Abnormalities in heart motion can eventually lead to life threatening cardiac injuries therefore measurements of dynamic heart functions are of great clinical importance. The images of moving spatial heart structures can be efficiently acquired using 4D echocardiography. Unfortunately, because of the low quality such images do not allow for precise measurements. To overcome this problem images need to be further processed and moving structures have to be extracted. In this work we present a method for estimating heart motion from the 3D echocardiographic image sequence. On the basis of this method we have developed an application that enables qualitative and quantitative (i.e. volume changes, stroke volume, ejection fraction and cardiac output parameters) description of the heart wall motion. We provide a set of tools for denoising images using the anisotropic diffusion algorithm extended to the fourth dimension and the time averaging method based on non-linear registration efficiently parameterized using the B-spline based Free Form Deformation. We have also developed a non-linear deformable segmentation algorithm for extraction of the inner ventricular surface. The motion of the left ventricle is reconstructed in our approach by recovering deformations of the matter during the cardiac cycle. All the obtained results using our framework can be efficiently presented in 3D using a set of newly developed heart motion visualization tools

Bała, Piotr

Chlebiej, Michał

Nowiński, Krzysztof

Ścisło, Piotr

English

University of Maria Curie-Skłodowska (UMCS): Scientific e-Journals / Uniwersytet Marii Curie-Skłodowskiej: e-czasopisma naukowe

