Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

[EN] In CT (computerized tomography) imaging reconstruction, the acquired sinograms are usually noisy, so artifacts will appear on the resulting images. Thus, it is necessary to find the adequate filters to combine with reconstruction methods that eliminate the greater amount of noise possible without altering in excess the information that the image contains. The present work is focused on the evaluation of several filtering techniques applied in the elimination of artifacts present in CT sinograms. In particular, we analyze the elimination of Gaussian and Speckle noise. The chosen filtering techniques have been studied using four functions designed to measure the quality of the filtered image and compare it with a reference image. In this way, we determine the ideal parameters to carry out the filtering process on the sinograms, prior to the process of reconstruction of the images. Moreover, we study their application on reconstructed noisy images when using noisy sinograms and finally we select the best filter to combine with an iterative reconstruction method in order to test if it improves the quality of the images. With this, we can determine the feasibility of using the selected filtering method for our CT reconstructions with projections reduction, concluding that the bilateral filter is the filter that behaves best with our images. We will test it when combined with our iterative reconstruction method, which consists on the Least Squares QR method in combination with a regularization technique and an acceleration step, showing how integrating this filter with our reconstruction method improves the quality of the CT images.This research has been supported by "Universitat Politecnica de Valencia", "Generalitat Valenciana" under PROMETEO/2018/035 as well as ACIF/2017/075 predoctoral grant co-financed by FEDER and FSE funds, and "Spanish Ministry of Science, Innovation and Universities" under Grant RTI2018-098156-B-C54 co-financed by FEDER funds.Chillarón-Pérez, M.; Vidal-Gimeno, V.; Verdú Martín, GJ. (2020). Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method. PLoS ONE. 15(3):1-14. https://doi.org/10.1371/journal.pone.0229113114153Managing patient dose in computed tomography. (2000). Annals of the ICRP, 30(4), 7-7. doi:10.1016/s0146-6453(01)00049-5Chillarón, M., Vidal, V., Segrelles, D., Blanquer, I., & Verdú, G. (2017). Combining Grid Computing and Docker Containers for the Study and Parametrization of CT Image Reconstruction Methods. Procedia Computer Science, 108, 1195-1204. doi:10.1016/j.procs.2017.05.065Flores, L., Vidal, V., & Verdú, G. (2015). Iterative Reconstruction from Few-view Projections. Procedia Computer Science, 51, 703-712. doi:10.1016/j.procs.2015.05.188Flores, L. A., Vidal, V., Mayo, P., Rodenas, F., & Verdú, G. (2014). Parallel CT image reconstruction based on GPUs. Radiation Physics and Chemistry, 95, 247-250. doi:10.1016/j.radphyschem.2013.03.011Parcero, E., Flores, L., Sánchez, M. G., Vidal, V., & Verdú, G. (2017). Impact of view reduction in CT on radiation dose for patients. Radiation Physics and Chemistry, 137, 173-175. doi:10.1016/j.radphyschem.2016.01.038I. Kumar, H. Bhadauria, J. Virmani, and J. Rawat, “Reduction of speckle noise from medical images using principal component analysis image fusion,” in Industrial and Information Systems, 2014 9th International Conference on. IEEE, 2014, pp. 1–6.Barrett, J. F., & Keat, N. (2004). Artifacts in CT: Recognition and Avoidance. RadioGraphics, 24(6), 1679-1691. doi:10.1148/rg.246045065Chillarón, M., Vidal, V., Verdú, G., & Arnal, J. (2018). CT Medical Imaging Reconstruction Using Direct Algebraic Methods with Few Projections. Computational Science – ICCS 2018, 334-346. doi:10.1007/978-3-319-93701-4_25Joseph, P. M. (1982). An Improved Algorithm for Reprojecting Rays through Pixel Images. IEEE Transactions on Medical Imaging, 1(3), 192-196. doi:10.1109/tmi.1982.4307572F. P. Group. FORBILD head phantom. [Online]. Available: http://www.imp.uni-erlangen.de/forbild/english/results/index.htm.Paige, C. C., & Saunders, M. A. (1982). LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares. ACM Transactions on Mathematical Software, 8(1), 43-71. doi:10.1145/355984.355989Yu, H., & Wang, G. (2010). A soft-threshold filtering approach for reconstruction from a limited number of projections. Physics in Medicine and Biology, 55(13), 3905-3916. doi:10.1088/0031-9155/55/13/022Beck, A., & Teboulle, M. (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. SIAM Journal on Imaging Sciences, 2(1), 183-202. doi:10.1137/080716542C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Sixth International Conference on Computer Vision. IEEE, 1998, pp. 839–846.A. Hore and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” in 2010 20th International Conference on Pattern Recognition. IEEE, aug 2010, pp. 2366–2369

