Bernardo, Agustín (2020) Estimación del esfuerzo miocárdico a partir del procesado de imágenes médicas / Myocardial strain measuring through medical image processing. Maestría en Física Médica, Universidad Nacional de Cuyo, Instituto Balseiro.
![[img]](/style/images/fileicons/application_pdf.png)
| PDF (Tesis) Español 9Mb |
Resumen en español
Las enfermedades cardiovasculares representan el 31% de las muertes anuales en todo el mundo. La detección temprana de disfunciones ventriculares, a través del estudio de la función miocárdica, es de vital importancia para la calidad de vida de aquellas personas que padecen de enfermedades cardiovasculares. La función miocárdica puede estudiarse a través de diversos parámetros. Entre ellos, el esfuerzo y la tasa de deformación del miocardio son los objetos de estudio de este trabajo. Se diseño, desarrolló, implementó y validó una herramienta denominada CardIAc Strain Analysis, que permite cuanticar el esfuerzo miocárdico y la tasa de deformaci ón en imágenes MR-C de eje corto, utilizando técnicas de Feature Tracking. Dicha aplicación se desarrolló bajo una licencia de código abierto, utilizando el framework 3D Slicer. Además, se realizó la integración de la misma con una herramienta de segmentaci ón semántica (CardIAc AI Segmentation) que funciona a través de inteligencia articial. Los resultados obtenidos muestran que la técnica propuesta permite estimar de forma adecuada el movimiento de los puntos materiales del ventrículo izquierdo, con un error cuya mediana es de 3.66 mm para la base de datos de quince estudios en voluntarios sanos cMAC-STACOM, y un error cuya mediana es de 2.98 mm en una base de datos sintética simulada a partir de la anterior. Se analizaron las curvas de esfuerzo medidas y los valores obtenidos para el esfuerzo circunferencial y longitudinal se corresponden con los indicados en la bibliografía y con otros grupos de investigación. Se obtuvo, en la base de datos sintética, un error medio de 4.07 %, 5.76% y 8.19% para el esfuerzo circunferencial, radial y longitudinal respectivamente. Los resultados cualitativos y cuantitativos muestran que CardIAc presenta un alto potencial para computar el esfuerzo miocárdico y la tasa de deformación en imágenes MR-C SAX, especialmente para el esfuerzo longitudinal y circunferencial.
Resumen en inglés
Cardiovascular diseases represent 31% of all deaths globally. Early diagnostic of cardiac insufficiencies, through the study of myocardial function, is of utmost importance to improve the life quality of people suffering cardiovascular diseases. Myocardial function can be characterized using a range of parameters. Specically, strain and strain rate are the main subjects of study in this work. An open source software (CardIAc Strain Analysis) that allows to quantify myocardial strain and strain rate in CMR images was designed, developed, implemented and validated, using Feature Tracking techniques. 3D Slicer was used as a framework to develop and distribute the user interface. Also, an integration between the proposed tool and an automatic myocardial segmentation tool (CardIAc AI Segmentation) was performed. Obtained results show that the proposed technique allows for an adequate measurement of the cardiac motion, with a median error of 3.66 mm for the fteen healthy volunteer database cMAC-STACOM and a median error of 2.98 mm for a synthetic database simulated from cMAC-STACOM images. Measured strain curves were analyzed and obtained values for circumferential and longitudinal strain agree with the values reported in the bibliography and those measured by other research groups. In the synthetic database an analysis of measured strain was performed and mean errors of 4.09%, 5.76% and 8.19% were obtained for circumferential, radial and longitudinal strain. Quantitative and qualitative results show that CardIAc presents a high potential to compute the myocardial strain and strain rate from SAX MR-C images, specially for circumferential and longitudinal strain.
