Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images
The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and certain artifacts. To minimize the impact of these...
- Autores:
-
Vera, Miguel
Bravo, Antonio
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/11453
- Palabra clave:
- numerical phantoms
Cardiac dataset
Processing techniques
Artifacts
Poisson noise
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
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Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
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Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images numerical phantoms Cardiac dataset Processing techniques Artifacts Poisson noise |
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Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
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Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
| title_fullStr |
Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
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Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
| title_sort |
Description and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images |
| dc.creator.fl_str_mv |
Vera, Miguel Bravo, Antonio |
| dc.contributor.author.none.fl_str_mv |
Vera, Miguel Bravo, Antonio |
| dc.subject.eng.fl_str_mv |
numerical phantoms Cardiac dataset Processing techniques Artifacts Poisson noise |
| topic |
numerical phantoms Cardiac dataset Processing techniques Artifacts Poisson noise |
| description |
The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and certain artifacts. To minimize the impact of these imperfections on the quality of the CT images, several researchers have developed digital image processing techniques (DPIT) by which the quality is evaluated considering several metrics and databases (DB), both real and simulated. This article describes the processes that made it possible to generate and utilize six three-dimensional synthetic cardiac DBs or voxels-based numerical phantoms. An exhaustive analysis of the most relevant features of images of the left ventricle, belonging to a real CT DB of the human heart, was performed. These features are recreated in the synthetic DBs, generating a reference phantom or ground truth free of imperfections (DB1) and five phantoms, in which Poisson noise (DB2), stair-step artifact (DB3), streak artifact (DB4), both artifacts (DB5) and all imperfections (DB6) are incorporated. These DBs can be used to determine the performance of DPIT, aimed at decreasing the effect of these imperfections on the quality of cardiac images. |
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2022 |
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2022-11-22T15:47:38Z |
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2022-11-22T15:47:38Z |
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2022 |
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Artículo científico |
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Vera, M., Bravo, A., & Medina, R. (2022). Description and Use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images. Data, 7(8), 115. https://doi.org/10.3390/data7080115 |
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23065729 |
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https://hdl.handle.net/20.500.12442/11453 |
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https://doi.org/10.3390/data7080115 |
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Vera, M., Bravo, A., & Medina, R. (2022). Description and Use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images. Data, 7(8), 115. https://doi.org/10.3390/data7080115 23065729 |
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https://hdl.handle.net/20.500.12442/11453 https://doi.org/10.3390/data7080115 |
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eng |
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MDPI |