CT image reconstruction with SuiteSparseQR factorization package

[EN] SuiteSparseQR is a factorization package for sparse matrices oriented to parallelism in multicore architectures. It employs BLAS and LAPACK as well as Intel's Threading Building Blocks to achieve high performance. Through the SPQR method implemented in this package we can use the QR decomposition to reconstruct CT images efficiently. In this paper, we analyze the behavior of the package applied to the reconstruction of medical CT images, studying the quality of the obtained image. To this purpose, we use the image dataset DeepLesion, which provides various CT studies of different lesions in different organs or tissues. We also compare it to our previous iterative reconstruction method called LSQR. This new method is promising since the computations are simplified if we compare it to the iterative options and the reconstructions are high-quality, as the results show.This research has been supported by "Universitat Politecnica de Valencia", "Generalitat Valenciana" under PROMETEO/2018/035 co-financed by FEDER funds, as well as ACIF/2017/075 predoctoral grant, and the "Spanish Ministry of Economy and Competitiveness" under Grant TIN2015-66972-05-4-R and TIAMHA co-financed by FEDER fundsChillarón-Pérez, M.; Vidal-Gimeno, V.; Verdú Martín, GJ. (2020). CT image reconstruction with SuiteSparseQR factorization package. Radiation Physics and Chemistry. 167:1-7. https://doi.org/10.1016/j.radphyschem.2019.04.039S1716

Image Noise Removal on Heterogeneous CPU-GPU Configurations

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.This work was supported by the Spanish Ministry of Science and Innovation [grant number TIN2011-26254].Sanchez, MG.; Vidal Gimeno, VE.; Arnal, J.; Vidal Meló, A. (2014). Image Noise Removal on Heterogeneous CPU-GPU Configurations. Elsevier. https://doi.org/10.1016/j.procs.2014.05.207

Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques

[EN] Background and objective: As Computed Tomography scans are an essential medical test, many techniques have been proposed to reconstruct high-quality images using a smaller amount of radiation. One approach is to employ algebraic factorization methods to reconstruct the images, using fewer views than the traditional analytical methods. However, their main drawback is the high computational cost and hence the time needed to obtain the images, which is critical in the daily clinical practice. For this reason, faster methods for solving this problem are required. Methods: In this paper, we propose a new reconstruction method based on the QR factorization that is very efficient on affordable equipment (standard multicore processors and standard Solid-State Drives) by using Out-Of-Core techniques. Results: Combining both affordable hardware and the new software proposed in our work, the images can be reconstructed very quickly and with high quality. We analyze the reconstructions using real Computed Tomography images selected from a dataset, comparing the QR method to the LSQR and FBP. We measure the quality of the images using the metrics Peak Signal-To-Noise Ratio and Structural Similarity Index, obtaining very high values. We also compare the efficiency of using spinning disks versus Solid-State Drives, showing how the latter performs the Input/Output operations in a significantly lower amount of time. Conclusions: The results indicate that our proposed me thod and software are valid to efficiently solve large-scale systems and can be applied to the Computed Tomography reconstruction problem to obtain high-quality images.This research has been supported by "Universitat Politecnica de Valencia", "Generalitat Valenciana" under PROMETEO/2018/035 and ACIF/2017/075, co-financed by FEDER and FSE funds, and the "Spanish Ministry of Science, Innovation and Universities" under Grant RTI2018-098156-B-C54 co-financed by FEDER funds.Chillarón-Pérez, M.; Quintana Ortí, G.; Vidal-Gimeno, V.; Verdú Martín, GJ. (2020). Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques. Computer Methods and Programs in Biomedicine. 193:1-11. https://doi.org/10.1016/j.cmpb.2020.105488S111193Berrington de González, A. (2009). Projected Cancer Risks From Computed Tomographic Scans Performed in the United States in 2007. Archives of Internal Medicine, 169(22), 2071. doi:10.1001/archinternmed.2009.440HALL, E. J., & BRENNER, D. J. (2008). Cancer risks from diagnostic radiology. The British Journal of Radiology, 81(965), 362-378. doi:10.1259/bjr/01948454Tang, X., Hsieh, J., Nilsen, R. A., Dutta, S., Samsonov, D., & Hagiwara, A. (2006). A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT—helical scanning. Physics in Medicine and Biology, 51(4), 855-874. doi:10.1088/0031-9155/51/4/007Zhuang, T., Leng, S., Nett, B. E., & Chen, G.-H. (2004). Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data. Physics in Medicine and Biology, 49(24), 5489-5503. doi:10.1088/0031-9155/49/24/007Mori, S., Endo, M., Komatsu, S., Kandatsu, S., Yashiro, T., & Baba, M. (2006). A combination-weighted Feldkamp-based reconstruction algorithm for cone-beam CT. Physics in Medicine and Biology, 51(16), 3953-3965. doi:10.1088/0031-9155/51/16/005Willemink, M. J., de Jong, P. A., Leiner, T., de Heer, L. M., Nievelstein, R. A. J., Budde, R. P. J., & Schilham, A. M. R. (2013). Iterative reconstruction techniques for computed tomography Part 1: Technical principles. European Radiology, 23(6), 1623-1631. doi:10.1007/s00330-012-2765-yWillemink, M. J., Leiner, T., de Jong, P. A., de Heer, L. M., Nievelstein, R. A. J., Schilham, A. M. R., & Budde, R. P. J. (2013). Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality. European Radiology, 23(6), 1632-1642. doi:10.1007/s00330-012-2764-zWu, W., Liu, F., Zhang, Y., Wang, Q., & Yu, H. (2019). Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction. IEEE Transactions on Medical Imaging, 38(4), 1079-1093. doi:10.1109/tmi.2018.2878226Wu, W., Zhang, Y., Wang, Q., Liu, F., Chen, P., & Yu, H. (2018). Low-dose spectral CT reconstruction using image gradient ℓ0–norm and tensor dictionary. Applied Mathematical Modelling, 63, 538-557. doi:10.1016/j.apm.2018.07.006Andersen, A. H. (1989). Algebraic reconstruction in CT from limited views. IEEE Transactions on Medical Imaging, 8(1), 50-55. doi:10.1109/42.20361Andersen, A. H., & Kak, A. C. (1984). Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm. Ultrasonic Imaging, 6(1), 81-94. doi:10.1177/016173468400600107Yu, W., & Zeng, L. (2014). A Novel Weighted Total Difference Based Image Reconstruction Algorithm for Few-View Computed Tomography. PLoS ONE, 9(10), e109345. doi:10.1371/journal.pone.0109345Flores, L., Vidal, V., & Verdú, G. (2015). Iterative Reconstruction from Few-view Projections. Procedia Computer Science, 51, 703-712. doi:10.1016/j.procs.2015.05.188Flores, L. A., Vidal, V., Mayo, P., Rodenas, F., & Verdú, G. (2014). Parallel CT image reconstruction based on GPUs. Radiation Physics and Chemistry, 95, 247-250. doi:10.1016/j.radphyschem.2013.03.011Chillarón, M., Vidal, V., Segrelles, D., Blanquer, I., & Verdú, G. (2017). Combining Grid Computing and Docker Containers for the Study and Parametrization of CT Image Reconstruction Methods. Procedia Computer Science, 108, 1195-1204. doi:10.1016/j.procs.2017.05.065Sollmann, N., Mei, K., Schwaiger, B. J., Gersing, A. S., Kopp, F. K., Bippus, R., … Baum, T. (2018). Effects of virtual tube current reduction and sparse sampling on MDCT-based femoral BMD measurements. Osteoporosis International, 29(12), 2685-2692. doi:10.1007/s00198-018-4675-6Yan Liu, Zhengrong Liang, Jianhua Ma, Hongbing Lu, Ke Wang, Hao Zhang, & Moore, W. (2014). Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction. IEEE Transactions on Medical Imaging, 33(3), 749-763. doi:10.1109/tmi.2013.2295738Tang, J., Nett, B. E., & Chen, G.-H. (2009). Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms. Physics in Medicine and Biology, 54(19), 5781-5804. doi:10.1088/0031-9155/54/19/008Vandeghinste, B., Vandenberghe, S., Vanhove, C., Staelens, S., & Van Holen, R. (2013). Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions. PLoS ONE, 8(7), e68449. doi:10.1371/journal.pone.0068449Qi, H., Chen, Z., & Zhou, L. (2015). CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization. Computational and Mathematical Methods in Medicine, 2015, 1-8. doi:10.1155/2015/354869Wu, W., Chen, P., Vardhanabhuti, V. V., Wu, W., & Yu, H. (2019). Improved Material Decomposition With a Two-Step Regularization for Spectral CT. IEEE Access, 7, 158770-158781. doi:10.1109/access.2019.2950427Rodriguez-Alvarez, M. J., Sanchez, F., Soriano, A., Moliner, L., Sanchez, S., & Benlloch, J. (2018). QR-Factorization Algorithm for Computed Tomography (CT): Comparison With FDK and Conjugate Gradient (CG) Algorithms. IEEE Transactions on Radiation and Plasma Medical Sciences, 2(5), 459-469. doi:10.1109/trpms.2018.2843803Chillarón, M., Vidal, V., & Verdú, G. (2020). CT image reconstruction with SuiteSparseQR factorization package. Radiation Physics and Chemistry, 167, 108289. doi:10.1016/j.radphyschem.2019.04.039Joseph, P. M. (1982). An Improved Algorithm for Reprojecting Rays through Pixel Images. IEEE Transactions on Medical Imaging, 1(3), 192-196. doi:10.1109/tmi.1982.4307572S. Toledo, F. Gustavson, The design and implementation of solar, a portable library for scalable out-of-core linear algebra computations, in: Proceedings of the Annual Workshop on I/O in Parallel and Distributed Systems, IOPADS,D’Azevedo, E., & Dongarra, J. (2000). The design and implementation of the parallel out-of-core ScaLAPACK LU, QR, and Cholesky factorization routines. Concurrency: Practice and Experience, 12(15), 1481-1493. doi:10.1002/1096-9128(20001225)12:153.0.co;2-vGunter, B. C., & Van De Geijn, R. A. (2005). Parallel out-of-core computation and updating of the QR factorization. ACM Transactions on Mathematical Software, 31(1), 60-78. doi:10.1145/1055531.1055534Quintana-Ortí, G., Igual, F. D., Marqués, M., Quintana-Ortí, E. S., & van de Geijn, R. A. (2012). A Runtime System for Programming Out-of-Core Matrix Algorithms-by-Tiles on Multithreaded Architectures. ACM Transactions on Mathematical Software, 38(4), 1-25. doi:10.1145/2331130.2331133Marqués, M., Quintana-Ortí, G., Quintana-Ortí, E. S., & van de Geijn, R. (2010). Using desktop computers to solve large-scale dense linear algebra problems. The Journal of Supercomputing, 58(2), 145-150. doi:10.1007/s11227-010-0394-2G. Lauritsch, H. Bruder, FORBILD head phantom, http://www.imp.uni-erlangen.de/phantoms/head/head.html.Yan, K., Wang, X., Lu, L., & Summers, R. M. (2018). DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. Journal of Medical Imaging, 5(03), 1. doi:10.1117/1.jmi.5.3.036501Miqueles, E., Koshev, N., & Helou, E. S. (2018). A Backprojection Slice Theorem for Tomographic Reconstruction. IEEE Transactions on Image Processing, 27(2), 894-906. doi:10.1109/tip.2017.2766785N. Koshev, E.S. Helou, E.X. Miqueles, Fast backprojection techniques for high resolution tomographyarXiv preprint: 1608.03589

