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Current Medical Imaging

Eiditor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

The Role of Phase Image in the Detection of Myocardial Dyskinesia by Magnetic Resonance Imaging (MRI)

Author(s): Narjes Benameur*, Younes Arous, Nejmeddine ben Abdallah and Tarek Kraiem

Volume 15 , Issue 2 , 2019

Page: [214 - 219] Pages: 6

DOI: 10.2174/1573405614666171213160836

Price: $58

Abstract

Background: The assessment of cardiac wall motion abnormalities plays an important role in the evaluation of many cardiovascular diseases and the prediction of functional recovery. Most of the methods dedicated to identifying the location of wall motion abnormalities have been restricted to study hypokinesia while an accurate way to assess dyskinesia is still needed in Cardiac Magnetic Resonance Imaging (CMRI).

Objective: The aim of this study is to propose a phase image based on the analytic signal able to assess the extent of the myocardial dyskinetic segments in Cardiac Magnetic Resonance Imaging (CMRI).

Materials: 22 subjects were retrospectively enrolled in this study (age 46 ± 11): 15 presenting an aneurysm and 7 control subjects with normal wall motion. For each patient, three standard views (short axis view, 2 chamber and 4 chamber views) were acquired using 3 Tesla Siemens Avanto MRI scanner and a segmented True FISP sequence. All the cine MRI images were analyzed by two experimented observers who were blinded to the diagnostic results.

Results: The outcomes of this study show that using the proposed phase image in MRI clinical routine can increase the accuracy of the detection of myocardial dyskinetic segments from 77.23 % to 86.38 %, the sensitivity from 67.48 % to 78.86 % as well the specificity from 80.92 % to 89.23 % compared to the standard method based on cine MRI interpretation.

Conclusion: The phase image is a promising tool in CMRI for the assessment of dyskinetic segments and the degree of myocardial asynchronism.

Keywords: Phase image, assessment, dyskinesia, wall motion abnormalities, CMRI, myocardium.

