1. Smiseth, OA, Torp, H, Opdahl, A, Haugaa, KH, Urheim, S. Myocardial strain imaging: how useful is it in clinical decision making? Eur Heart J. 2016;37(15):1196-207.
Google Scholar | Crossref | Medline2. Potter, E, Marwick, TH. Assessment of left ventricular function by echocardiography. JACC Cardiovasc Imaging. 2018;11(2):260-74.
Google Scholar | Crossref | Medline3. Fabiani, I, Pugliese, NR, Santini, V, Conte, L, Di Bello, V. Speckle-tracking imaging, principles and clinical applications: a review for clinical cardiologists. In: Lakshmanadoss, U , ed. Echocardiography in Heart Failure and Cardiac Electrophysiology. InTech; Rijeka, 2016, pp. 85-114. http://www.intechopen.com/books/echocardiography-in-heart-failure-and-cardiac-electrophysiology/speckle-tracking-imaging-principles-and-clinical-applications-a-review-for-clinical-cardiologists
Google Scholar | Crossref4. Mondillo, S, Galderisi, M, Mele, D, Cameli, M, Lomoriello, VS, Zacà, V, et al. Speckle-Tracking echocardiography. J Ultrasound Med. 2011;30(1):71-83.
Google Scholar | Crossref | Medline | ISI5. Farsalinos, KE, Daraban, AM, Ünlü, S, Thomas, JD, Badano, LP, Voigt, J-U. Head-to-Head comparison of global longitudinal strain measurements among nine different vendors. J Am Soc Echocardiogr. 2015;28(10):1171-81.e2.
Google Scholar | Crossref | Medline6. D’hooge, J, Barbosa, D, Gao, H, Claus, P, Prater, D, Hamilton, J, et al. Two-dimensional speckle tracking echocardiography: standardization efforts based on synthetic ultrasound data. Eur Heart J – Cardiovasc Imaging. 2016;17(6):693-701.
Google Scholar | Crossref | Medline7. Thomas, JD, Badano, LP. EACVI-ASE-industry initiative to standardize deformation imaging: a brief update from the co-chairs. Eur Heart J – Cardiovasc Imaging. 2013;14(11):1039-40.
Google Scholar | Crossref8. Castel, A-L, Menet, A, Ennezat, P-V, Delelis, F, Le Goffic, C, Binda, C, et al. Global longitudinal strain software upgrade: implications for intervendor consistency and longitudinal imaging studies. Arch Cardiovasc Dis. 2016;109(1):22-30.
Google Scholar | Crossref | Medline9. Negishi, K, Negishi, T, Kurosawa, K, Hristova, K, Popescu, BA, Vinereanu, D, et al. Practical guidance in echocardiographic assessment of global longitudinal strain. JACC Cardiovasc Imaging. 2015;8(4):489-92.
Google Scholar | Crossref | Medline10. Costa, SP, Beaver, TA, Rollor, JL, Vanichakarn, P, Magnus, PC, Palac, RT. Quantification of the variability associated with repeat measurements of left ventricular two-dimensional global longitudinal strain in a real-world setting. J Am Soc Echocardiogr. 2014;27(1):50-4.
Google Scholar | Crossref | Medline11. Mirea, O, Pagourelias, ED, Duchenne, J, Bogaert, J, Thomas, JD, Badano, LP, et al. Variability and reproducibility of segmental longitudinal strain measurement. JACC Cardiovasc Imaging. 2018;11(1):15-24.
Google Scholar | Crossref | Medline12. Voigt, J-U, Pedrizzetti, G, Lysyansky, P, Marwick, TH, Houle, H, Baumann, R, et al. Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. Eur Heart J – Cardiovasc Imaging. 2015;16(1):1-11.
