Quantitative Analysis and Parametric Display of Regional Myocardial Mechanics

Transcription

1 Quantitative Analysis and Parametric Display of Regional Myocardial Mechanics Christian. D. Eusemann a,b, Matthias E. Bellemann b, and Richard A. Robb a a Mayo Foundation Rochester, MN USA b Fachhochschule Jena, Tatzendpromenade 1b Jena, Germany ABSTRACT Quantitative assessment of regional heart motion has significant potential for more accurate diagnosis of heart disease and / or cardiac irregularities. Local heart motion may be studied from medical imaging sequences. Using functional parametric mapping, regional myocardial motion during a cardiac cycle can be color mapped onto a deformable heart model to obtain better understanding of the structure-to-function relationships in the myocardium, including regional patterns of akinesis or diskinesis associated with ischemia or infarction. In this study, 3D reconstructions were obtained from the Dynamic Spatial Reconstructor 1,2 (DSR) at 15 time points throughout one cardiac cycle of pre-infarct and post-infarct hearts. Deformable models were created from the 3-D images for each time point of the cardiac cycles. From these polygonal models, regional excursions and velocities of each vertex representing a unit of myocardium were calculated for successive time intervals. The calculated results were visualized through model animations and / or specially formatted static images. The time point of regional maximum velocity and excursion of myocardium through the cardiac cycle was displayed using color mapping. The absolute value of regional maximum velocity and maximum excursion were displayed in a similar manner. Using animations, the local myocardial velocity changes were visualized as color changes on the cardiac surface during the cardiac cycle. Moreover, the magnitude and direction of motion for individual segments of myocardium could be displayed. Comparisons of these dynamic parametric displays suggest that the ability to encode quantitative functional information on dynamic cardiac anatomy enhances the diagnostic value of 4D images of the heart. Myocardial mechanics quantified this way adds a new dimension to the analysis of cardiac functional disease, including regional patterns of akinesis and diskinesis associated with ischemia and infarction. Similarly, disturbances in regional contractility and filling may be detected and evaluated using such measurements and displays. Keywords: Myocardial dynamics, Functional mapping, Heart motion analysis 1. INTRODUCTION Cardiac disease is the most common cause of death. Information about the structure and regional mechanical behavior of the myocardium is important for accurate diagnosis and effective treatment of heart disease. Improvements in medical imaging technology provide rich opportunities for extraction of these clinically useful parameters. Previous studies have used 3D data sets acquired from MRI, especially MRI tagging 3-6, Ultrasound 7 and DSR 8-12 for the assessment of dynamic heart motion. MRI tagging allows unambiguous tracking of exact myocardial segments. It is the gold standard for muscle segment motion tracking, and it provides effective assessment of heart motion, but it is limited by the cost and time intensive MRI modality. This paper describes a new method to measure and visualize myocardial structure and function using a fast, modality independent algorithm applied to both normal and infarcted hearts in a controlled study. The algorithm is based on deformable surface reconstruction, involving creation of a polygonal surface mesh to represent the myocardial walls. The initial surface mesh represents the spatial position of each individual surface element of the cardiac walls. This triangular mesh deforms to fit successive cardiac volumes. The method forces the mesh at each time point volume to have the same