Annales UMCS Informatica AI VIII, 2 (2008) 43–50DOI: 10.2478/v10065-008-0023-3Development of heart motion reconstructionframework based on the 4D echocardiographic dataMichaÃl Chlebiej1∗, Krzysztof Nowin´ski2, Piotr S´cisÃlo3, Piotr BaÃla1,21Faculty of Mathematics and Computer Science. N. Copernicus University,Chopina 12/18, 87-100 Torun´, Poland2Interdisciplinary Centre for Mathematical and Computational Modeling,Warsaw University, Pawin´skiego 5a, 02-106 Warsaw, Poland3Department of Cardiology, Medical Academy of Warsaw, 02-097 Warszawa, PolandAbstractAbnormalities in heart motion can eventually lead to life threatening cardiac injuries there-fore measurements of dynamic heart functions are of great clinical importance. The imagesof moving spatial heart structures can be efficiently acquired using 4D echocardiography. Un-fortunately, because of the low quality such images do not allow for precise measurements.To overcome this problem images need to be further processed and moving structures haveto be extracted. In this work we present a method for estimating heart motion from the 3Dechocardiographic image sequence. On the basis of this method we have developed an appli-cation that enables qualitative and quantitative (i.e. volume changes, stroke volume, ejectionfraction and cardiac output parameters) description of the heart wall motion. We providea set of tools for denoising images using the anisotropic diffusion algorithm extended to thefourth dimension and the time averaging method based on non-linear registration efficientlyparameterized using the B-spline based Free Form Deformation. We have also developeda non-linear deformable segmentation algorithm for extraction of the inner ventricular surface.The motion of the left ventricle is reconstructed in our approach by recovering deformations∗Corresponding author: e-mail address: meow@mat.uni.torun.plPobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCS44 MichaÃl Chlebiej, Krzysztof Nowin´ski . . .of the matter during the cardiac cycle. All the obtained results using our framework can beefficiently presented in 3D using a set of newly developed heart motion visualization tools.1. IntroductionUltrasonographic examination of the heart (echocardiography), together withtechniques based on electrocardiography, is one of the most frequently usedmethods of the heart examination. Using this modality, vital informationabout morphology and hemodynamics of the heart could be collected by sim-ple, bedside assessment. Echocardiography, even real time 3D, is a relativelyinexpensive (when compared to CT or MRI) data acquisition technique. Inclinical practice the analysis of the data mainly relies on the visual inspectionof the acquired views and on the physician’s experience. Such methods lead toqualitative and subjective assessment without taking into account individualquantitative information included in images. Another problem of the echocar-diographic analysis are artifacts from the thorax (e.g. emphysema), which in5–10% of patients makes echocardiography not useful. To reveal all these thevital information and decrease information noise, automated computer-basedanalysis is highly desirable.Recently, methods were proposed for the reconstruction of heart motion fromthe 4D ultrasound images. For the left ventricle segmentation surface basedmethods (using shape and motion constraints) have been proposed to deal withspeckle noise in the echocardiograms [1]. Biomedical models have been inves-tigated for the modeling of cardiac cycle [2]. In this work we present a set ofthe heart wall visualization methods. These methods make an extensive use ofthe left ventricle motion description method [3] briefly presented in the nextsection.2. MethodologyThe heart reconstruction method implemented in our framework consists offive main stages (see Fig. 1). In the first step images are filtered using 4Danisotropic diffusion [4]. Next non–rigid registration of the 3D time sequence isperformed to obtain the description of deformation field. Non–rigidly registeredimages are used to compute an average 3D dataset. The next phase consistsof the shape and texture based segmentation followed by a triangulation stepresulting in the 3D surface model. In the final step deformation operator isapplied to the surface model in order to recover the time motion of the leftventricle.In the first stage (see Fig. 1a) our goal is to reduce noise from the sequence of3D ultrasound data. We have decided to use the anisotropic diffusion schemePobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UCSDevelopment of heart motion reconstruction . . . 45T0 T1 T2 T3 T4 T5 T6 T7 T8 DDDDDDDD+ +...+Ref D1DNcrossections and surface rendering-pathsI0 I1 I4 I8 I12Non-linear hierarchical registration using B-spline FFD modelImage Sequence Filtering by 4D anisotropic diffusionRecovering the time dependent deformation fieldDenoising by temporal averaging using the sequence of non-linearly registered 3D imagesSegmetnation of the left ventricleVisualization of the reconstructed motionmotion vectors lineFig. 1. Flow diagram of heart motion reconstruction methodand take into account temporal consistency of the acquired data. The diffusionalgorithm has been extended to the fourth dimensional block of data with thetime taken as the fourth dimension. As it was presented in [3] such filteringdrastically reduces the speckle noise and enhances the structure boundaries.Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCS46 MichaÃl Chlebiej, Krzysztof Nowin´ski . . .The speckle noise may lead to partial disappearing of the image boundaries.The time diffusion may help to recover some of the missing boundary parts.In the second stage (see Fig. 1b) we want to describe the motion of the wholematter within the 3D image sequence. Such an approach gives possibility tomodel the motion taking into consideration individual patient specific anatom-ical features. In order to achieve realistic motion we have to extract heartdynamics by studying 3D movement of a corresponding anatomy between thereference frame (at time T0) and the following frames (T1 − T8). We recoverthe transformation that aligns the reference frame with all other frames usingintensity based 3D volume registration. In our work we have used FFD [5](free–form–deformation) based method which is a well known powerful tool formodeling 3D deformable objects. We have selected MSD (mean square differ-ence) as the similarity function:EMSD(FI,RI, T ) =1N∫∫∫Ω(IRI(p)− IFI (T (p)))2 dpwhere IRI represents the reference image intensities, IFI represents the cor-responding transformed intensities of the floating image and N is the totalnumber of overlapping voxels. In order to deal with large displacements in theregistration process, we use a classical incremental multi–resolution procedure.After obtaining the 3D frames of the deformation field we are able to describemotion of the whole matter in the volume object.The third phase of our method involves noise reduction by temporal aver-aging (see Fig. 1c). The deformation field frames are used to generate newdatasets elastically aligned with the reference frame T . After this step an aver-age dataset from the reference frame and all the deformed datasets is created.Such an approach enables noise smoothing together with preservation of imagestructures boundaries.The fourth stage applies the segmentation procedure to recover the shapeof the left ventricle (see Fig. 1d). In our work we have used an iterativedeformable boundary approach for the segmentation of the ventricular innersurface. The selected method uses energy function consisting of texture basedand shape based terms. It is a 3D extension of an algorithm proposed in [6].Texture based energy term is calculated on the basis of texture intensity energymap which represents the probabilities of the intensity values i being consistentwith the current segmentation model (updated in every iteration). This termhas been formulated as the Shannon’s entropy [7]. The shape energy termtakes into account gradient information (revealed using the Canny-Deriche’s3D boundary detection filter [8]) available in the source image. The main ideaof this term is to deform (shrink or expand) segmentation model towards imagePobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCSDevelopment of heart motion reconstruction . . . 