Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

BackgroundT2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.MethodsWe describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.ResultsThe mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.ConclusionCompared to standard manual approaches, the new highly automatic method for estimating myocardial oedema is accurate and straightforward. It has potential as a generic software tool for physicians to use in clinical practice.

[1]  Nikos Maglaveras,et al.  Detection and modeling of infarcted myocardium regions in MRI images using a contour deformable model , 1995, Computers in Cardiology 1995.

[2]  E. McVeigh,et al.  Phase‐sensitive inversion recovery for detecting myocardial infarction using gadolinium‐delayed hyperenhancement † , 2002, Magnetic resonance in medicine.

[3]  R. F. Hoyt,et al.  Cardiac magnetic resonance imaging , 2004, Postgraduate Medical Journal.

[4]  Einar Heiberg,et al.  Myocardium at risk after acute infarction in humans on cardiac magnetic resonance: quantitative assessment during follow-up and validation with single-photon emission computed tomography. , 2009, JACC. Cardiovascular imaging.

[5]  Daniel Rueckert,et al.  Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm , 2003, MICCAI.

[6]  K Kadir,et al.  LV wall segmentation using the variational level set method (LSM) with additional shape constraint for oedema quantification , 2012, Physics in medicine and biology.

[7]  Caroline Petitjean,et al.  A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..

[8]  Marcel Breeuwer,et al.  Automatic cardiac contour propagation in short axis cardiac MR images , 2005 .

[9]  P. Kellman,et al.  Magnetic Resonance Imaging Delineates the Ischemic Area at Risk and Myocardial Salvage in Patients With Acute Myocardial Infarction , 2010, Circulation. Cardiovascular imaging.

[10]  Matthias G. Friedrich,et al.  Auto‐Threshold quantification of late gadolinium enhancement in patients with acute heart disease , 2013, Journal of magnetic resonance imaging : JMRI.

[11]  P. Myerowitz,et al.  Effects of hypoproteinemia-induced myocardial edema on left ventricular function. , 1998, American journal of physiology. Heart and circulatory physiology.

[12]  Marcel Breeuwer,et al.  Automatic myocardium segmentation in late-enhancement MRI , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  Gilles Bertrand,et al.  Topology preserving alternating sequential filter for smoothing two-dimensional and three-dimensional objects , 2004, J. Electronic Imaging.

[14]  O. Simonetti,et al.  An improved MR imaging technique for the visualization of myocardial infarction. , 2001, Radiology.

[15]  D. Pennell Myocardial salvage: retrospection, resolution, and radio waves. , 2006, Circulation.

[16]  Einar Heiberg,et al.  Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance , 2012, Journal of Cardiovascular Magnetic Resonance.

[17]  Richard D. White,et al.  Quantitative assessment of myocardial scar in delayed enhancement magnetic resonance imaging , 2003, Journal of magnetic resonance imaging : JMRI.

[18]  J. Schulz-Menger,et al.  Delayed Enhancement and T2-Weighted Cardiovascular Magnetic Resonance Imaging Differentiate Acute From Chronic Myocardial Infarction , 2004, Circulation.

[19]  Daniel Rueckert,et al.  Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm , 2004, Medical Image Anal..

[20]  Ryszard S. Choraś Image Processing and Communications Challenges 3 - 3rd International Conference, IP&C 2011, Proceedings , 2011, IP&C.

[21]  R. Kim,et al.  Contrast-enhanced magnetic resonance imaging of myocardium at risk: distinction between reversible and irreversible injury throughout infarct healing. , 2000, Journal of the American College of Cardiology.

[22]  John J Soraghan,et al.  Automatic quantification of oedema from T2 weighted CMR image using a Hybrid Thresholding Oedema Sizing Algorithm (HTOSA) , 2010, 2010 Computing in Cardiology.

[23]  S. Petersen,et al.  Comparing analysis methods for quantification of myocardial oedema in patients following reperfused ST-elevation MI , 2011, Journal of Cardiovascular Magnetic Resonance.

[24]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[25]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[26]  Andrew E Arai,et al.  In Vivo T2-Weighted Magnetic Resonance Imaging Can Accurately Determine the Ischemic Area at Risk for 2-Day-Old Nonreperfused Myocardial Infarction , 2008, Investigative radiology.

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

[28]  V. Wright,et al.  Bright-Blood T2-Weighted MRI Has High Diagnostic Accuracy for Myocardial Hemorrhage in Myocardial Infarction: A Preclinical Validation Study in Swine , 2011, Circulation. Cardiovascular imaging.

[29]  Peter Kellman,et al.  ACUT2E TSE‐SSFP: A hybrid method for T2‐weighted imaging of edema in the heart , 2008, Magnetic resonance in medicine.

[30]  P. Kellman,et al.  Quantitative myocardial infarction on delayed enhancement MRI. Part I: Animal validation of an automated feature analysis and combined thresholding infarct sizing algorithm , 2006, Journal of magnetic resonance imaging : JMRI.

[31]  C. Berry,et al.  Bright-Blood T2-Weighted MRI Has Higher Diagnostic Accuracy Than Dark-Blood Short Tau Inversion Recovery MRI for Detection of Acute Myocardial Infarction and for Assessment of the Ischemic Area at Risk and Myocardial Salvage , 2011, Circulation. Cardiovascular imaging.

[32]  Colin Berry,et al.  Automatic Left Ventricle Segmentation in T2 Weighted CMR Images , 2010, IP&C.

[33]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[34]  Khaoula Elagouni,et al.  Automatic segmentation of pathological tissues in cardiac MRI , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[35]  A Radjenovic,et al.  Assessment of tissue edema in patients with acute myocardial infarction by computer‐assisted quantification of triple inversion recovery prepared MRI of the myocardium , 2011, Magnetic resonance in medicine.

[36]  P. Kellman,et al.  Myocardial edema as detected by pre-contrast T1 and T2 CMR delineates area at risk associated with acute myocardial infarction. , 2012, JACC. Cardiovascular imaging.

[37]  J. Gili,et al.  Analysis of myocardial oedema by magnetic resonance imaging early after coronary artery occlusion with or without reperfusion. , 1993, Cardiovascular research.

[38]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.