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Vera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e3Bravo, Antonio07aba3dd-3344-4237-9ad4-3d40d655e9152022-11-22T15:47:38Z2022-11-22T15:47:38Z2022Vera, M., Bravo, A., & Medina, R. (2022). Description and Use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images. Data, 7(8), 115. https://doi.org/10.3390/data708011523065729https://hdl.handle.net/20.500.12442/11453https://doi.org/10.3390/data7080115The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and certain artifacts. To minimize the impact of these imperfections on the quality of the CT images, several researchers have developed digital image processing techniques (DPIT) by which the quality is evaluated considering several metrics and databases (DB), both real and simulated. This article describes the processes that made it possible to generate and utilize six three-dimensional synthetic cardiac DBs or voxels-based numerical phantoms. An exhaustive analysis of the most relevant features of images of the left ventricle, belonging to a real CT DB of the human heart, was performed. These features are recreated in the synthetic DBs, generating a reference phantom or ground truth free of imperfections (DB1) and five phantoms, in which Poisson noise (DB2), stair-step artifact (DB3), streak artifact (DB4), both artifacts (DB5) and all imperfections (DB6) are incorporated. These DBs can be used to determine the performance of DPIT, aimed at decreasing the effect of these imperfections on the quality of cardiac images.pdfengMDPIAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2DataVol. 7 Issue 8 (2022)numerical phantomsCardiac datasetProcessing techniquesArtifactsPoisson noiseDescription and use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Imagesinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Kroft, L.; De Roos, A.; Geleijns, J. Artifacts in ECG–synchronized MDCT coronary angiography. Am. J. Roentgenol. 2007, 189, 581–591.Shim, S.; Kim, Y.; Lim, S. Improvement of image quality with β–blocker premedication on ECG–gated 16–MDCT coronary angiography. Am. J. Roentgenol. 2005, 184, 649–654.Niwa, S.; Ichikawa, K.; Kawashima, H.; Takata, T.; Minami, S.; Mitsui, W. Reduction of streak artifacts caused by low photon counts utilizing an image-based forward projection in computed tomography. Comput. Biol. Med. 2021, 135, 104583.Faletra, F.; Pandian, N.; Ho, S. Anatomy of the Heart by Multislice Computed Tomography; Wiley: Hoboken, NJ, USA, 2008.Hong, C.; Becker, C.; Huber, A.; Schoepf, U.; Ohnesorge, B.; Knez, A.; Reiser, M. ECG–gated reconstructed multi–detector row CT coronary angiography: Effect of varying trigger delay on image quality. Radiology 2001, 220, 712–717.Rydber, J.; Buckwalter, K.; Caldemeyer, K.; Phillips, M.; Conces, D.; Aisen, A.; Persohn, S.; Kopecky, K. Multisection CT: Scanning techniques and clinical applications. RadioGraphics 2000, 20, 1787–1806.Clemente, J.; Bravo, A.; Medina, R. Using morphological and clustering analysis for left ventricle detection in MSCT cardiac images. In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, Sarajevo, Bosnia and Herzegovina, 16–19 December 2008; pp. 264–269.Primak, A.; McCollough, C.; Bruesewitz, M.; Zhang, J.; Fletcher, J. Relationship between noise, dose, and pitch in cardiacmulti–detector row CT. Radiographics 2006, 26, 1785–1794.Chan, R.; Chenz, K. Multilevel algorithm for a poisson noise removal model with total-variation regularization. Int. J. Comput. Math. 2007, 1, 1183–1198.Zanella, R.; Boccacci, P.; Zanni, L.; Bertero, M. Efficient gradient projection methods for edge–preserving removal of Poisson noise. Inverse Probl. 2009, 25, 045010.Maiera, A.; Wigstrm, L.; Hofmann, H.; Hornegger, J.; Zhu, L.; Strobel, N.; Fahrig, R. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT. Med. Phys. 2011, 38, 5896–5909.McGarry, C.; Grattan, L.; Ivory, A.; Leek, F.; Liney, G.; Liu, Y.; Miloro, P.; Rai, R.; Robinson, A.; Shih, A.; et al. Tissue mimicking materials for imaging and therapy phantoms: A review. Phys. Med. Biol. 2020, 65, 44.Roy, C.; Marini, D.; Segars, W.; Seed, M.; Macgowan, C. Fetal XCMR: A numerical phantom for fetal cardiovascular magnetic resonance imaging. J. Cardiovasc. Magn. Reson. 2019, 21, 29.Bravo, A.; Clemente, J.; Vera, M.; Avila, J.; Medina, R. A hybrid boundary–region left ventricle segmentation in computed tomography. In Proceedings of the 5th VISAPP, Angers, France, 17–21 May 2010; pp. 107–114.Vera, M. Segmentación de Estructuras Cardiacas en Imágenes de Tomografía Computarizada Multi-Corte. Ph.D. Thesis, Universidad de Los Andes, Mérida, Venezuela, 2014.Li, F.; Rajchl, M.; White, J.; Goela, A.; Peters, T. Ultrasound Guidance for Beating Heart Mitral Valve Repair Augmented by Synthetic Dynamic CT. IEEE Trans. Med. Imaging 2015, 34, 2025–2035.Müller, M.; Paganelli, C.; Keall, P. A phantom study to create synthetic CT from orthogonal two-dimensional cine MRI and evaluate the effect of irregular breathing. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018; pp. 4162–4165.Lyu, F.; Ye, M.; Ma, A.; Yip, T.; Wong, H.; Yuen, P. Learning from Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation. IEEE Trans. Med. Imaging 2022.Nomura, Y.; Watanabe, H.; Manila, N.; Asai, S.; Kurabayashi, T. Evaluation of streak metal artifacts in cone beam computed tomography by using the Gumbel distribution: A phantom study. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2020, 131, 494–502.Dossing, A.; Müller, F.; Becce, F.; Stamp, L.; Bliddal, H.; Boesen., M. Dual-energy computed tomography can detect and characterize monosodium urate, calcium pyrophosphate and hydroxyapatite: A phantom study on diagnostic performance. Osteoarthr. Cartil. 2021, 29, S320–S321.Medici, S.; Desorgher, L.; Carbonez, P.; Damet, J.; Bochud, F.; Pitzschke, A. Impact of the phantom geometry on the evaluation of the minimum detectable activity following a radionuclide intake: From physical to numerical phantoms. Radiat. Meas. 2020, 139, 106485.Lubis, L.; Basith, R.; Hariyati, I.; Ryangga, D.; Mart, T.; Bosmans, H.; Soejoko, D. Novel phantom for performance evaluation of contrast-enhanced 3D rotational angiography. Phys. Med. 2021, 90, 91–98.Pasyar, P.; Masjoodi, S.; Montazeriani, Z.; Makkiabadi, B. A digital viscoelastic liver phantom for investigation of elastographic measurements. Comput. Biol. Med. 2020, 127, 104078.Shepp, L.; Logan, B. The Fourier Reconstruction of a Head Section. IEEE Trans. Nucl. Sci. 1974, 21, 21–43.Koay, C.; Sarlls, J.; Özarslan, E. Three-Dimensional Analytical Magnetic Resonance Imaging Phantom in the Fourier Domain. Magn. Reson. Med. 2007, 58, 430–436.Collins, D.; Zijdenbos, A.; Kollokian, V.; Sled, J.; Kabani, N.; Holmes, C.; Evans, C. Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 1998, 17, 463–468.Trawiński, Z.; Wójcik, J.; Nowicki, A.; Olszewski, R.; Balcerzak, A.; Frankowska, E.; Zegadło, A.; Rydzyński, P. Strain examinations of the left ventricle phantom by ultrasound and multislices computed tomography imaging. Biocybern. Biomed. Eng. 2015, 35, 255–263.Bravo, A.; Vera, M.; Huérfano, Y.; Manrique, Y. A comprehensive study of a similarity criterion in cardiac computerized tomography images enhancement. Rev. Fac. Ing. Univ. Antioq. 2020, 102, 51–61.Pratt, W. Digital Image Processing; John Wiley & Sons Inc.: Hoboken, NJ, USA, 2007.Petrou, M.; Bosdogianni, P. Image Processing the Fundamentals; Wiley: Chichester, UK, 2003.Devroye, L. Non-Uniform Random Variate Generation; Springer: New York, NY, USA, 1986.Knuth, D. The art of computer programming. In Seminumerical Algorithms, 3rd ed.; Addison-Wesley: Boston, MA, USA, 1997; Volume 2.Ahrens, J.; Dieter, U. Computer generation of Poisson deviates from modified normal distributions. ACM Trans. Math. Softw. 1982, 8, 163–179.Haralick, R.; Shapiro, L. Computer and Robot Vision. Boston; Addison-Wesley: Boston, MA, USA, 1992.Wang, Z.; Bovik, A.; Sheikh, H.; Simoncelli, E. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Processing 2004, 13, 600–612.Bravo, A.; Medina, R. An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms. Comput. Med. Imaging Graph. 2008, 32, 386–408.Vera, M.; Medina, R.; Del Mar, A.; Arellano, J.; Huérfano, Y.; Bravo, A. An automatic technique for left ventricle segmentation from msct cardiac volumes. J. Phys. Conf. Ser. 2019, 1160, 01200.Vera, M.; Bravo, A.; Garreau, M.; Medina, R. Similarity enhancement for automatic segmentation of cardiac structures in computed tomography volumes. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA; 2011; pp. 8094–8097.Chacón, G.; Rodríguez, J.E.; Bermúdez, V.; Flórez, A.; Del Mar, A.; Pardo, A.; Lameda, C.; Madriz, D.; Bravo, A.J. A score function as quality measure for cardiac image enhancement techniques assessment. Rev. Latinoam. Hipertens. 2019, 14, 180–186.Meijering, H. Image Enhancement in Digital X–ray Angiography. Ph.D. Thesis, Utrecht University, Utrecht, The Netherlands, 2000.Perona, P.; Malik, J. Scalespace and edge detection using anisotropic diffusion. IEEE Trans. Patt. Anal. Mach. 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