2 number of vertices and triangles. Through this process regional excursions and velocities of each vertex representing the same unit of myocardium can be calculated for each time -point interval. The results can be visualized through comprehensive static displays and model animations. Regional maximum velocity and excursion of myocardium through the cardiac cycle can be displayed on the dynamic model using color mapping. The absolute value of regional maximum velocity and excursion of myocardium during the cardiac cycle can be similarly displayed. Animations provide visualization of the local myocardial velocity changes and trajectories of myocardial surface points along specific pathways. To verify the algorithm, a data set including volume image reconstructions of a cardiac cycle of a canine heart prior and post an occlusion of an epicardial coronary artery was processed. Using such visualization methods, quantitative functional information is encoded on dynamic beating heart anatomy, providing a parametric display, which significantly enhances the diagnostic value of 4D images of the heart. The ability to see the motion of the heart walls combined with local myocardial excursion and velocity maps has potential to improve diagnosis of myocardial abnormalities, including regional ischemia, infarction, anomalous filling defects and electrophysiologic disturbances in regional contraction patterns. 1. METHODS 2.1 Image Acquisition For 4-D quantitative visualization of myocardial dynamics, 3-D reconstructions over time are necessary. Myocardial reconstructions were obtained from the DSR 1,2 at the Mayo Clinic. The DSR is a high-resolution three-dimensional imaging scanner, which operates on the principles of computerized tomography (CT). To achieve high speed, 14 equispaced x-ray tubes and imaging cameras are mounted on a rotating gantry. Accurate stop-action 3-D images of moving organs can be achieved with the DSR. 3-D image data sets used for this study were obtained from DSR scans of intact canine hearts and contain fifteen time points each throughout one complete cardiac cycle with a heart rate of 60 beats/min. The first data is a healthy canine heart. Each time point of this volume image reconstruction included 110 adjacent slices with a spatial resolution of mm 3 /voxel. The second data set is a canine heart before and after an infarct caused by occluding in the left anterior descending coronary artery (LAD). These data sets included 115 adjacent slices with a spatial resolution of 0.75 mm 3 /voxel. The data sets span one complete cardiac cycle, beginning and ending at end diastole. Scans used circulatory contrast agents to enhance the visibility of chambers and vessels. 2.2 Image Segmentation The segmentation of anatomic structures, which isolates specific objects of an image, is often the initial process in image analysis. Segmentation of the cardiac image volumes was performed using tools found within the AnalyzeAVW 13,14 software package developed in the Biomedical Imaging Resource at Mayo Clinic. Using the Volume Render tool, all slices at one time point can be displayed as a single 3-D volume. The contrast filled chambers can be readily segmented by specifying an appropriate threshold. AnalyzeAVW offers several automatic and semi-automatic segmentation tools, but in order to divide the thresholded data into specific anatomic regions, such as the left ventricle (LV), atrium and aorta, manual segmentation tools are used. The disadvantages of manual segmentations are that it is labor intensive and is prone to subjective and/or fatigue errors. Using the Trace Tool and Slice Edit, tools in AnalyzeAVW, regions of interest can be accurately traced with a computer mouse or trackball. The Trace Tool allows tracing, demarking and deleting specific regions in the 3-D projection. Regions can be defined or erased all the way through the volume or by only one pixel or layer at a time. In Slice Edit areas of interest can be traced on single slices of the image volume in transverse, coronal or sagittal views. Both tools modify the data format from image files to object files. In manual segmentation, knowledge about the anatomic structures of the heart is essential. To determine the anatomical borders between the two chambers and between the LV and aorta, multiple anatomic landmarks are required. The iodine bolus does not show the valves, which are the most useful structures to use as reference points for segmenting these structures. However, by comparing all time points, a fast filling process at end diastole could be identified in the contrast bolus. This area correlates to the base of the aorta at the aortic valve. This point was used as an anatomic reference point, along with the papillary muscles. Using these references the LV, atrium and aorta were segmented throughout the cardiac cycle. Experienced cardiologists verified the segmentations. As a final step preparatory to using the surface tiling algorithm, a math morphology tool in AnalyzeAVW was used to fill any holes in the segmented objects which would cause errors in the modeling process. Regions of lower contrast material concentration may cause some holes during the segmentation. 2.3 Surface reconstruction of the Myocardium Surface reconstruction is the transformation from a binary image volume to a geometric description of an object surface. To model the physical structure of the LV volumes, an adaptive deformable surface tiling program was used 15. The physical

