Assessment of left ventricular contraction by parametric analysis of main motion (PAMM): theory and application for echocardiography

The computerized study of the regional contraction of the left ventricle has undergone numerous developments, particularly in relation to echocardiography. A new method, parametric analysis of main motion (PAMM), is proposed in order to synthesize the information contained in a cine loop of images in parametric images. PAMM determines, for the intensity variation time curves (IVTC) observed in each pixel, two amplitude coefficients characterizing the continuous component and the alternating component; the variable component is generated from a mother curve by introducing a time shift coefficient and a scale coefficient. Two approaches, a PAMM data driven and a PAMM model driven (simpler and faster), are proposed. On the basis of the four coefficients, an amplitude image and an image of mean contraction time are synthesized and interpreted by a cardiologist. In all cases, both PAMM methods allow better IVTC adjustment than the other methods of parametric imaging used in echocardiography. A preliminary database comprising 70 segments is scored and compared with the visual analysis, taken from a consensus of two expert interpreters. The levels of absolute and relative concordance are 79% and 97%. PAMM model driven is a promising method for the rapid detection of abnormalities in left ventricle contraction.

[1]  G R Sutherland,et al.  Color Doppler myocardial imaging: a new technique for the assessment of myocardial function. , 1994, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[2]  S. Hardt,et al.  Echocardiographic quantification of left ventricular asynergy in coronary artery disease with Fourier phase imaging , 2001, The International Journal of Cardiovascular Imaging.

[3]  G. Mailloux,et al.  Computer Analysis of Heart Motion from Two-Dimensional Echocardiograms , 1987, IEEE Transactions on Biomedical Engineering.

[4]  María J. Ledesma-Carbayo,et al.  Cardiac Motion Analysis from Ultrasound Sequences Using Non-rigid Registration , 2001, MICCAI.

[5]  H. Torp,et al.  Myocardial Strain by Doppler Echocardiography: Validation of a New Method to Quantify Regional Myocardial Function , 2000, Circulation.

[6]  Roberto M. Lang,et al.  Improvement in echocardiographic evaluation of left ventricular wall motion using still-frame parametric imaging , 2002 .

[7]  Élie Mousseaux,et al.  Parametric analysis of main motion: application to the assessment of left ventricular wall motion by MR imaging , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[8]  Frédérique Frouin,et al.  Comparison between factor analysis of dynamic structures and Fourier analysis in detection of segmental wall motion abnormalities: a clinical evaluation , 1995, The International Journal of Cardiac Imaging.

[9]  Andrés Santos,et al.  Assessment of normal and ischaemic myocardium by quantitative M-mode tissue Doppler imaging. , 2002, Ultrasound in medicine & biology.

[10]  C. Lamberti,et al.  Evaluation of differential optical flow techniques on synthesized echo images , 1996, IEEE Transactions on Biomedical Engineering.

[11]  Yongmin Kim,et al.  A multiple active contour model for cardiac boundary detection on echocardiographic sequences , 1996, IEEE Trans. Medical Imaging.

[12]  A. Støylen,et al.  Real-time strain rate imaging of the left ventricle by ultrasound. , 1998, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[13]  N. Paragios.  A level set approach for shape-driven segmentation and tracking of the left ventricle , 2003, IEEE Transactions on Medical Imaging.

[14]  R Di Paola,et al.  Factor analysis in gated cardiac studies. , 1984, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[15]  J. Noble,et al.  Quantitative regional analysis of myocardial wall motion. , 2001, Ultrasound in medicine & biology.

[16]  Alain De Cesare,et al.  Algorithmes rapides de restauration de signaux avec prise en compte des discontinuités , 1997 .

[17]  Echocardiographic Fourier phase and amplitude imaging for quantification of ischemic regional wall asynergy: an experimental study using coronary microembolization in dogs. , 1995, Journal of the American College of Cardiology.

[18]  James D. Thomas,et al.  Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates , 1998, IEEE Transactions on Medical Imaging.

[19]  L. Guize,et al.  Reproducibility of the ventricular synchronization parameters assessed by multiharmonic phase analysis of radionuclide angiography in the normal heart , 2002, The International Journal of Cardiovascular Imaging.

[20]  J Meunier.  Tissue motion assessment from 3D echographic speckle tracking. , 1998, Physics in medicine and biology.

[21]  Frédérique Frouin,et al.  Factor analysis of the left ventricle by echocardiography (FALVE): a new tool for detecting regional wall motion abnormalities. , 2004, European journal of echocardiography : the journal of the Working Group on Echocardiography of the European Society of Cardiology.

[22]  Otto A. Smiseth,et al.  Quantification of Left Ventricular Systolic Function by Tissue Doppler Echocardiography: Added Value of Measuring Pre- and Postejection Velocities in Ischemic Myocardium , 2002, Circulation.

[23]  V. Mor-Avi,et al.  Detection of regional temporal abnormalities in left ventricular function during acute myocardial ischemia. , 2001, American journal of physiology. Heart and circulatory physiology.

[24]  L Weinert,et al.  Segmental analysis of color kinesis images: new method for quantification of the magnitude and timing of endocardial motion during left ventricular systole and diastole. , 1997, Circulation.

[25]  A Herment,et al.  Spatial regularization applied to factor analysis of medical image sequences (FAMIS). , 1999, Physics in medicine and biology.

[26]  P. Simard,et al.  Restoration of the velocity field of the heart from two-dimensional echocardiograms. , 1989, IEEE transactions on medical imaging.

[27]  O. L. Kwan,et al.  Normal values of regional left ventricular endocardial motion: multicenter color kinesis study. , 2000, American journal of physiology. Heart and circulatory physiology.

[28]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Circulation.

[29]  J. Meunier,et al.  Echographic image mean gray level changes with tissue dynamics: a system-based model study , 1995, IEEE Transactions on Biomedical Engineering.

[30]  L Weinert,et al.  Quantitative evaluation of global and regional left ventricular diastolic function with color kinesis. , 1998, Circulation.

[31]  José M. N. Leitão,et al.  Wall position and thickness estimation from sequences of echocardiographic images , 1996, IEEE Trans. Medical Imaging.

[32]  H. Bazett,et al.  AN ANALYSIS OF THE TIME‐RELATIONS OF ELECTROCARDIOGRAMS. , 1997 .

[33]  O. Gérard,et al.  Efficient model-based quantification of left ventricular function in 3-D echocardiography , 2002, IEEE Transactions on Medical Imaging.

[34]  Andrew Blake,et al.  Evaluating a robust contour tracker on echocardiographic sequences , 1999, Medical Image Anal..

[35]  J. Alison Noble,et al.  A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography , 2002, IEEE Transactions on Medical Imaging.

[36]  K J Parker,et al.  Multilevel and motion model-based ultrasonic speckle tracking algorithms. , 1998, Ultrasound in medicine & biology.