Google Scholar | Crossref | Medline13. Cygan, S, Kumor, M, Zmigrodzki, J, Leśniak-Plewińska, B, Kowalski, M, Kałuzyński, K. Left ventricular phantoms with inclusions simulating transmural and non-transmural infarctions: FEM and EchoPAC study. In: Duric, N, Heyde, B, eds. Progress in Biomedical Optics and Imaging – Proceedings of SPIE. SPIE, Bellingham, WA, 2017, P. 1013918. http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2254350
Google Scholar | Crossref14. Xu, L, Wang, N, Chen, X, Liang, Y, Zhou, H, Yan, J. Quantitative evaluation of myocardial layer-specific strain using two-dimensional speckle tracking echocardiography among young adults with essential hypertension in China. Medicine. 2018;97(39):e12448.
Google Scholar | Crossref | Medline15. Chakraborty, B, Zhi, L, Heyde, B, Luo, J, D’hooge, J. 2D RF-based non-rigid image registration for cardiac motion estimation: comparison against block matching. In: 2016 IEEE International Ultrasonics Symposium (IUS), Tours, 2016, pp. 1–4. http://ieeexplore.ieee.org/document/7728621/
Google Scholar | Crossref16. Żmigrodzki, J, Cygan, S, Leśniak-Plewińska, B, Kowalski, M, Kałużyński, K. Effect of transmural extent of the simulated infarction in a left ventricular model on displacement and strain distribution estimated from synthetic ultrasonic data. Ultrasound Med Biol. 2017;43(1):206-17.
Google Scholar | Crossref | Medline17. Żmigrodzki, J, Cygan, S, Kałużyński, K. Evaluation of strain averaging area and strain estimation errors in a spheroidal left ventricular model using synthetic image data and speckle tracking. BMC Med Imaging. 2021;21(1):105.
Google Scholar | Crossref | Medline18. Dalen, H, Thorstensen, A, Aase, SA, Ingul, CB, Torp, H, Vatten, LJ, et al. Segmental and global longitudinal strain and strain rate based on echocardiography of 1266 healthy individuals: the HUNT study in Norway. Eur Heart J – Cardiovasc Imaging. 2010;11(2):176-83.
Google Scholar | Crossref19. van Mourik, MJW, Zaar, DVJ, Smulders, MW, Heijman, J, Lumens, J, Dokter, JE, et al. Adding speckle-tracking echocardiography to visual assessment of systolic wall motion abnormalities improves the detection of myocardial infarction. J Am Soc Echocardiogr. 2019;32(1):65-73.
Google Scholar | Crossref | Medline20. Collier, P, Phelan, D, Klein, A. A test in context: myocardial strain measured by speckle-tracking echocardiography. J Am Coll Cardiol. 2017;69:1043-56.
Google Scholar | Crossref | Medline21. Alessandrini, M, Heyde, B, Queiros, S, Cygan, S, Zontak, M, Somphone, O, et al. Detailed evaluation of five 3D speckle tracking algorithms using synthetic echocardiographic recordings. IEEE Trans Med Imaging. 2016;35(8):1915-26.
Google Scholar | Crossref22. Larsson, M, Kremer, F, Claus, P, Brodin, L-A, D’hooge, J. A novel measure to express tracking quality in ultrasound block matching. In: 2010 IEEE International Ultrasonics Symposium, San Diego, CA, USA, 2010, pp. 1636–9, http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5935715&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5935715
Google Scholar23. Heyde, B, Alessandrini, M, Hermans, J, Barbosa, D, Claus, P, D’hooge, J. Anatomical image registration using volume conservation to assess cardiac deformation from 3D ultrasound recordings. IEEE Trans Med Imaging. 2016;35:501-11.
Google Scholar | Crossref24. Lu, C, Chelikani, S, Papademetris, X, Knisely, JP, Milosevic, MF, Chen, Z, et al. An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy. Med Image Anal. 2011;15(5):772-85.
