AAM-based Segmentation for Imaging Cardiac Electrophysiology

Summary

Objectives: Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling 4-dimensional volume data (3D + time) the electrical sequence can be computed non-invasively. In this paper an approach for extracting the ventricular and atrial blood masses for structurally normal hearts by using cine-gated shortaxis data obtained via magnetic resonance imaging (MRI) is introduced.

Methods: The blood masses are extracted by employing Active Appearance Models (AAMs). The ventricular blood masses are segmented, applying the AAMs after providing apex cordis and base of the heart in the volume data, whereas the more complex geometry of the atria requires a more specific attempt. On account of this the atrium was divided into three divisions of appearance, where the images of the volume data in the related divisions have a maximum affinity. The first division reaches from the base of the heart to initial visibility of the upper and left lower pulmonary vein. The second division up from there to the last occurrence and the third division from there to the end of the visibility of the right upper and lower pulmonary vein. After extracting the cardiac blood masses the result gets triangulated and remeshed for activation time imaging.

Results: With this method the cardiac models of eight patients were extracted and the AT imaging approach was applied to single-beat ECG data of atrial and ventricular depolarization.

Conclusion: The advantage of the proposed AAM approach is that only a few initial parameters have to be set. Therefore, the approach can be integrated into a processing pipeline that works semi-automatically. The extracted models can be used for further investigations.

