2020, Number 2
<< Back Next >>
Revista Cubana de Informática Médica 2020; 12 (2)
Process for the fusion of Positron Emission Tomography and Magnetic Resonance neuroimaging
Orellana GA, Socarrás LD, González PKC
Language: Spanish
References: 22
Page:
PDF size: 785.44 Kb.
ABSTRACT
In radiology, various imaging techniques are used for the diagnosis of diseases and assistance in surgical interventions with the aim of determining the exact location and dimension of a brain tumor. Techniques such as Positron Emission Tomography and Magnetic Resonance can determine the malignant or benign nature of a brain tumor and study brain structures with high-resolution neuroimaging. International researchers have used different techniques for the fusion of Positron Emission Tomography and Magnetic Resonance, allowing the observation of physiological characteristics in correlation with anatomical structures. The present research aims to develop a process for the fusion of neuroimaging of Positron Emission Tomography and Magnetic Resonance Imaging. Five activities were defined in the process and the algorithms to be used in each one, which led identifying the most efficient ones to increase the quality in the fusion process. As a result, a neuroimaging fusion process was obtained based on a hybrid Wavelet and Curvelet scheme that guarantees high quality merged images.
REFERENCES
Núñez M. Procesamiento de imágenes en Medicina Nuclear. Montevideo: Escuela Universitaria de Tecnología Médica. Comité de Tecnólogos de ALASBIMN. 2008.
Martínez Krawczuk WM. Segmentación de imágenes médicas (Doctoral dissertation, Universidad Nacional de La Plata).
Lorenzo-Bosquet C, Hernández-Vara J, Castell-Conesa J, Miquel-Rodríguez F. Neuroimagen funcional mediante SPECT en la enfermedad de Parkinson y los parkinsonismos. Rev Neurol. 2008;46:430-5.
Herraiz JL. Técnicas avanzadas de reconstrucción de imagen nuclear PET, X-CT y SPECT. Memoria del Trabajo del Máster de Física Biomédica. 2008.
Pichler BJ, Judenhofer MS, Wehrl HF. PET/MRI hybrid imaging: devices and initial results. European radiology. 2008 Jun 1;18(6):1077-86.
Judenhofer MS, Wehrl HF, Newport DF, Catana C, Siegel SB, Becker M, Thielscher A, Kneilling M, Lichy MP, Eichner M, Klingel K. Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nature medicine. 2008 Apr;14(4):459-65.
Caloca LA. Tecnicas avanzadas de fusion de imágenes. México, DF: Universidad Nacional Autónoma de México. 2007.
Garibotto V, Heinzer S, Vulliemoz S, Guignard R, Wissmeyer M, Seeck M, Lovblad KO, Zaidi H, Ratib O, Vargas MI. Clinical applications of hybrid PET/MRI in neuroimaging. Clinical nuclear medicine. 2013 Jan 1;38(1):e13-8.
Heiss WD. The potential of PET/MR for brain imaging. European journal of nuclear medicine and molecular imaging. 2009 Mar 1;36(1):105-12.
Donoho DL, Duncan MR. Digital curvelet transform: strategy, implementation, and experiments. InWavelet applications VII 2000 Apr 5 (Vol. 4056, pp. 12-30). International Society for Optics and Photonics.
Piella G. A general framework for multiresolution image fusion: from pixels to regions. Information fusion. 2003 Dec 1;4(4):259-80.
Uludağ K, Roebroeck A. General overview on the merits of multimodal neuroimaging data fusion. Neuroimage. 2014 Nov 15;102:3-10.
Schillaci O, Simonetti G. Fusion imaging in nuclear medicine—applications of dual-modality systems in oncology. Cancer Biotherapy and Radiopharmaceuticals. 2004 Feb 1;19(1):1-0.
Marín García A. Implementación y evaluación de algoritmos de fusión de imagen en el contexto de la imagen médica [tesis]. Cartagena: Universidad Politécnica de Cartagena; 2013 [citado 24 mayo 2020]. Disponible en: https://repositorio.upct.es/bitstream/handle/10317/3429/pfc5406.pdf%3Bjses Aprox. 218 pp.
Melo SB. Transformaciones geométricas sobre imágenes digitales. Facultad de Ciencias-Carrera de Matemáticas. Universidad Distrital Francisco José de Caldas. Jun. 2009.
Patel V, Mistree K. A review on different image interpolation techniques for image enhancement. International Journal of Emerging Technology and Advanced Engineering. 2013 Dec;3(12):129-33.
Fadnavis S. Image interpolation techniques in digital image processing: an overview. International Journal of Engineering Research and Applications. 2014 Oct 4;4(10):70-3.
Amini N, Fatemizadeh E, Behnam H. MRI and PET image fusion by using curvelet transform. Journal of Advances in Computer Research. 2014 Nov 1;5(4):23-30.
Wakure S, Todmal S. Survey on different image fusion techniques. IOSR J VLSI Signal Process. 2013 Mar;1(6):42-8.
Joseph J, Barhatte A. Medical Image Fusion Based on Wavelet Transform and Fast Curvelet Transform. International Journal of Engineering Development and Research. 2014;2(1):284-8.
Agarwal J, Bedi SS. Implementation of hybrid image fusion technique for feature enhancement in medical diagnosis. Human-centric Computing and Information Sciences. 2015 Dec;5(1):1-7.
Kaur N, Bahl M. Image Fusion based on Wavelet and Curvelet Transform using ANFIS Algorithm. Image. 2014 Sep;3(9):aprox. 9 pp.