Google Scholar | Crossref | Medline25. Elmahdy, MS, Wolterink, JM, Sokooti, H, Išgum, I, Staring, M. Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy. In: Shen, D , et al. eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11769. Springer, Cham, 2019, pp. 366-74. http://arxiv.org/abs/1906.12223
Google Scholar | Crossref26. Parajuli, N, Compas, CB, Lin, BA, Sampath, S, O’Donnell, M, Sinusas, AJ, et al. Sparsity and biomechanics inspired integration of shape and speckle tracking for cardiac deformation analysis. In: van Assen, H, Bovendeerd, P, Delhaas, T, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, Cham; 2015, pp. 57-64. https://pubmed.ncbi.nlm.nih.gov/27976753/
Google Scholar | Crossref27. Parajuli, N, Lu, A, Stendahl, JC, Zontak, M, Boutagy, N, Eberle, M, et al. Integrated dynamic shape tracking and RF speckle tracking for cardiac motion analysis. In: Ourselin, S, Joskowicz, L, Sabuncu, M, Unal, G, Wells, W, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, Cham; 2016, pp. 431-8. https://link.springer.com/chapter/10.1007/978-3-319-46720-7_50
Google Scholar | Crossref28. Compas, CB, Wong, EY, Huang, X, Sampath, S, Lin, BA, Pal, P, et al. Radial basis functions for combining shape and speckle tracking in 4D echocardiography. IEEE Trans Med Imaging. 2014;33(6):1275-89.
Google Scholar | Crossref | Medline29. Compas, CB, Lin, BA, Sampath, S, Huang, L, Wei, Q, Sinusas, AJ, et al. Comparing shape tracking, speckle tracking, and a combined method for deformation analysis in echocardiography. In: Proceedings – 2011 1st IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2011, San Jose, CA, 2011, pp. 120-5.
Google Scholar | Crossref30. Mahapatra, D, Sun, Y. Integrating segmentation information for improved mrf-based elastic image registration. IEEE Trans Image Process. 2012;21(1):170-83.
Google Scholar | Crossref | Medline31. Liu, J, Banchs, J, Mousavi, N, Plana, JC, Scherrer-Crosbie, M, Thavendiranathan, P, et al. Contemporary role of echocardiography for clinical decision making in patients during and after cancer therapy. JACC Cardiovasc Imaging. 2018;11:1122-31.
Google Scholar | Crossref32. Bursi, F, Santangelo, G, Sansalone, D, Valli, F, Vella, AM, Toriello, F, et al. Prognostic utility of quantitative offline 2D-echocardiography in hospitalized patients with COVID-19 disease. Echocardiography. 2020;37(12):2029-39.
Google Scholar | Crossref33. Gallard, A, Galli, E, Hubert, A, Bidaut, A, Le Rolle, V, Smiseth, O, et al. Echocardiographic view and feature selection for the estimation of the response to CRT. PLoS One. 2021;16:e0252857.
Google Scholar | Crossref | Medline34. Doherty, JU, Kort, S, Mehran, R, Schoenhagen, P, Soman, P, Panel, R, et al. ACC/AATS/AHA/ASE/ASNC/HRS/SCAI/SCCT/SCMR/STS 2019 appropriate use criteria for multimodality imaging in the assessment of cardiac structure and function in nonvalvular heart disease. J Am Soc Echocardiogr. 2019;32:553-79.
Google Scholar | Crossref | Medline35. Zmigrodzki, J, Cygan, S, Wilczewska, A, Kaluzynski, K. Quantitative assessment of the effect of the out-of-plane movement of the homogenous ellipsoidal model of the left ventricle on the deformation measures estimated using 2D speckle tracking: an in-silico study. IEEE Trans Ultrason Ferroelectr Freq Control. 2018;65(10):1789-803.
