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Image-Based Motion and Strain Estimation of the Vessel Wall

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Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

The estimation of vessel wall motion is valuable for characterising vessel status in health and disease. Following the periodic movement of the heart and the resulting blood pressure variations during the cardiac cycle, the vascular wall performs a complex three-dimensional motion. Wall motion can be quantified through the calculation of a number of kinematic parameters, including displacements, velocities, and accelerations, as well as strain, which has gained attention for characterising tissue function. To estimate vascular motion from images, different imaging modalities can be used, including ultrasound, magnetic resonance imaging (MRI) and computed tomography (CT). Among these, ultrasound imaging is the most widely used technique for vascular motion estimation, due to its wide availability, easy use, high temporal resolution, and possibility to access various central and peripheral vessels. Offering lower temporal but higher spatial resolutions than ultrasound, MRI and CT have been used mostly in aortic kinematics. In parallel to the development of sophisticated imaging and analysis methods, which will allow to reveal unexplored aspects of the complex kinematic phenomena of vascular tissue, the systematic application of image-based motion indices to clinical practice is crucial towards improving clinical decision making and ultimately public health.

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References

  1. Martini FH, Nath JL, Bartholomew EF (2017) Fundamentals of anatomy and physiology, 11th edn. Pearson, USA

    Google Scholar 

  2. Wagenseil JE, Mecham RP (2009) Vascular extracellular matrix and arterial mechanics. Physiol Rev 89:957–989

    Article  Google Scholar 

  3. Fung YC (1993) Biomechanics—mechanical properties of living tissues, 2nd edn. Springer, New York Inc

    Google Scholar 

  4. Nikita KS (2013) Atherosclerosis: the evolving role of vascular image analysis. Comput Med Imaging Graph 37(1):1–3

    Article  Google Scholar 

  5. Greenwald SE (2007) Ageing of the conduit arteries. J Pathol 211(2):157–172

    Article  Google Scholar 

  6. Mackenzie IS, Wilkinson IB, Cockroft JR (2002) Assessment of arterial stiffness in clinical practice. Q J Med 95:67–74

    Article  Google Scholar 

  7. Sarvazyan A, Hall TJ, Urban MW, Fatemi M, Aglyamov SR, Garra BS (2011) An overview of elastography—an emerging branch of medical imaging. Curr Med Imaging Rev 7(4):255–282

    Article  Google Scholar 

  8. Hoskins PR, Kenwright DA (2015) Recent developments in vascular ultrasound technology. Ultrasound 23(3):158–165

    Article  Google Scholar 

  9. Gennisson JL, Deffieux T, Fink M, Tanter M (2013) Ultrasound elastography: principles and techniques. Diagn Interv Imaging 94(5):487–495

    Article  Google Scholar 

  10. Tanter M, Fink M (2014) Ultrafast imaging in biomedical ultrasound. IEEE Trans Ultrason Ferroelectr Freq Contr 61(1):102–119

    Article  Google Scholar 

  11. Golemati S, Gastounioti A, Nikita KS (2016) Ultrasound-image-based cardiovascular tissue motion estimation. IEEE Rev Biomed Eng 9:208–218

    Article  Google Scholar 

  12. Wittek A, Karatolios K, Fritzen CP et al (2016) Cyclic three-dimensional wall motion of the human ascending and abdominal aorta characterized by time-resolved three-dimensional ultrasound speckle-tracking. Biomech Model Mechanobiol 15:1375–1388

    Article  Google Scholar 

  13. Luo J, Konofagou EE (2011) Imaging of wall motion coupled with blood flow velocity in the heart and vessels in vivo: a feasibility study. Ultrasound Med Biol 37(6):980–995

    Article  Google Scholar 

  14. van Disseldorp EM, Petterson NJ, Rutten MC, van de Vosse FN, van Sambeek MR, Lopata RG (2016) Patient specific wall stress analysis and mechanical characterization of abdominal aortic aneurysms using 4D ultrasound. Eur J Vasc Endovasc Surg 52(5):635–642

    Article  Google Scholar 

  15. de Korte CL, Carlier SG, Mastik F, Doyley MM, van der Steen AF, Serruys PW, Bom N (2002) Morphological and mechanical information of coronary arteries obtained with intravascular elastography; feasibility study in vivo. Eur Heart J 23(5):405–413

    Article  Google Scholar 

  16. Maurice RL, Fromageau J, Brusseau E, Finet G, Rioufol G, Cloutier G (2007) On the potential of the langrangian estimator for endovascular ultrasound elastography: in vivo human coronary artery study. Ultrasound Med Biol 33(8):1199–1205

