The online version of this article (doi:10.1186/s12880-017-0195-7) contains supplementary material, which is available to authorized users.
Tagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images.
CIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (C B ) and search box size (S B ), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset (http://stacom.cardiacatlas.org/motion-tracking-challenge/) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique.
For a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 × 512 image with (C B ,S B )=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (∼4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP.
In summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting.
Nesto RW, Kowalchuk GJ. The ischemic cascade: temporal sequence of hemodynamic, electrocardiographic and symptomatic expressions of ischemia. Am J Cardiol. 1987; 59(7):23–30. CrossRef
Ohyama Y, Volpe GJ, Lima JA. Subclinical myocardial disease in heart failure detected by cmr. Curr Cardiovasc Imaging Rep. 2014; 7(6):1–10. CrossRef
Edvardsen T, Gerber BL, Garot J, Bluemke DA, Lima JAC, Smiseth OA. Quantitative assessment of intrinsic regional myocardial deformation by doppler strain rate echocardiography in humans: Validation against three-dimensional tagged magnetic resonance imaging. Circulation. 2002; 106(1):50–6. doi: 10.1161/01.CIR.0000019907.77526.75. CrossRefPubMed
Maret E, Todt T, Brudin L, Nylander E, Swahn E, Ohlsson JL, Engvall JE. Functional measurements based on feature tracking of cine magnetic resonance images identify left ventricular segments with myocardial scar. Cardiovasc Ultrasound. 2009; 7(1):1. CrossRef
Hor KN, Gottliebson WM, Carson C, Wash E, Cnota J, Fleck R, Wansapura J, Klimeczek P, Al-Khalidi HR, Chung ES, et al.Comparison of magnetic resonance feature tracking for strain calculation with harmonic phase imaging analysis. JACC: Cardiovasc Imaging. 2010; 3(2):144–51.
Young AA, Axel L. Three-dimensional motion and deformation of the heart wall: estimation with spatial modulation of magnetization–a model-based approach. Radiology. 1992; 185(1):241–7. doi: 10.1148/radiology.185.1.1523316. PMID: 1523316. PMID: 1523316 CrossRefPubMed
Clark NR, Reichek N, Bergey P, Hoffman EA, Brownson D, Palmon L, Axel L. Circumferential myocardial shortening in the normal human left ventricle. assessment by magnetic resonance imaging using spatial modulation of magnetization. Circulation. 1991; 84(1):67–74. doi: 10.1161/01.CIR.84.1.67. CrossRefPubMed
McVeigh ER, Zerhouni EA. Noninvasive measurement of transmural gradients in myocardial strain with mr imaging. Radiology. 1991; 180(3):677–83. doi: 10.1148/radiology.180.3.1871278. PMID: 1871278. PMID: 1871278 CrossRefPubMedPubMedCentral
Lugo-Olivieri C, Moore C, Guttman M, Lima J, McVeigh E, Zerhouni E. The effects of ischemia on the temporal evolution of radial myocardial deformation in humans. Radiology. 1994; 193(P):161.
Lima JA, Ferrari VA, Reichek N, Kramer CM, Palmon L, Llaneras MR, Tallant B, Young AA, Axel L. Segmental motion and deformation of transmurally infarcted myocardium in acute postinfarct period. Am J Physiol Heart Circ Physiol. 1995; 268(3):1304–1312.
Croisille P, Judd RM, Lima JAC, Moore C, Arai M, Lugoolivieri C, Zerhouni EA. Combined dobutamine stress 3d tagged and contrast-enhanced mri differentiate viable from nonviable myocardium after acute infarction and reperfusion. In: Circulation (Vol. 92, No. 8). Dallas: Amer Heart Assoc.: 1995. p. 2426–2426.
Osman NF, Kerwin WS, Mcveigh ER, Prince JL. Cardiac motion tracking using cine harmonic phase (harp) magnetic resonance imaging. Mag. Reson. Med. 1999; 42:1048–1060. CrossRef
Prasad AK. Particle image velocimetry. Curr Sci. 2000; 79(1):51–60.
Sommeria J. UVMAT. http://servforge.legi.grenoble-inp.fr/projects/soft-uvmat/.
Tobon-Gomez C, Craene MD, McLeod K, Tautz L, Shi W, Hennemuth A, Prakosa A, Wang H, Carr-White G, Kapetanakis S, Lutz A, Rasche V, Schaeffter T, Butakoff C, Friman O, Mansi T, Sermesant M, Zhuang X, Ourselin S, Peitgen HO, Pennec X, Razavi R, Rueckert D, Frangi AF, Rhode KS. Benchmarking framework for myocardial tracking and deformation algorithms: An open access database. Med Image Anal. 2013; 17(6):632–48. CrossRefPubMed
McLeod K, Prakosa A, Mansi T, Sermesant M, Pennec X. An incompressible log-domain demons algorithm for tracking heart tissue. In: International Workshop on Statistical Atlases and Computational Models of the Heart. Berlin: Springer: 2011. p. 55–67.
Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, Pennell DJ, Rumberger JA, Ryan T, Verani MS, 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. Circulation. 2002; 105(4):539–42. CrossRefPubMed
Jacob AJ, Alex V, Krishnamurthi G. Segmentation and tracking of myocardial boundaries using dynamic programming. In: International Workshop on Statistical Atlases and Computational Models of the Heart. Cham: Springer: 2016. p. 118–26.
- Estimation of myocardial deformation using correlation image velocimetry
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