3 structure is represented as a set of nodes (polygon vertices) interconnected by adjustable springs (polygon edges). The structure of the segmented volume at end diastole was used as an initial triangular mesh. This triangular mesh gradually deforms to fit every volume throughout the cardiac cycle. The method forces the mesh for each time point volume to have the same number of vertices and triangles. Each vertex is assigned to the closest voxel of the subsequent time-point surface. Figure 1 shows the triangular mesh of the end diastolic and end systolic volumes of the first data set. The displacement of a single vertex in the mesh can be used to describe the trajectory of motion of a specific myocardial surface point throughout the cardiac cycle. 2.4 Motion tracking, analysis and visualization The goal of this research was to measure, compute and visualize the instantaneous regional myocardial motion throughout the cardiac cycle of both healthy and infarcted hearts. To compute these parameters powerful workstations were used, especially for visualization which is compute intensive. The software to perform these computations was written in C/C++ and Open Inventor. Figure 1: Polygonal mesh deformable model of left ventricle at end diastole (left) and end systole (right) of a healthy canine heart Tracking and Analysis To measure motion, all information about each vertex must be assigned to a specific position in an array. The array was partitioned into fifteen major columns representing the time points. Each column includes three sub columns representing x, y, z coordinates of the vertex. The rows of the array contained the vertices. Each time point of the first data set consisted of vertices and faces. The total dimension of the array was by 9, representing total coordinates. To process the second data set, two arrays with even bigger total dimensions were created. The non-infarct time points consisted of vertices and faces, and the infarct time points consist of vertices and faces. The specific goal of the research was to compute and display the regional maximum velocity and excursion of myocardium through the cardiac cycle, including the absolute value of these parameters. The myocardium shifts globally within the chest throughout the cardiac cycle, and this has a confounding influence on quantifying the intrinsic cardiac motion. To eliminate this problem a static vertex describing the cardiac shift was determined. At the apex the superficial fibres of the myocardium turn inward into the interior of the ventricle (vortex cordis), so the apex was chosen as a fixed reference point. Using this anatomic fiducial, the heart shift could be determined and used to compensate the calculation of myocardial motion. To calculate the specific motion of the LV, the deformation constraint was based on the surface reconstruction algorithm used. Since each vertex describes the pathway of a single surface point, computation of the dynamic properties of myocardial function is then straightforward. Regional excursion is defined as the vector length between any selected vertex in space and the initial position of the vertex. Regional velocity is the vector distance one vertex moves during successive time-points. To find the time point of regional maximum velocity and excursion, the maximum value for each surface vertex was calculated throughout the cardiac cycle. The time point of this regional maximum was assigned to a time point specific color value. Colors were also assigned to the maximum excursion and velocity values.