Google Scholar | Crossref | Medline36. Cygan, S, Żmigrodzki, J, Leśniak-Plewińska, B, Karny, M, Pakieła, Z, Kałużyński, K. Influence of polivinylalcohol cryogel material model in FEM simulations on deformation of LV phantom. In: van Assen, H, Bovendeerd, P and, Delhaas, T, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9126. Springer, Cham; 2015, pp. 313-20. http://link.springer.com/chapter/10.1007/978-3-319-20309-6_36
Google Scholar | Crossref37. Heyde, B, Cygan, S, Choi, HF, Lesniak-Plewinska, B, Barbosa, D, Elen, A, et al. Regional cardiac motion and strain estimation in three-dimensional echocardiography: a validation study in thick-walled univentricular phantoms. IEEE Trans Ultrason Ferroelectr Freq Control. 2012;59(4):668-82.
Google Scholar | Crossref | Medline38. Lesniak-Plewinska, B, Cygan, S, Kaluzynski, K, D’hooge, J, Zmigrodzki, J, Kowalik, E, et al. A dual-chamber, thick-walled cardiac phantom for use in cardiac motion and deformation imaging by ultrasound. Ultrasound Med Biol. 2010;36(7):1145-56.
Google Scholar | Crossref | Medline39. Cygan, S. Modelowanie numeryczne fantomów serca na potrzeby obrazowania odkształceń w echokardiografii (Numerical modeling of heart phantoms as a support for strain imaging in echocardiography). Warsaw: Akademicka Oficyna Wydawnicza EXIT; 2019. http://www.exit.pl/cyg.htm
Google Scholar40. Rösner, A, Barbosa, D, Aarsæther, E, Kjønås, D, Schirmer, H, D’hooge, J. The influence of frame rate on two-dimensional speckle-tracking strain measurements: a study on silico-simulated models and images recorded in patients. Eur Heart J – Cardiovasc Imaging. 2015;16(10):1137-47.
Google Scholar | Crossref41. Jensen, JA, Svendsen, NB. Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE Trans Ultrason Ferroelectr Freq Control. 1992;39(2):262-7.
Google Scholar | Crossref42. Jensen, JA. FIELD: a program for simulating ultrasound systems. In: 10th Nordicbaltic Conference on Biomedical Imaging, Tampere, 1996, vol. 4, pp. 351-3, http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=BD378D7B812E35F8807F13B09BC92D5D?doi=10.1.1.50.4778&rep=rep1&type=pdf
Google Scholar43. Leclerc, S, Smistad, E, Pedrosa, J, Ostvik, A, Cervenansky, F, Espinosa, F, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans Med Imaging. 2019;38(9):2198-210.
Google Scholar | Crossref | Medline44. Hu, Y, Xia, B, Mao, M, Jin, Z, Du, J, Guo, L, et al. AIDAN: an attention-guided dual-path network for pediatric echocardiography segmentation. IEEE Access. 2020;8:29176-87.
Google Scholar | Medline45. Alessandrini, M, Chakraborty, B, Heyde, B, Bernard, O, De Craene, M, Sermesant, M, et al. Realistic vendor-specific synthetic ultrasound data for quality assurance of 2d speckle tracking echocardiography: simulation pipeline and open access database. IEEE Trans Ultrason Ferroelectr Freq Control. 2017;65(3):411-22.
Google Scholar | Crossref46. Kybic, J, Unser, M. Fast parametric elastic image registration. IEEE Trans Image Process. 2003;12(11):1427-42.
Google Scholar | Crossref | Medline47. Wilczewska, A, Cygan, S, Żmigrodzki, J. Displacement field estimation for echocardiography strain imaging using B-spline based elastic image registration: synthetic data study. In: Jabłoński, R and, Szewczyk, R, eds. Recent Global Research and Education: Technological Challenges. Springer International Publishing; Warsaw, 2017, pp. 309-15. http://link.springer.com/10.1007/978-3-319-46490-9_42
Google Scholar | Crossref48. Rueckert, D, Sonoda, LI, Hayes, C, Hill, DL, Leach, MO, Hawkes, DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999; 18(8):712-21.
Google Scholar | Crossref | Medline49. Keys, R. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust. 1981;29(6):1153-60.