| Tipo de objeto: | Tesis (Maestría en Física Médica) |
|---|---|
| Palabras Clave: | Heart disease; Enfermedad del corazón; [Myocardil; Miocardio; Strain; Esfuerzo; Displacement; Deformación; Software] |
| Referencias: | [1] IHME. Global burden of disease study 2017. 1 [2] WHO. Cardiovascular disease facts, 2016. URL https://www.who.int/ health-topics/cardiovascular-diseases. 1 [3] Frangi, A. F., Niessen, W. J., Viergever, M. A. Three-dimensional modeling for functional analysis of cardiac images, a review. IEEE Transactions on Medical Imaging, 20 (1), 2{5, Jan 2001. 1, 9, 10 [4] Messas, E., Guerrero, J. L., Handschumacher, M. D., Chow, C.-M., Sullivan, S., Schwammenthal, E., et al. Paradoxic decrease in ischemic mitral regurgitation with papillary muscle dysfunction. Circulation, 104 (16), 1952{1957, 2001. 2 [5] Yeon, S., Reichek, N., Tallant, B., Lima, J., Calhoun, L., Clark, N., et al. Validation of in vivo myocardial strain measurement by magnetic resonance tagging with sonomicrometry. Journal of the American College of Cardiology, 38, 555{61, 09 2001. 2 [6] Suffoletto, M. S., Dohi, K., Cannesson, M., Saba, S., Gorcsan, J. Novel speckletracking radial strain from routine black-and-white echocardiographic images to quantify dyssynchrony and predict response to cardiac resynchronization therapy. Circulation, 113 (7), 960{968, 2006. 2 [7] Dellazzoppa, L. Un enfoque de aprendizaje profundo para la cuantificación del la función cardíaca, 2020. 4, 37, 38 [8] Curiale, A. H., Bernardo, A., Dellazoppa, L., Mato, G. Manual de uso: Cardiac, 2020. URL https://cutt.ly/cardIAc-manual. 4, 45, 48 [9] Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., et al. 3d slicer as an image computing platform for the quantitative imaging network, 2012. URL http://www.sciencedirect.com/science/article/pii/ S0730725X12001816, quantitative Imaging in Cancer. 5 [10] Python software foundation. URL http://www.python.org. 5 [11] Nord, H., Chambe-Eng, E. Visualization toolkit. URL http://www.vtk.org/. 5 [12] Johnson, H. J., McCormick, M. M., Ibanez, L. The ITK Software Guide Book 1: Introduction and Development Guidelines-Volume 1, 2015. 5 [13] Harris, C. R., Millman, K. J., van der Walt et al, S. Array programming with numpy. Nature, 585, 357{362, 2020. 5 [14] Gitlab. URL http://www.gitlab.com. 5 [15] Nord, H., Chambe-Eng, E. Qt framework, 1995. URL http://www.qt.io/. 5 [16] Ahrens, J., Geveci, B., Law, C. Paraview: An end-user tool for large data visualization. Visualization Handbook, 2005. 5 [17] Cerqueira, M. D., Weissman, N. J., Dilsizian, V., Jacobs, A. K., Kaul, S., Laskey, W. K., et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation, 105 (4), 539{542, 2002. URL https://ahajournals.org/doi/abs/10.1161/hc0402.102975. 9 [18] Curiale, A. H. Contribuciones a la cuantificación de insuciencias cardiacas mediante el análisis y procesado de ecocardiografías. Universidad de Valladolid, 2015. 9, 11, 14, 15 [19] Parisi, A. F. Approaches to determination of left ventricular volume and ejection fraction by real-time two-dimensional echocardiography. Clinical Cardiology, 1979. 10 [20] D'hooge, J., Heimdal, A., Jamal, F., Kukulski, T., Bijnens, B., Rademakers, F., et al. Regional Strain and Strain Rate Measurements by Cardiac Ultrasound: Principles, Implementation and Limitations. European Journal of Echocardiography, 1 (3), 154{170, 09 2000. URL https://doi.org/10.1053/euje.2000.0031. 14 [21] Roche, A., Malandain, G., Ayache, N. Unifying maximum likelihood approaches in medical image registration. International Journal of Imaging Systems and Technology, 11 (1), 71{80, 2000. 16 [22] Hill, D. L. G. Medical image registration. Phys. Med. Biol., 2001. 16 [23] Robinson, D., Chen, F., Wilson, L. Measurement of velocity of propagation from ultrasonic pulse-echo data. Ultrasound in Medicine & Biology, 8 (4), 413 { 420, 1982. Ultrasonic Mammography. 