4 2.4.2 Visualization To visualize instantaneous regional myocardial motion, parametric displays with value or time point equivalent colors were designed. Each dynamic property was divided into fifteen reference colors. The reference colors were assigned to each time point in the cardiac cycle. Each time point represented a period of 66.7ms. The excursion value colors were assigned in 0.75 voxel (0.69 mm) increments, and the velocity value colors in 0.5 voxel (0.46mm) increments. To display the dynamic properties, the assigned color values were mapped onto the polygonal surface representing the myocardial wall. The color of each surface triangle was determined by interpolating the colors of the three connecting triangular vertices. Images of these functional structure maps can be displayed as the six faces of a cube pulled apart to see all myocardial surfaces simultaneously displayed from the six orthogonal directions. To create a dynamic picture of the actual myocardial contraction and relaxation process, an animation of the myocardial motion was created. This animation displays simultaneously the anatomic deformation and the instantaneous local myocardial velocity changes, as well as the trajectory of the motion for individual segments of myocardium throughout the cardiac cycle. 3. RESULTS Figures 2-3 illustrate the function to structure mappings of excursion values and velocities during the 15 different time points of the entire cardiac cycle for the healthy heart. Figure 2 provides an overview of the regional maximum excursion value. In this illustration, white denotes the lowest value, and black denotes the highest value. The right panel view shows the greatest excursion of the myocardium. This correlates to an excursion between voxels and voxels or 11.1mm and 11.7mm. In addition to the illustrated excursion value map, the velocity value maps show a similar pattern. Regions with high velocity also demonstrate greater excursion. The grayscale distribution in each of the 6 views of the ventricular model in Figure 3 corresponds to the maximum velocity of regional myocardium over the ventricular wall. For example, in the left panel view of the cube, the light gray areas, at the base, indicates myocardium that reached maximum velocity at time point 5. To illustrate the differences in function to structure relationships caused by an infarct, Figures 4-7 display regional myocardial excursions and velocity values before and after occlusion of an epicardial coronary artery. In Figure 4, the regional maximum excursion of the myocardium through the cardiac cycle prior to occlusion is shown. Figure 5 illustrates this same mapping for the post-occlusion, infarcted heart. Both illustrations show that the major myocardial surface areas reach their maximum regional excursion near the end systolic volume (time -point 7). However, the infarcted heart shows significant differences in maximum excursion at the base of the myocardium and near the atrium. The bottom, left, back and right panel views show regions of early maximum excursion near the base of the chamber. The number of such surface points in the post-occlusion myocardium at time intervals 1-4 is about three times higher than for the pre-occlusion myocardium. These regions of early maximum excursions also have a low value of maximum excursion, which indicates to a reduction in ejection fraction. The distribution of regional excursion data also suggests that remote regions of the myocardium try to compensate for reduced LV function caused by damaged tissue. This compensatory delay is illustrated in all post occlusion panels. The front, right and the top panels illustrate large regions of late maximum excursions, and the bottom, top, back and the left panels show areas of maximum excursion as late as time-point 10. Especially the atrial shows large regions of late maximu m excursion. This compensatory mechanics of the infarcted LV lead to a shorter diastolic period. Figures 6-7 compare regional maximum velocity values of myocardium through the cardiac cycle. Figure 6 illustrates the regional velocity mapping of the non-infarct myocardium. Figure 7 shows the regional velocity mapping of the infarcted myocardium. The graph in Figure 8 compares the number of vertices in the LV model against regional velocities, for both pre- and post-occlusion hearts. Comparing Figures 6 and 7 reveals that the infarcted myocardium has larger areas of low velocity values (up to mm / sec), which is also clearly depicted in the graph of Figure 8. These areas correlate to nearly two thirds of the total surface area of the myocardium, about twice as many vertices as in the non-infarct heart. In contrast to the large and variable regions of low velocities in the infarcted myocardium, the major area of the maximum velocity values of the non-infarct myocardium is quite uniformly distributed between and 67.5 mm / sec. The lower maximum velocity values of the infarcted heart help explain the interpretations of Figures 4 and 5, that the maximum excursion and the decrease of the maximum excursion value is extended over more of the myocardium in the infarcted heart.

5 Figure 2: Value of regional maximum excursion of myocardium through cardiac cycle. Figure3: Regional maximum velocity of myocardium through cardiac cycle.

6 Figure 4: Regional maximum excursion of myocardium through cardiac cycle (non-infarct). Figure 5: Regional maximum excursion of myocardium through cardiac cycle (infarct).

7 Figure 6: Value of regional maximum velocity of myocardium through cardiac cycle (non-infarct). Figure 7: Value of regional maximum velocity of myocardium through cardiac cycle (infarct).

8 Figure 9 shows yet another type of quantitative parametric display. Illustrated is an image at one time -point during the animation of the deformable model of the normal heart. White shades on the myocardial surface depict regions of little or no motion and black shades show relative velocity values of moving myocardium the deeper black corresponding to higher velocity. The darker trajectory illustrates the systolic (contraction) motion pathway of surface points and the yellow trajectory illustrates the diastolic (relaxation) motion pathway through one complete heartbeat. All images are much more clearly visualized using color as opposed to grayscale illustrations Number of Vertices Non-Infarct Infarct Velocity-Value in mm/sec Figure 8: Graphical display of the number of model vertices per velocity value interval Figure 9: Display of the velocity map and pathway trajectories of selected voxels during myocardial contraction (red) and relaxation (yellow).

