. The algorithm compares the skeleton with the 2D topological model which has the most similar vascular net shape. The method performs in a hierarchical manner, first labeling the main artery, then the sub-branches. It handles inter-individual anatomical variations, segmentation errors and image ambiguities. We tested the method on standard angiograms of Coronix and on clinical examinations of nine patients. We demonstrated successful scores of 90% correct labeling for the main arteries and 60% for the sub-branches. The method appears to be particularly efficient for the arteries in focus. It is therefore a very promising tool for the automatic 3D reconstruction of the coronary tree from monoplane temporal angiographic clinical sequences.

In vivo finite element model-based image analysis of pacemaker lead mechanics

BACKGROUND

Fractures of implanted pacemaker leads are currently identified by inspecting radiographic images without making full use of a priori known material and structural information. Moreover, lead designers are unable to incorporate clinical image data into analyses of lead mechanics.

METHODS

A novel finite element/active contour method was developed to quantify the in vivo mechanics of implanted leads by estimating the distributions of stress, strain, and traction using biplane videoradiographic images. The nonlinear equilibrium equations governing a thin elastic beam undergoing 3-D large rotation were solved using one-dimensional isoparametric finite elements. External forces based on local image greyscale values were computed from each pair of images using a perspective transformation governing the relationship between the image planes.

RESULTS

Cantilever beam forward solution results were within 0.2% of the analytic solution for a wide range of applied loads. The finite element/active contour model was able to reproduce the principal curvatures of a synthetic helix within 3% of the analytic solution and estimates of the helix's geometric torsion were within 20% of the analytic solution. Applying the method to biplane videoradiographic images of a lead acutely implanted in an anesthetized dog resulted in expected variations in curvature and bending stress between compliant and rigid segments of the lead.

CONCLUSIONS

By incorporating knowledge about lead geometric and material properties, the 3-D finite element/active contour method regularizes the image reconstruction problem and allows for more quantitative and automatic assessment of implanted lead mechanics.

Evaluation of center-line extraction algorithms in quantitative coronary angiography

Objective testing of centerline extraction accuracy in quantitative coronary angiography (QCA) algorithms is a very difficult task. Standard tools for this task are not yet available. We present a simulation tool that generates synthetic angiographic images of a single coronary artery with predetermined centerline and diameter function. This simulation tool was used creating a library of images for the objective comparison and evaluation of QCA algorithms. This technique also provides the means for understanding the relationship between the algorithms' performance and limitations and the vessel's geometrical parameters. In this paper, two algorithms are evaluated and the results are presented.

Fast LV motion estimation using subspace approximation techniques

Cardiac motion estimation is very important in understanding cardiac dynamics and in noninvasive diagnosis of heart disease. Magnetic resonance (MR) imaging tagging is a technique for measuring heart deformations. In cardiac tagged MR images, a set of dark lines are noninvasively encoded within myocardial tissue providing the means for measurement of deformations of the heart. The points along tag lines measured in different frames and in different directions carry important information for determining the three-dimensional nonrigid movement of left ventricle. However, these measurements are sparse and, therefore, multidimensional interpolation techniques are needed to reconstruct a dense displacement field. In this paper, a novel subspace approximation technique is used to accomplish this task. We formulate the displacement estimation as a variational problem and then project the solution into spline subspaces. Efficient numerical methods are derived by taking advantages of B-spline properties. The proposed technique significantly improves our previous results reported in [3] with respect to computational time. The method is applied to a temporal sequence of two-dimensional images and is validated with simulated and in vivo heart data.

Vessel extraction in medical images by wave-propagation and traceback

This paper presents an approach for the extraction of vasculature from angiography images by using a wave propagation and traceback mechanism. We discuss both the theory and the implementation of the approach. Using a dual-sigmoidal filter, we label each pixel in an angiogram with the likelihood that it is within a vessel. Representing the reciprocal of this likelihood image as an array of refractive indexes, we propagate a digital wave through the image from the base of the vascular tree. This wave "washes" over the vasculature, ignoring local noise perturbations. The extraction of the vasculature becomes that of tracing the wave along the local normals to the waveform. While the approach is inherently single instruction stream multiple data stream (SIMD), we present an efficient sequential algorithm for the wave propagation and discuss the traceback algorithm. We demonstrate the effectiveness of our integer image neighborhood-based algorithm and its robustness to image noise.

Tag surface reconstruction and tracking of myocardial beads from SPAMM-MRI with parametric B-spline surfaces

Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization, and create tag planes intersecting image slices. The resulting grid of signal voids allows for tracking deformations of tissues in otherwise homogeneous-signal myocardial regions. In this paper, we propose a specific spatial modulation of magnetization (SPAMM) imaging protocol together with efficient techniques for measurement of three-dimensional (3-D) motion of material points of the human heart (referred to as myocardial beads) from images collected with the SPAMM method. The techniques make use of tagged images in orthogonal views by explicitly reconstructing 3-D B-spline surface representation of tag planes (tag planes in two orthogonal orientations intersecting the short-axis (SA) image slices and tag planes in an orientation orthogonal to the short-axis tag planes intersecting long-axis (LA) image slices). The developed methods allow for viewing deformations of 3-D tag surfaces, spatial correspondence of long-axis and short-axis image slice and tag positions, as well as nonrigid movement of myocardial beads as a function of time.

Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account

Segmentation of the skull in medical imagery is an important stage in applications that require the construction of realistic models of the head. Such models are used, for example, to simulate the behavior of electro-magnetic fields in the head and to model the electrical activity of the cortex in EEG and MEG data. In this paper, we present a new approach for segmenting regions of bone in MRI volumes using deformable models. Our method takes into account the partial volume effects that occur with MRI data, thus permitting a precise segmentation of these bone regions. At each iteration of the propagation of the model, partial volume is estimated in a narrow band around the deformable model. Our segmentation method begins with a pre-segmentation stage, in which a preliminary segmentation of the skull is constructed using a region-growing method. The surface that bounds the pre-segmented skull region offers an automatic 3D initialization of the deformable model. This surface is then propagated (in 3D) in the direction of its normal. This propagation is achieved using level set method, thus permitting changes to occur in the topology of the surface as it evolves, an essential capability for our problem. The speed at which the surface evolves is a function of the estimated partial volume. This provides a sub-voxel accuracy in the resulting segmentation.

A new model-based technique for enhanced small-vessel measurements in X-ray ciné-angiograms

Arterial diameter estimation from X-ray ciné angiograms is important for quantifying coronary artery disease (CAD) and for evaluating therapy. However, diameter measurement in vessel cross sections < or =1.0 mm is associated with large measurement errors. We present a novel diameter estimator which reduces both magnitude and variability of measurement error. We use a parametric nonlinear imaging model for X-ray ciné angiography and estimate unknown model parameters directly from the image data. Our technique allows us to exploit additional diameter information contained within the intensity profile amplitude, a feature which is overlooked by existing methods. This method uses a two-step procedure: the first step estimates the imaging model parameters directly from the angiographic frame and the second step uses these measurements to estimate the diameter of vessels in the same image. In Monte-Carlo simulation over a range of imaging conditions, our approach consistently produced lower estimation error and variability than conventional methods. With actual X-ray images, our estimator is also better than existing methods for the diameters examined (0.4-4.0 mm). These improvements are most significant in the range of narrow vessel widths associated with severe coronary artery disease.

Unsupervised contour representation and estimation using B-splines and a minimum description length criterion

This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach.

Model-based quantitation of 3-D magnetic resonance angiographic images

Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA's) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments directly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accurately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.

Model-based morphological segmentation and labeling of coronary angiograms

A method for extraction and labeling of the coronary arterial tree (CAT) using minimal user supervision in single-view angiograms is proposed. The CAT structural description (skeleton and borders) is produced, along with quantitative information for the artery dimensions and assignment of coded labels, based on a given coronary artery model represented by a graph. The stages of the method are: 1) CAT tracking and detection; 2) artery skeleton and border estimation; 3) feature graph creation; and iv) artery labeling by graph matching. The approximate CAT centerline and borders are extracted by recursive tracking based on circular template analysis. The accurate skeleton and borders of each CAT segment are computed, based on morphological homotopy modification and watershed transform. The approximate centerline and borders are used for constructing the artery segment enclosing area (ASEA), where the defined skeleton and border curves are considered as markers. Using the marked ASEA, an artery gradient image is constructed where all the ASEA pixels (except the skeleton ones) are assigned the gradient magnitude of the original image. The artery gradient image markers are imposed as its unique regional minima by the homotopy modification method, the watershed transform is used for extracting the artery segment borders, and the feature graph is updated. Finally, given the created feature graph and the known model graph, a graph matching algorithm assigns the appropriate labels to the extracted CAT using weighted maximal cliques on the association graph corresponding to the two given graphs. Experimental results using clinical digitized coronary angiograms are presented.

Automatic extraction and tracking of the tongue contours

Computerized analysis of the tongue surface movement can provide valuable information to speech and swallowing research. Ultrasound technology is currently the most attractive modality for the tongue imaging mainly because of its high video frame rate. However, problems with ultrasound imaging, such as noise and echo artifacts, refractions, and unrelated reflections pose significant challenges for computer analysis of the tongue images and hence specific methods must be developed. This paper presents a system that is developed for automatic extraction and tracking of the tongue surface movements from ultrasound image sequences. The ultrasound images are supplied by the head and transducer support system (HATS), which was developed in order to fix the head and support the transducer under the chin in a known position without disturbing speech. In this work, we propose a novel scheme for the analysis of the tongue images using deformable contours. We incorporate novel mechanisms to 1) impose speech related constraints on the deformations; 2) perform spatiotemporal smoothing using a contour postprocessing stage; 3) utilize optical flow techniques to speed up the search process; and 4) propagate user supplied information to the analysis of all image frames. We tested the system's performance qualitatively and quantitatively in consultation with speech scientists. Our system produced contours that are within the range of manual measurement variations. The results of our system are extremely encouraging and the system can be used in practical speech and swallowing research in the field of otolaryngology.

Mapping the human retina

The new therapeutic method of scotoma-based photocoagulation (SBP) developed at the Vienna Eye Clinic for diagnosis and treatment of age-related macular degeneration requires retinal maps from scanning laser ophthalmoscope images. This paper describes in detail all necessary image analysis steps for map generation. A prototype software system for fully automatic map generation has been implemented and tested on a representative dataset selected from a clinical study with 50 patients. The map required for the SBP treatment can be reliably extracted in all cases. Thus, algorithms presented in this paper should be directly applicable in daily clinical routine without major modifications.

Coupled B-snake grids and constrained thin-plate splines for analysis of 2-D tissue deformations from tagged MRI

Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization and create tagged patterns within a deforming body such as the heart muscle. The resulting patterns define a time-varying curvilinear coordinate system on the tissue, which we track with coupled B-snake grids. B-spline bases provide local control of shape, compact representation, and parametric continuity. Efficient spline warps are proposed which warp an area in the plane such that two embedded snake grids obtained from two tagged frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the plane, where no information is available, a C1 continuous vector field is interpolated. The implementation proposed in this paper improves on our previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The methods are validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.