16 [24] Shannon, C. E. A mathematical theory of communication. The Bell System Technical Journal, 27 (3), 379{423, July 1948. URL https://ieeexplore.ieee. org/document/6773024. 16 [25] Christensen, G. E., Rabbitt, R. D., Miller, M. I. Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing, 5 (10), 1435{1447, Oct 1996. 17 [26] OGLE, K. N. The perception of the visual world. james j. gibson; leonard carmichael, ed. boston: Houghton miin, 1950. 235. Science, 113 (2940), 535{535, 1951. URL https://science.sciencemag.org/content/113/2940/535.1. 17 [27] Thirion, J.-P. Image matching as a diusion process: an analogy with maxwell's demons. Medical Image Analysis, 2 (3), 243{260, 1998. 18 [28] Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. Diffeomorphic demons: Effcient non-parametric image registration. NeuroImage, 45 (1, Supplement 1), S61 { S72, 2009. URL http://www.sciencedirect.com/science/article/pii/ S1053811908011683, mathematics in Brain Imaging. 18 [29] Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. Symmetric log-domain dieomorphic registration: A demons-based approach. Medical Image Computing and Computer Assisted Intervention, 2008. 18 [30] Adelson, E., Anderson, C., Bergen, J., Burt, P., Ogden, J. Pyramid methods in image processing. RCA Eng., 29, 11 1983. 20 [31] Salerno, M. Feature tracking by cmr. JACC: Cardiovascular Imaging, 11 (2 Part 1), 206{208, 2018. URL http://imaging.onlinejacc.org/content/11/2_ Part_1/206. 21 [32] Lee, V. S. Cardiovascular MRI: Physical Principles to Practical Protocols. Lippincott Williams & Wilkins, 2005. 25 [33] Gamma, E., Helm, R., Johnson, R., Vlissides, J. M. Design Patterns: Elements of Reusable Object-Oriented Software. 1a ed. Addison-Wesley Professional, 1994. URL http://www.amazon.com/ Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612/ ref=ntt_at_ep_dpi_1. 29 [34] Ieee guide for software verication and validation plans. IEEE Std 1059-1993, págs.. 1{87, 1994. 31 [35] Dru, F., Vercauteren, T. An itk implementation of the symmetric log-domain diffeomorphic demons algorithm. HAL-INRIA, 2009. 40 [36] Tobon-Gomez, C., De Craene, M., McLeod, K., Tautz, L., Shi, W., Hennemuth, A., et al. Benchmarking framework for myocardial tracking and deformation algorithms: An open access database. Medical Image Analysis, 17 (6), 632 { 648, 2013. URL http://www.sciencedirect.com/science/article/pii/ S1361841513000388. 49, 50, 51 [37] Duchateau, N., Sermesant, M., Delingette, H., Ayache, N. Model-based generation of large databases of cardiac images: Synthesis of pathological cine mr sequences from real healthy cases. IEEE Transactions on Medical Imaging, 37 (3), 755{766, 2018. 49, 50 [38] Bestel, J., Clément, F., Sorine, M. A biomechanical model of muscle contraction. tomo 2208, págs.. 1159{1161. 2001. 50 [39] Mooney, M. A theory of large elastic deformation. Journal of Applied Physics, 11 (9), 582{592, 1940. URL https://doi.org/10.1063/1.1712836. 50 [40] Amzulescu, M. S., De Craene, M., Langet, H., Pasquet, A., Vancraeynest, D., Pouleur, A. C., et al. Myocardial strain imaging: review of general principles, validation, and sources of discrepancies. European Heart Journal - Cardiovascular Imaging, 20 (6), 605{619, 03 2019. URL https://doi.org/10.1093/ehjci/jez041. 53 [41] Voigt, J.-U., Cvijic, M. 2- and 3-dimensional myocardial strain in cardiac health and disease. JACC: Cardiovascular Imaging, 12 (9), 1849{1863, 2019. 53 |
| Materias: | Medicina > Física médica |
| Divisiones: | Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Física médica |
| Código ID: | 950 |
| Depositado Por: | Marisa G. Velazco Aldao |
| Depositado En: | 26 Jul 2021 08:13 |
| Última Modificación: | 26 Jul 2021 08:13 |
Personal del repositorio solamente: página de control del documento