9 4. DISCUSSION The ability to encode quantitative functional information on dynamic cardiac anatomy enhances the diagnostic value of 4D images of the heart. Measures of regional myocardial mechanics at specific time -points throughout the cardiac cycle adds another dimension to the analysis of cardiac disease. The ability to see the motion of the heart walls combined with local myocardial tissue excursion and velocity could improve diagnosis of myocardial abnormalities, including regional ischemia and infarcts, diastolic relaxation defects and / or disturbances in regional contraction patterns. Since the algorithm is modality independent, it can also be applied to MR-Tagging data. The algorithm is computationally efficient, producing each functional structure map throughout the complete cardiac cycle in less than two minutes. To animate the entire heartbeat, with encoded functional mappings and trajectory of the motion for individual segments, less than three minutes is required on an SGI O2 workstation with an R MHz Processor and 256 Megabytes of RAM. Thus the algorithm processes data in a time frame short enough for potential clinical application on a routine basis. ACKNOWLEDGEMENTS The authors express thanks to Weite Lin for his help with the deformable surface reconstruction program, to Pat Lund for the segmentation assistance and to Erik Ritman, M.D/Ph.D., Mike Wahl, MD and Jon J. Camp for their expert advice and technical support. REFERENCES 1. E.L. Ritman, L.D. Harris, J.H. Kinsey, R.A. Robb, Computed Tomographic Imaging of the Heart: The Dynamic Spatial Reconstructor, Radiol. Clin. North Am Dec; 18(3): R.A. Robb, A.H. Lent, B.K. Gilbert, A. Chu, The Dynamic Spatial Reconstructor: A Computed Tomography System for High-Speed Simultaneous Scanning of Multiple Cross Sections of the Heart, J. Med. Syst. 1980;4(2) 3. J.L. Prince, E.R. McVeigh, Motion Estimation from tagged MR image sequences, IEEE Transactions on Medical Imaging, 11: , June J. Park, D. Metaxas, L. Axel, Volumetric Deformable Models with Parameter Functions: A new approach to the 3D Motion Analysis of the LV from MRI-SPAMM, Fifth International Conference on Computer Vision, , J.C. McEachen, F.G. Meyer, R.T. Constable, A. Nehorai, J.S. Duncan, A Recursive Filter for Phase Velocity Assisted Shape-based Tracking of Cardiac Non-rigid Motion, 6. P. Croissile, C.C. Moore, R.M. Judd, J.A.C. Lima, M. Arai, E.R. McVeigh, L.C. Becker, E.A. Zerhouni, Differentiation of viable and nonviable myocardium by the use of three-dimensional tagged MRI in 2-day-old reperfused infarcts, Circulation, 99: , X. Papademetris, A.J. Sinusas, D.P. Dione, J.S. Duncan, 3D Cardiac Deformation from Ultrasound Images, 8. P. Shi, G. Robinson, J.S. Duncan, Myocardial Motion and Function Assessment Using 4D Images, Proceedings of IEEE Visualization in Biomedical Computing P. Shi, A. Amini, G. Robinson, A. Sinusas, C.T. Constable, J.S. Duncan, Shape-Based 4D Left Ventricular Myocardial Function Analysis, 10. D. Friboulet, I. E. Magnin, C. Mathieu, A. Pommert, K.H. Hoehne, Assessment and Visualization of the Curvature of the Left Ventricle from 3D Medical Images, Computerized Medical Imaging and Graphics 1993, Vol. 4/5: J.M. Gorce, D. Friboulet, P. Clarysse, I.E. Magnin, Three-dimensional Velocity Field Estimation of Moving Cardiac Walls, Computers in Cardiology 1994, Vol /94: P. Clarysse, O. Jaouen, I.E. Magnin, J.M. Morvan, 3D Representation and Deformation Analysis of the Heart Walls from X-ray and MRI Images,, Computers in Cardiology 1994, Vol /94: R.A. Robb, D.P. Hanson, The ANALYZE software system for visualization and analysis in surgery simulation, chap. In Computer Integrated Surgery (Cambridge, MA: MIT Press, 1995) 14. R.A. Robb, Three-Dimensional Biomedical Imaging - Principles and Practice, VCH Publishers, New York, NY W.T. Lin, R.A. Robb, Realistic Visualization for Surgery Simulation Using Dynamic Volume Texture Mapping and Model Deformation, Proceedings of SPIE, Medical Imaging 1999, Vol. 3658: