Interfaz gráfica de usuario para una línea de luz de sincrotrón
DOI:
https://doi.org/10.37135/unach.ns.001.04.06Keywords:
Beamline, graphical user interface, image processing, python, tomographyAbstract
Esta investigación consistió en desarrollar una interfaz gráfica de usuario en lenguaje Python para la reconstrucción de imágenes basada en la técnica de propagación basada en imágenes (PBI). La metodología utilizada consistió en volver a escribir y ordenar el código existente reformulándolo en forma de clases y métodos para vincularlo a la plantilla de interfaz gráfica de usuario mediante PyQt. Se explicaron los requisitos de análisis, el diseño y la implementación de la interfaz gráfica del usuario. La prueba de la interfaz gráfica se realizó en dos experimentos utilizando la técnica PBI, analizando sus diferencias y similitudes, lo que demuestra que la interfaz gráfica de usuario, su herramienta, y el algoritmo para la reconstrucción fueron probados con éxito.
Downloads
References
Bech, M., Tapfer, A., Velroyen, A., Yaroshenko, A., Pauwels, B., Hostens, J., & Pfeiffer, F. (2013). In-vivo dark-field and phase-contrast x-ray imaging. Scientific reports,, 3(3209).
Beniz, D., & Espindola, A. . (2016). Using Tkinter of Python to Create Graphical User Interface (GUI) for Scripts in LNLS. WEPOPRPO25, 09, 25–28.
Boerckel, J. D., Mason, D. E., McDermott, A. M., & Alsberg, E. (2014). Microcomputed tomography: approaches and applications in bioengineering. Stem cell research and therapy, 5(6), 144.
Bronnikov, A. V. (2006a). Phase-contrast CT: fundamental theorem and fast image reconstruction algorithms.
Bronnikov, A. V. (2006b). Phase-contrast ct: fundamental theorem and fast image reconstruction algorithms. In developments in x-ray tomography. International Society for Optics and Photonics., 6318, 63180Q.
Buchmann, J., & Ludwig, C. (2003). Practical lattice basis sampling reduction. Lecture Notes in Computer Science, 4076(19).
Cai, W. (2009). Feasibility study of phase-contrast cone beam CT imaging systems. University of Rochester, Rochester, USA, 1 edition. isbn = 9781109634686.
Chen, R. C., Rigon, L., & Longo, R. (2013). Comparison of single distance phase retrieval algorithms by considering different object composition and the effect of statistical and structural noise. Optics express, 21(6), 7384–7399.
Donzelli, M., Brauer-Krisch, E., Nemoz, C., Brochard, T., & Oelfke, U. (2016). Conformal image-guided microbeam radiation therapy at the esrf biomedical beamline id17. 46.
ESRF (2019a). Id17 - biomedical beamline. http://www.esrf.eu/UsersAndScience/ Experiments/CBS/ID17,accessed2019-04-10.
ESRF (2019b). Overview of the beamline optics. http://www.esrf.eu/UsersAndScience/ Experiments/CRG/BM02/optic, accessed 2019-04-10.
Fedel, G., Piton, J., Do Carmo, L., & Beniz, D. (2017). Python for User Interfaces at Sirius. Proceedings, 16th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS 2017), 09, 8–13.
Gureyev, T. E., Mayo, S. C., Myers, D. E., Nesterets, Y., Paganin, D. M., Pogany, A., & Wilkins, S. W. (2009). Refracting r¨ontgen’s rays: propagation-based x-ray phase contrast for biomedical imaging. Journal of Applied Physics, 105(10).
Jarek, S. (2019). Seeded region growing (imagej plugin). http://ij-plugins. sourceforge.net/plugins/segmentation/ Howto-Seeded-Region-Growing-Segmentation. pdf, accessed 2019-05-24.
King, D. M., Moran, C. M., McNamara, J. D., Fagan, A. J., & Browne, J. E. (2011). Development of a vessel-mimicking material for use in anatomically realistic doppler flow phantoms. Ultrasound in medicine and biology, 37(5), 813–826.
Konrad-Zuse-Zentrum (2019). Amira users guide. http://www1.udel.edu/ctcr/sites/udel. edu.ctcr/files/Amira%20Users%20Guide.pdf, accessed 2019-05-24.
Kuhlman, D. (2009). A python book: Beginning python, advanced python, and python exercises. Dave Kuhlman Lutz.
Lussani, F. C., Vescovi, R. F. D. C., Souza, T. D. D., Leite, C. A., & Giles, C. (2015). A versatile x-ray microtomography station for biomedical imaging and materials research. Review of Scientific Instruments, 86(6).
Mayo, S. C., Davis, T. J., Gureyev, T. E., Miller, P. R., Paganin, D., Pogany, A., & Wilkins, S. W. (2003a). X-ray phase-contrast microscopy and microtomography. Optics express, 11(19), 2289–2302.
Mayo, S. C., Davis, T. J., Gureyev, T. E., Miller, P. R., Paganin, D., Pogany, A., & Wilkins, S. W. (2003b). X-ray phase-contrast microscopy and microtomography. Optics Express, 11(19), 2289–2302.
Mirone, A., Brun, E., Gouillart, E., Tafforeau, P., & Kieffer, J. (2013). The pyhst2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms., 3(324), 41–48.
Mohammadi, S., Larsson, E., Alves, F., Dal Monego, S., Biffi, S., Garrovo, C., & Dullin, C. (2014). Quantitative evaluation of a single-distance phase-retrieval method applied on in-line phase-contrast images of a mouse lung. Journal of Synchrotron Radiation, 21(4), 784–789.
Phenogenomics (2019). Micro-computed tomography (micro-ct). http://www.mouseimaging.ca/technologies/microct.html, accessed 2019-05-23.
Pressman, R. (2010). Ingeniería del Software (Un Enfoque Práctico), volume 1. Mc-GrawHill., España, 1 edition. isbn = 978-607-15-0314-5.
Price, R. R., Axel, L., Morgan, T., Newman, R., Perman, W., Schneiders, N., & Thomas, S. R. (1990). Quality assurance methods and phantoms for magnetic resonance imaging: report of aapm nuclear magnetic resonance task group no. 1. Medical physics, 17(2), 287–295.
Riverbank (2019). What is pyqt? https://riverbankcomputing.com/software/pyqt/intro, accessed 2019-04-10.
Simon, C. (2019). Gui programming in python. https: //wiki.python.org/moin/GuiProgramming, accessed 2019-05-23.
Slepicka, H. H., Canova, H. F., Beniz, D. B., &Piton, J. R. (2015). Py4syn: Python for synchrotrons. Journal of Synchrotron Radiation, 22(05), 1182–9.