    Article  Google Scholar 

  17. Deleaval F, Bouvier A, Finet G, Cloutier G, Yazdani SK, Le Floc’h S, Clarysse P, Pettigrew RI, Ohayon J (2013) The intravascular ultrasound elasticity-palpography technique revisited: a reliable tool for the in vivo detection of vulnerable coronary atherosclerotic plaques. Ultrasound Med Biol 39(8):1469–1481

    Article  Google Scholar 

  18. Tacheau A, Le Floc’h S, Finet G, Doyley MM, Pettigrew RI, Cloutier G, Ohayon J (2016) The imaging modulography technique revisited for high-definition intravascular ultrasound: theoretical framework. Ultrasound Med Biol 42(3):727–741

    Article  Google Scholar 

  19. Golemati S, Nikita KS (2018) Carotid artery wall motion and strain analysis using tracking. In: Loizou CP, Pattichis CS, D’houge J (eds) Handbook of speckle filtering and tracking in cardiovascular ultrasound imaging and video. IET

    Google Scholar 

  20. Golemati S, Sassano A, Lever MJ, Bharath AA, Nicolaides AN (2003) Carotid artery wall motion estimated from B-mode ultrasound using region tracking and block matching. Ultrasound Med Biol 29(3):387–399

    Article  Google Scholar 

  21. Cinthio M, Ahlgren AR, Bergkvist J, Jansson T, Persson HW, Lindström K (2006) Longitudinal movements and resulting shear strain of the arterial wall. Am J Physiol Heart Circ Physiol 291(1):H394–H402

    Article  Google Scholar 

  22. Zahnd G, Boussel L, Marion A, Durand M, Moulin P, Sérusclat A, Vray D (2011) Measurement of two-dimensional movement parameters of the carotid artery wall for early detection of arteriosclerosis: a preliminary clinical study. Ultrasound Med Biol 37(9):1421–1429

    Article  Google Scholar 

  23. Svedlund S, Eklund C, Robertsson P, Lomsky M, Gan LM (2011) Carotid artery longitudinal displacement predicts 1-year cardiovascular outcome in patients with suspected coronary artery disease. Arterioscler Thromb Vasc Biol 31(7):1668–1674

    Article  Google Scholar 

  24. Gastounioti A, Golemati S, Stoitsis JS, Nikita KS (2013) Carotid artery wall motion analysis from B-mode ultrasound using adaptive block matching: in silico evaluation and in vivo application. Phys Med Biol 58(24):8647–8661

    Article  Google Scholar 

  25. Tat J, Psaromiligkos IN, Daskalopoulou SS (2016) Carotid atherosclerotic plaque alters the direction of longitudinal motion in the artery wall. Ultrasound Med Biol 42(9):2114–2122

    Article  Google Scholar 

  26. Dempsey RJ, Varghese T, Jackson DC, Wang X, Meshram NH, Mitchell CC, Hermann BP, Johnson SC, Berman SE, Wilbrand SM (2017) Carotid atherosclerotic plaque instability and cognition determined by ultrasound-measured plaque strain in asymptomatic patients with significant stenosis. J Neurosurg 10:1–9

    Google Scholar 

  27. Sisini F, Tessari M, Gadda G, DiDomenico G, Taibi A, Menegatti E, Gambaccini M, Zamboni P (2015) An ultrasonographic technique to assess the jugular venous pulse: a proof of concept. Ultrasound Med Biol 41(5):1334–1341

    Article  Google Scholar 

  28. Herment A, Lefort M, Kachenoura N, DeCesare A, Taviani V, Graves MJ, Pellot-Barakat C, Frouin F, Mousseaux E (2011) Automated estimation of aortic strain from steady-state free-precession and phase contrast MR images. Magn Reson Med 65:986–993

    Article  Google Scholar 

  29. Franquet A, Avril S, LeRiche R, Badel P, Schneider F, Li Z, Boissier C, Favre J (2013) A new method for the in vivo identification of mechanical properties in arteries from cine MRI images: theoretical framework and validation. IEEE Trans Med Imaging 6:1–16

    Google Scholar 

  30. Suever JD, Watson PJ, Eisner RL, Lerakis S, O’Donnell RE, Oshinski JN (2011) Time-resolved analysis of coronary vein motion and cross-sectional area. J Magn Reson Imaging 34(4):811–815

    Article  Google Scholar 

  31. Ozturk C, Derbyshire JA, McVeigh ER (2003) Estimating motion from MRI data. Proc IEEE Inst Electr Electron Eng 9(10):1627–1648

    Article  Google Scholar 

  32. Manduca A, Oliphant TE, Dresner MA, Mahowald JL, Kruse SA, Amromin E, Felmlee JP, Greenleaf JF, Ehman RL (2001) Magnetic resonance elastography: non-invasive mapping of tissue elasticity. Med Image Anal 5(4):237–254