47boundaries. In this segmentation algorithm, starting with an initial estimate,a deformable model evolves under the influence of the defined energy to convergeto the desired boundary of an image structure object. The model deformationsare efficiently parameterized using the B–spline based free form deformation.The flow diagram of the segmentation algorithm is presented in detail in Fig. 2.Fig. 2. Deformable segmentation algorithm diagramThe last stage of the algorithm involves 3D surface mesh generation and ap-plication of several newly developed visualization methods. Using the recoveredventricular surface we are able to reconstruct the cardiac motion by applyingthe deformation field operator. Using such approach we deal with a singleobject deforming in time so we are able to interpolate between the frames toobtain smooth motion. The motion of the heart can be also characterized interms of its local variations. It is possible to calculate displacement vectors: to-tal displacement (relative to the reference frame T0) and displacement betweenconsequent time frames which can be seen as an instantaneous velocity. We canalso visualize the motion occurring on the surface (twisting) by decomposingthe instantaneous velocity vectors into tangential and normal components. AllPobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCS48 MichaÃl Chlebiej, Krzysztof Nowin´ski . . .of these local variations can be visualized in different ways. In our work weused two kinds of techniques – color and vector based. We colorized movingsurface according to the length values of displacement vectors. It is very usefulto deal with small surface deformations. When the motion is significant it isbetter to visualize vector values using arrows representing length and spatialorientation of moving matter. We call the last method of motion visualizationused in our system line–paths method. In this method we select a small set ofsurface points and visualize the path of their motion during the cardiac cycleusing colorized polygons. Colors of the line segments represent consequent timeframes. Such method allows to estimate the viability of the heart using a singleimage. In addition to the line–paths method we may generate so called activitysurface. With this method we can visualize total path length values (in singlecardiac cycle) for every surface point. The method applied to the single staticimage allows us to estimate spatial extents of pathological regions.3. Framework implementationOur framework has been implemented in the C++ language using TrolltechQT [9] and Kitware VTK [10] libraries for visualization purposes. It has beencompiled and tested under Windows and Linux operating systems. The pre-sented motion reconstruction method is semiautomatic. With opening the 4Dsource dataset the user has an ability to select parameters for anisotropic dif-fusion filtering (number of iterations, diffusion function, threshold parameters)and non-linear registration (number of multi–resolution levels, desired qualityof matching (from the minimum FFD grid size 8× 8× 8 up to 32× 32× 32).At this point the procedure of filtering, matching and average dataset gen-eration steps is performed automatically. The most time consuming part is 3Dregistration. It takes for the pair of 3D images from 3 to 11 minutes (dependingon the desired quality) on typical 2.0GHz PC, so the whole matching processmay take around an hour. Afterwards, the user has to select parameters (ini-tial segmentation shape, weights for energy functional) using interactive toolsand apply the segmentation procedure. This step takes from 1 to 5 minutesof computational time. The last step involves only selection of visualizationmethod and color lookup table. The user can interactively manipulate with theobject and save the resulting 3D animation on disk. Beside the visualizationtechniques it is also possible to calculate some statistical parameters like strokevolume, ejection fraction and cardiac output [11] using volume differences inthe end-diastolic and end–systolic phases (minimum and maximum volumes inthe cardiac cycle) and the heart rate. An exemplary illustration of the graphicaluser interface of the developed framework is visible in Fig. 3.Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCSDevelopment of heart motion reconstruction . . . 49Fig. 3. Example of the heart motion reconstruction framework: deformablesegmentation module (interactive initial ellipsoid selection on the 2D perpendicularprojections, selection of energy functional weights), volume non-linear registrationmodule (selection of matching quality parameters), 3D interactive module (spatialpresentation of 2D perpendicular projections superimposed with semi transparentsurface of the left ventricle and motion vectors)4. Conclusions and future workIn this work we have presented a method for the dynamic heart motionreconstruction from the 4D echocardiographic images. We have also presentedimplementation details and developed software framework. In the near futurewe want to parallelize registration procedures to obtain more acceptable runningtimes of the whole procedure. We are also greatly interested in development offully automatic segmentation procedures of the inner wall of the left ventricle.We plan to extend the segmentation algorithm to be able to detect the outerwall of the heart muscle. Then it will be possible to estimate global parameterslike wall thickening or ventricular mass [11]. In the physician’s opinion, theproposed methods and developed tools enable to detect pathological regions ofthe beating heart with high precision and in the near future may be useful intheir daily clinical practice.Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCS50 MichaÃl Chlebiej, Krzysztof Nowin´ski . . .References[1] Montgant J., Delingette H., Space and time shape constrained deformable surfaces for 4Dmedical image segmentation, Medical Image Computing And Computed Assisted Inter-vention, LNCS, Springer Pittsburgh, Vol. 1935 (2000) 196.[2] Papademetris X., SinuaS A., Dione D., Duncan J., Estimation on 3D left ventriculardeformation from echocardiography, Medical Image Anal., 5(1) (2001) 17.[3] Chlebiej M., MikoÃlajczak P., Nowin´ski K., S´cisÃlo P., BaÃla P., Generation of dynamic heartmodel based on 4D echocardiographic images, LNCS, ICCSA 2006, Springer-Verlag BerlinHeidelberg, 5 (2006) 394.[4] Perona P., Malik J., Scale-space and edge detection using anisotropic diffusion, IEEETrans. On Pattern Anal. and Mach. Intell., 12(7) (1990) 629.[5] Sederberg T., Parry S., Free form deformation of solid geometric models, ComputerGraphics, 20(4) (1986) 151.[6] Huang X., Metaxas D., Chen T., MetaMorphs: Deformable shape and texture models,IEEE Comp. Vision and Patt. Recog.., Washington, D.C., June, 2004.[7] Shannon C.E., A mathematical theory of communication, Bell System Technical Journal,Vol. 27, pp. 2790423 and 623-656, July and October 1948.[8] Monga O., Deriche R., Malandain G., Cocquerez J.P., Recursive filtering and edge track-ing: two primary tools for 3D edge detection, Image and Vision Computing, 9 (4) (1991)203.[9] www.trolltech.com[10] www.kitware.com[11] Frangi A. F., Niessen W. J., Viergever M. A., Three-dimensional modeling for functionalanalysis of cardiac images: A review, IEEE Transactions on Medical Imaging, 20(1)(2001) 2.Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 21:57:29UMCSPowered by TCPDF (www.tcpdf.org)

Development of heart motion reconstruction framework based on the 4D echocardiographic data

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Development of heart motion reconstruction framework based on the 4D echocardiographic data

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Development of heart motion reconstruction framework based on the 4D echocardiographic data

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