Keywords

Segmentation - Active Appearance Models - image processing - medical imaging

• References

• 1 Tilg B, Fischer G, Modre R, Hanser F, Messnarz B, Schocke M, Kremser C, Berger T, Hintringer F, Roithinger FX. Model-based imaging of cardiac electrical excitation in humans. IEEE Trans Med Imag 2002; 21: 1031-9.
• 2 Modre R, Tilg B, Fischer G, Wach P. A noninvasive myocardial activation time imaging: A novel inverse algorithm applied to clinical ecg mapping data. IEEE Trans Biomed Eng 2002; 49: 1153-61.
• 3 van Rijn RAC, Peper A, Grimbergen CA. High quality recording of bioelectric events. Part 1. Interference reduction, theory and practice. Med Biol Eng Comput 1990; 28 (Suppl. 05) 389-97.
• 4 van Rijn RAC, Peper A, Grimbergen CA. Highquality recording of bioelectric events. Part 2. Low-noise, low-power multichannel amplifier design. Med Biol Eng Comput 1991; 29 (Suppl. 04) 433-4.
• 5 Taccardi B, Punske B, Lux RL, MacLeod RS, Ershler PR, Dustman TJ, Vyhmeister Y. Useful lessons from body surface mapping. J Cardiovasc Electrophysiol 1998; 9 (Suppl. 07) 773-86.
• 6 Harrild DM, Henriquez CS. A Computer Model of Normal Conduction in the Human Atria. Circ Res 2000; 87: e25-36.
• 7 Harrild DM, Henriquez CS. A finite volume model of cardiac propagation. Ann Biomed Eng 1997; 25 (Suppl. 02) 315-34.
• 8 Haissaguerre M, Jais P, Shah DC, Takahashi A, Hocini M, Quiniou G, Garrigue S, Le Mouroux A, Le Metayer P, Clementy J.. Spontaneous initiation of atrial fibrillation by ectopic beats originating from the pulmonary veins. N Engl J Med 1998; 9: 659-66.
• 9 Chen S, Hsieh M, Tai C, Tsai C, Prakash V, Yu W, Hsu T, Ding Y, Chang M. Initiation of atrial fibrillation by ectopic beats originating from the pulmonary veins: electrophysiological responses, and effects of radiofrequency ablation. Circulation 1999; 100: 1879-86.
• 10 Vigmond EJ, Ruckdeschel R, Trayanova NA. Reentry in a morphologically realistic atria. J Cardiovasc Electrophysiol 2001; 12 (Suppl. 09) 1046-54.
• 11 Morady F. Radio-frequency ablation as treatment for cardiac arrhythmias. N Engl J Med 1999; 340: 534-44.
• 12 Andrea E, Atie J, Maciel W, Araujo N, Saad E, Camanho LE, Alonso H, Siqueira L, Belo LG. Mapping of supraventricular tachycardias by using a new tridimensional technology The CARTO system. J. Electrocardiol 2001; 34 (Suppl. 04) 334.
• 13 Ben Haim SA, Osadchy D, Schuster I, Gepstein L, Hayam G, Josephson ME. Nonfluoroscopic, in vivo navigation and mapping technology. Nat Med 1996; 2 (Suppl. 012) 1393-5.
• 14 Tilg B, Hanser F, Modre R, Fischer G, Messnarz B, Berger T, Hintringer F, Pachinger O, Roithinger F. Clinical ECG mapping and imaging of cardiac electrical excitation. Journal of Electrocardiology 2002; 35, no. Suppl: 81-7.
• 15 Tilg B, Fischer G, Modre R, Hanser F, Messnarz B, Roithinger F. Electrocardiographic imaging of atrial and ventricular electrical activation. Medical Image Analysis 2003; 7: 391-8.
• 16 Modre R, Tilg B, Fischer G, Hanser F, Messnarz B, Seger M, Schocke M, Berger T, Hintringer F, Roithinger F. Atrial noninvasive activation time imaging of paced rhythm data. Journal of Cardiovascular Electrophysiology 2003; 14 (Suppl. 07) 712-9.
• 17 Staib LH, Duncan JS. Boundary finding with parametrically deformable contour models. IEEE Trans Pattern Anal and Machine Intelligence 1992; 14 (Suppl. 011) 1061-75.
• 18 Staib LH, Duncan JS. Model-based deformable surface finding for medical images. IEEE Trans Med Imaging 1996; 15 (Suppl. 05) 720-31.
• 19 Higgins WE, Chung M, Ritman EL. Extraction of left-ventricular chamber from 3-D CT images of the heart. IEEE Trans Med Imaging 1990; 9 (Suppl. 04) 384-95.
• 20 Niessen WJ, ter Haar Romeny BM, Viergever MA. Geodesic deformable models for medical image analysis. IEEE Trans Med Imaging 1998; 17 (Suppl. 04) 634-41.
• 21 Weng J, Singh A, Chiu MY. Learning-based ventricle detection from cardiac MR and CT images. IEEE Trans Med Imaging 1997; 16 (Suppl. 04) 378-91.
• 22 Stalidis G, Maglaveras N, Efstratiadis SN, Dimitriadis AS, Pappas C. Model-Based Processing Scheme for Quantitative 4-D Cardiac MRI Analysis. IEEE Trans Inf Tech in Biomed 2002; 6 (Suppl. 01) 59-72.
• 23 Bradley C, Pullan A, Hunter P. Effects of material properties and geometry on electrocardiographic forward simulations. Annals of Biomedical Engineering 2000; 28 (Suppl. 07) 721-41.
• 24 Craw I, Cameron P. Parameterising images for recognition and reconstruction. 2nd British Machine Vision Conference. London: Springer; 1998. pp 367-70.
• 25 Cootes T, Taylor C.. Modelling object appearance using the gray-level surface. 5th British Machine Vision Conference, Sept. 1994. BMVA Press; pp 419-28.
• 26 Lantis A, Taylor C, Cootes T. Automatic tracking, coding and reconstruction of human faces using flexible appearance models. IEE Electronic Letters 1994; 30: 1578-9.
• 27 Edwards G, Taylor C, Cootes T.. Interpreting face images using active appearance models. In: 3rd International Conference on Automatic Face and Gesture Recognition, Japan: 1998. pp 300-5.
• 28 Cootes T, Taylor C. Active appearance models. In: Burkhardt H, Neumann B. (eds). 5th European Conference on Computer Vision. Springer; 1998. pp 484-98.
• 29 Cootes T, Taylor C. Statistical models of appearance for computer vision. http://www.isbe.man.ac.uk 2004
• 30 Cootes T, Taylor C. Modelling object appearance using the gray-level surface. 5th British Machine Vision Conference, Sept 1994. BMVA Press; pp 419-28.
• 31 Dice LR. Measures of the amount of ecologic association between species. Ecology 1945; 26 (Suppl. 03) 297-302.
• 32 Heckbert PS, Gardland M. Optimal Triangulation and Quadric-Based Surface Simplification. J Comput Geom: Theory and Applications; 1999
• 33 Bern M, Eppstein D. Mesh generation and optimal triangulation. Computing in Euclidean Geometry. 2nd ed. World Scientific 1995 pp 47-123.
• 34 Pfeifer B, Hanser F, Hintermüller C, Modre-Osprian R, Fischer G, Seger M, Kremser C, Tilg B. Atrial myocardium model extraction. Proc SPIE Medical Imaging 2004 Visualization, Image-Guided Procedures, and Display May 2004. Vol. 5367, pp 320-31.