    Article  Google Scholar 

  33. Van der Geest RJ, Reiber JH (1999) Quantification in cardiac MRI. J Magn Reson Imaging 10(5):602–608

    Google Scholar 

  34. McVeigh ER (1996) MRI of myocardial function: motion tracking techniques. Magn Reson Imaging 14(2):137–150

    Article  Google Scholar 

  35. Aletras AH, Ding S, Balaban RS, Wen H (1999) DENSE: displacement encoding with stimulated echoes in cardiac functional MRI. J Magn Reson 137:247–252

    Article  Google Scholar 

  36. Abd-Elmoniem KZ, Sampath S, Osman NF, Prince JL (2007) Real-time monitoring of cardiac regional function using fastHARP MRI and region-of-interest reconstruction. IEEE Trans Biomed Eng 54(9):1650–1656

    Article  Google Scholar 

  37. Wedding KL, Draney MT, Herfkens RJ, Zarins CK, Taylor CA, Pelc NJ (2002) Measurement of vessel wall strain using cine phase contrast MRI. J Magn Reson Imaging 15(4):418–428

    Article  Google Scholar 

  38. Lin AP, Bennett E, Wisk LE, Gharib M, Fraser SE, Wen H (2008) Circumferential strain in the wall of the common carotid artery: comparing displacement-encoded and cine MRI in volunteers. Magn Reson Med 60(1):8–13

    Article  Google Scholar 

  39. Krishnan K, Ge L, Haraldsson H, Hope MD, Saloner DA, Guccione JM, Tseng EE (2015) Ascending thoracic aortic aneurysm wall stress analysis using patient-specific finite element modeling of in vivo magnetic resonance imaging. Interact CardioVasc Thorac Surg 21(4):471–480

    Article  Google Scholar 

  40. Xu L, Chen J, Glaser KJ, Yin M, Rossman PJ, Ehman RL (2013) MR elastography of the human abdominal aorta: a preliminary study. J Magn Reson Imaging 38:1549–1553

    Article  Google Scholar 

  41. Kolipaka A, Woodrum D, Araoz PA, Ehman RL (2012) MR elastography of the in vivo abdominal aorta: a feasibility study for comparing aortic stiffness between hypertensives and normotensives. J Magn Reson Imaging 35(3):582–586

    Article  Google Scholar 

  42. Litmanovich D, Bankier AA, Cantin L, Raptopoulos V, Boiselle PM (2009) CT and MRI in diseases of the aorta. Am J Roentgenol 193(4):928–940

    Article  Google Scholar 

  43. Cesare E, Splendiani A, Barile A, Squillaci E, Cesare A, Brunese L, Masciocchi C (2016) CT and MR imaging of the thoracic aorta. Open Med (Wars) 11(1):143–151

    Google Scholar 

  44. Lin E, Alessio A (2009) What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT? J Cardiovasc Comput Tomogr 3(6):403–408

    Article  Google Scholar 

  45. Morrison T, Choi G, Zarins CK, Taylor CA (2009) Circumferential and longitudinal cyclic strain of the human thoracic aorta: age-related changes. J Vasc Surg 49:1029–1036

    Article  Google Scholar 

  46. Schlicht MS, Khanafer K, Duprey A, Cronin P, Berguer R (2013) Experimental foundation for in vivo measurement of the elasticity of the aorta in computed tomography angiography. Eur J Vasc Endovasc Surg 46:447–452

    Article  Google Scholar 

  47. Martin C, Sun W, Primiano C, McKay R, Elefteriades J (2013) Age-dependent ascending aorta mechanics assessed through multiphase CT. Ann Biomed Eng 41:2565–2574

    Article  Google Scholar 

  48. Weber TF, Müller T, Biesdorf A, Wörz S, Rengier F, Heye T, Holland-Letz T, Rohr K, Kauczor HU, von Tengg-Kobligk H (2014) True four-dimensional analysis of thoracic aortic displacement and distension using model-based segmentation of computed tomography angiography. Int J Cardiovasc Imaging 30:185–194

    Article  Google Scholar 

  49. Lee MK, Holdsworth DW, Fenster A (2000) Dynamic 3D computed tomography: non-invasive method for determination of the aortic dynamic elastic modulus. In: Proceedings of the 22nd annual international conference of the IEEE engineering in medicine and biology society

    Google Scholar 

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Correspondence to Spyretta Golemati .

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Golemati, S., Patelaki, E., Nikita, K.S. (2019). Image-Based Motion and Strain Estimation of the Vessel Wall. In: Golemati, S., Nikita, K. (eds) Cardiovascular Computing—Methodologies and Clinical Applications. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-5092-3_9

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  • DOI: https://doi.org/10.1007/978-981-10-5092-3_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5091-6

  • Online ISBN: 978-981-10-5092-3

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