Graphical Abstract
[1]
Braunwald E, Kloner RA. The stunned myocardium: Prolonged, postischemic ventricular dysfunction. Circulation 1982; 66(6): 1146-9.
[2]
Declan PO, Rizwan A, Stuart AC. Cardiac MRI of myocardial salvage at the peri-infarct border zones after primary coronary intervention. Am J Physiol Heart Circ Physiol 2009; 297(1): H340-6.
[3]
Kai J, Xin Y. Quantification of regional myocardial wall motion by cardiovascular magnetic resonance. Quant Imaging Med Surg 2014; 4(5): 345-57.
[4]
Ben Ameur N, Khlifa N, Kraiem T. Parametric Images for the assessment of cardiac kinetics by Magnetic Resonance Imaging (MRI). In: Image Processing Applications and Systems conference (IPAS); 2014 Nov 5-7; Sfax, Tunisia: IEEE 2014; pp. 1-4.
[5]
Koch R, Lang RM, Garcia MJ, et al. Objective evaluation of regional left ventricular wall motion during dobutamine stress echocardiographic studies using segmental analysis of color kinesis images. J Am Coll Cardiol 1999; 34(2): 409-19.
[6]
Otto AS, Hans T, Anders O, Kristina HH, Stig U. Myocardial strain imaging: How useful is it in clinical decision making? Eur Heart J 2016; 37: 1196-207.
[7]
Kachenoura N, Mor-Avi V, Frouin F, et al. Diagnostic value of parametric imaging of left ventricular wall motion from contrast-enhanced echocardiograms in patients with poor acoustic windows. J Am Soc Echocardiogr 2009; 22(3): 276-83.
[8]
Kjøller E, Køber L, Jørgensen S, Torp-Pedersen C. Trace Study Group. Short and long-term prognostic importance of regional dyskinesia versus akinesia in acute myocardial infarction. Heart 2002; 87: 410-4.
[9]
Nakjima K, Bunko H, Tada A, et al. Phase analysis in the Wolff-Parkinson-White syndrome with surgically proven accessory conduction pathways: Concise communication. J Nucl Med 1984; 25(1): 7-13.
[10]
Alhogbani T, Strohm O, Friedrich MG. Evaluation of left atrial contraction contribution to left ventricular filling using cardiovascular magnetic resonance. J Magn Reson Imaging 2013; 37(4): 860-4.
[11]
Che J, Garcia EV, Bax JJ, Iskandrian AE, Borges-Neto S, Soman P. SPECT myocardial perfusion imaging for the assessment of left ventricular mechanical dyssynchrony. J Nucl Cardiol 2011; 18(4): 685-94.
[12]
Brateman L, Buckley K, Keim SG, Wargovich TJ, Williams CM. Left ventricular regional wall motion assessment by radionuclide ventriculography: A comparison of cine display with Fourier imaging. J Nucl Med 1991; 32(5): 777-82.
[13]
Mahrholdt H, Zhydkov A, Hager S, et al. Left ventricular wall motion abnormalities as well as reduced wall thickness can cause false positive results of routine SPECT perfusion imaging for detection of myocardial infarction. Eur Heart J 2005; 26(20): 2127-35.
[14]
Boogers MM, Van Kriekinge SD, Henneman MM, et al. Quantitative gated SPECT-derived phase analysis on gated myocardial perfusion SPECT detects left ventricular dyssynchrony and predicts response to cardiac resynchronization therapy. Nucl Med May 2009; 50(5): 718-72.
[15]
Ortega-Alcalde D. Parametric images and Fourier analysis.Nuclear cardiology in everyday practice Springer. In: Candell-Riera J, Ortega-Alcalde D, Eds. Kluwer Academic Publishers 1994; pp. 173-86.
[16]
Muxí A, Paredes P, Mont L, et al. Left ventricular function and visual phase analysis with equilibrium radionuclide angiography in patients with biventricular device. Eur J Nucl Med Mol Imaging 2008; 35(5): 912-21.
[17]
Kim EY, Choe KO, Park CY, Kim MJ, Cho SY. Left ventricular regional wall motion assessment in myocardial infarction by phase analysis. Korean Circ J 1993; 23(2): 249-61.
[18]
Chen C, Li D, Miao C, et al. LV dyssynchrony as assessed by phase analysis of gated SPECT myocardial perfusion imaging in patients with Wolff-Parkinson-White syndrome. Eur J Nucl Med Mol Imaging 2012; 39(7): 1191-8.
[19]
Venouziou M, Zhang H. Characterizing the Hilbert transform by the bedrosian theorem. J Math Anal Appl 2008; 338: 1477-81.
[20]
Pugh EL. The generalized analytic signal. J Math Anal Appl 1982; 89(2): 674-99.
[21]
Venkitaraman A, Seelamantula CS. on computing amplitude, phase, and frequency modulations using a vector interpretation of the analytic signal. IEEE Signal Process Lett 2013; 20(12): 1187-90.
[22]
Benameur N, Caiani EG, Arous Y, Abdallah NB, Kraiem T. Interpretation of cardiac wall motion from Cine-MRI combined with parametric imaging based on the Hilbert transform. Magn Reson Mater Phy 2017; 30: 347-57.
[23]
Wachinger C, Klein T, Navab N. The 2D analytic signal for envelope detection and feature extraction on ultrasound images. Med Image Anal 2012; 16(6): 1073-84.
[24]
Cerqueira MD, Weissman NJ, Dilsizian V, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart:a statement for healthcare professionals for the cardiac imaging committee of the council on clinical cardiology of the American heart association. Circulation 2002; 105: 539-42.
[25]
Harley HRS. Cardiac ventricular aneurysm. Thorax 1969; 24(2): 148-72.
[26]
Tsadok Y, Petrank Y, Sarvari S, Edvard T, Adam D. Automatic segmentation of cardiac MRI cines validated for long axis views. Comput Med Imaging Graph 2013; 37(7): 500-11.
[27]
Lee HY, Codella NCF, Cham MD, Weinsaft JW, Wang Y. Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEEE Trans Biomed Eng 2010; 57(4): 905-13.
[28]
Ordas S, Boisrobert L, Huguet M, Frangi AF. Active shape models with invariant optimal features (IOF-ASM)–application to cardiac MRI segmentation. Comput Cardiol 2003; 30: 633-6.
[29]
Corsi C, Lamberti C, Catalano O, et al. Improved quantification of left ventricular volumes and mass based on endocardial and epicardial surface detection from cardiac MR Images using Level set models. J Cardiovasc Magn Reson 2005; 7(3): 595-602.
[30]
Xavier M, Lalande A, Walker PM, Brunotte F, Legrand L. An adapted optical flow algorithm for robust quantification of cardiac wall motion from standard cine-MR examinations. IEEE Trans Inf Technol Biomed 2012; 16(5): 859-68.
[31]
Jia K, Wang X, Tang X. Optical flow estimation using learned sparse model. In: IEEE International Conference on Computer Vision (ICCV). 2011 Nov 6-13; Barcelona, Spain. IEEE 2011.

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