Reconstrucción Mejorada de Datos de Resonancia Magnética Mediante Aproximación por Descomposición por Valores Singulares

Improved MRI Reconstruction Using a Singular Value Decomposition Approximation
  • Davi Marco Lyra-Leite University of Brasília
  • João Paulo Carvalho Lustosa da Costa University of Brasília
  • João Luiz Azevedo de Carvalho University of Brasília
DOI: https://doi.org/10.14483/23448393.3853
Palabras clave: data reconstruction, denoising, magnetic resonance imaging, truncated SVD. (en_US)
Palabras clave: descomposición truncada de valores singulares, reconstrucción de datos, reducción del ruido, resonancia magnética. (es_ES)
Resumen Autores/as Descargas Referencias Cómo citar

Resumen (es_ES)

La reconstrucción de datos de resonancia magnética (RM) puede ser una tarea computacionalmente ardua. La razón señal-ruido también puede presentar complicaciones, especialmente en imágenes de alta resolución. En este sentido, la compresión de datos puede ser útil no sólo para reducir la complejidad y los requerimientos de memoria, sino también para reducir el ruido, hasta inclusive permitir eliminar componentes espurios.

El presente trabajo propone el uso de un sistema basado en la descomposición por valores singulares de bajo orden para reconstrucción y reducción de ruido en imágenes de RM. El criterio de información de Akaike se utiliza para estimar el orden del modelo, que es usado para remover los componentes ruidosos y reducir la cantidad de datos procesados y almacenados. El método propuesto es evaluado usando datos de RM in vivo. Se presentan imágenes reconstruidas con menos de 20% de los datos originales y con calidad similar en cuanto a su inspección visual. Igualmente se presenta una evaluación cuantitativa del método.

Resumen (en_US)

The reconstruction of magnetic resonance imaging (MRI) data can be a computationally demanding task. Signal-to-noise ratio is also a concern, especially in high-resolution imaging. Data compression may be useful not only for reducing reconstruction complexity and memory requirements, but also for reducing noise, as it is capable of eliminating spurious components.

This work proposes the use of a singular value decomposition low-rank approximation for reconstruction and denoising of MRI data. The Akaike Information Criterion is used to estimate the appropriate model order, which is used to remove noise components and to reduce the amount of data to be stored and processed. The proposed method is evaluated using in vivo MRI data. We present images reconstructed using less than 20% of the original data size, and with a similar quality in terms of visual inspection. A quantitative evaluation is also presented

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Biografía del autor/a

Davi Marco Lyra-Leite, University of Brasília

Was born in Brasília-DF, Brazil. He received his B.E. degree in electrical engineering in 2012 from the University of Brasília, in Brasília-DF, Brazil.

During his undergraduate studies, he received a scientific initiation scholarship from the National Council of Technological and Scientific Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq) of the Brazilian Government.

Currently, he is a Ph.D. student at the University of Southern California, in Los Angeles, CA, United States. His research interests are in the area of magnetic resonance imaging, biomedical signal processing, medical image formation and analysis, and cardiovascular diseases.

João Paulo Carvalho Lustosa da Costa, University of Brasília

Was born in Fortaleza, Brazil. He received his Diploma degree in electronic engineering in 2003 from the Military Institute of Engineering (IME) in Rio de Janeiro, Brazil, his M.S. degree in 2006 from University of Brasília (UnB) in Brazil, and his Doktor-Ingenieur (Ph.D.) degree with Magna cum Laude in 2010 from Ilmenau University of Technology (TU Ilmenau) in Germany.

During his Ph.D. studies, he was a scholarship holder of the National Council of Technological and Scientific Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq) of the Brazilian Government, and also a captain of the Brazilian Army.

Currently, he is a professor at the Department of Electrical Engineering of the University of Brasília (UnB), and he participates in the Digital Signal Processing Group and in the Laboratory of Technologies for Decision Making (LATITUDE), supported by DELL computers of Brazil. He is co-responsible for the Laboratory of Array Signal Processing (LASP) at UnB. His research interests are in the areas of multi-dimensional array signal processing, model order selection, principal component analysis, MIMO communications systems, parameter estimation schemes, development of communication solutions and of sensors for UAVs, and business intelligence.

João Luiz Azevedo de Carvalho, University of Brasília

Was born in Campinas, Brazil. He received his B.E. degree in network engineering in 2002 from the University of Brasília, in Brasília-DF, Brazil, his M.S. degree in electrical engineering in 2003 from the University of Brasília, in Brasília-DF, Brazil, and M.S. and Ph.D. degrees in electrical engineering in 2006 and 2008, respectively, from the University of Southern California, in Los Angeles, CA, United States.

Currently, he is a professor at the Department of Electrical Engineering of the University of Brasília. His research interests are in the field of biomedical signal and image processing, including magnetic resonance flow imaging, reconstruction from undersampled MRI data, heart rate variability, and surface electromyography.

Referencias

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Cómo citar
Lyra-Leite, D. M., Carvalho Lustosa da Costa, J. P., & Azevedo de Carvalho, J. L. (2012). Reconstrucción Mejorada de Datos de Resonancia Magnética Mediante Aproximación por Descomposición por Valores Singulares. Ingeniería, 17(2), 35 - 45. https://doi.org/10.14483/23448393.3853
Publicado: 2012-12-28
Número
Vol. 17 Núm. 2 (2012): Julio - Diciembre
Sección
Artículos

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