Analysis of FPGA filter in computed tomography images for radioactive dose reduction

[EN] X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.[ES] Las imágenes de Rayos-X o de tomografía computarizada (CT) pueden contener ruido debido al proceso de adquisición. Este ruido complica sustancialmente el proceso diagnóstico, por lo que será necesario el desarrollo de filtros efectivos. En este trabajo se estudia el comportamiento del filtro Fuzzy Peer Group Averaging (Fuzzy PGA) sobre una colección de imágenes mamográficas que ha sido previamente contaminada con ruido impulsivo y gaussiano. El objetivo del trabajo es averiguar si Fuzzy PGA es adecuado para la mejora de imágenes CT obtenidas con una dosis de radiación reducida. Los resultados indican que Fuzzy PGA se comporta, efectivamente, mejor que el resto de métodos estudiados en este trabajo y por tanto resulta un candidato adecuado.Parcero Iglesias, E.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Arnal García, J. (2014). Analysis of FPGA filter in computed tomography images for radioactive dose reduction. Grupo Senda. http://hdl.handle.net/10251/49701

A More Realistic RTP/RTCP-Based Simulation Platform for Video Streaming QoS Evaluation

[EN] Over the last few years, the demand for real-time multimedia services has been growing progressively so that video streaming applications are expected to be dominant in future communications systems, and most of them using RTP/RTCP protocols. This paper presents an evolved tool-set for video streaming QoS evaluation in simulated environments using such protocols. We have designed a new NS-2 module with a full RTP/RTCP implementation (following strictly the RFC 3550) and we propose to combine it with additional multimedia tools to obtain an advanced simulation framework that allows the measurement of network-level QoS metrics (such as throughput, delay, jitter or loss rate) in simulation time. Besides, as the transmitted video files can be reconstructed and played out at the receiver side, the measurement of the quality of the delivered video streams, by employing the most common objective quality metrics (such as PSNR, SSIM or VQM) or subjective metrics (MOS), is also supported. By using this tool-set, researchers and practitioners can assess their novel designs (such as network protocols, routing strategies or video coding mechanisms) for such applications in heterogeneous scenarios over different network conditions. As RTCP feedback capabilities have been added, source based control techniques (such as rate adaptability or Multiple Description Coding) could be included and tested using this more realistic and powerful simulation platform.This work has been financed, partially, by Polytechnics University of Valencia (UPV), under its R&D Support Program in PAID-06-08-002-585 Project and in PAID-01-10, and by Generalitat Valenciana, under its R&D Support Program in GV 2010/009 Project.Boronat Segui, F.; Montagud Aguar, M.; Vidal Gimeno, VE. (2011). A More Realistic RTP/RTCP-Based Simulation Platform for Video Streaming QoS Evaluation. Journal of Mobile Multimedia. 7(1):66-88. http://hdl.handle.net/10251/45964S66887

Análisis de Matriz del Sistema para TAC

En aplicaciones prácticas de tomografía computarizada a menudo el conjunto de proyecciones es incompleto debido a las condiciones físicas durante el proceso de adquisición de datos. Otro problema importante, es reducir la dosis de radiación en pacientes. Estos problemas requieren la reconstrucción de imágenes por un conjunto limitado de proyecciones. Por esta razón, los métodos iterativos están siendo utilizados cada vez más por los investigadores del campo de reconstrucción de imágenes. En este trabajo, resolvemos el problema de reconstrucción por menos número de proyecciones y analizamos como la solución del problema depende de la generación de los elementos de la matriz del sistema que simula el proceso de escaneoEste trabajo fue soportadopor el proyecto ANITRAN PROMETEOII/2014/008 de la Generalitat Valenciana de España y por el Ministerio de Economía y Competitividad español con la subvención TIN2015-66972-C5-4-R cofinanciado por fondos FEDER.Vidal Gimeno, VE.; Flores, LA.; Verdú Martín, GJ. (2016). Análisis de Matriz del Sistema para TAC. Senda Editorial. http://hdl.handle.net/10251/87719

GPU based algorithms in CT imaging

[EN] In X-ray computed tomography (CT) imaging, projections taken by a scanner are used to reconstruct the internal structure of an object. Due to the complexity of the data, the problem of reconstruction is a time consuming process. Although modern processors have gained sufficient power to be competitive in 2D reconstruction, it is not the case for 3D reconstruction especially when iterative methods are used. Today, the technology allows reducing this drawback effectively. In this work we compare two iterative algorithms of image reconstruction based on GPU implementation.This work was partially funded by ANITRAN PROMETEO/2010/039, the Spanish Ministry of Science and Innovation (Project ENE2011-22823, TIN2011-26254).Flores, LA.; Vidal Gimeno, VE.; Verdú Martín, GJ. (2015). GPU based algorithms in CT imaging. Annals of Multicore and GPU Programming. 2(1):25-31. http://hdl.handle.net/10251/64838S25312

Impact of view reduction in CT on radiation dose for patients

[EN] Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the reduction of radiation exposure on patients during the data acquisition. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To solve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. The feasibility of the proposed methods is demonstrated numerically. (C) 2016 Elsevier Ltd. All rights reserved.This work has been supported by Universitat Politecnica de Valencia and the project N3D-VALKIN PROMETEOII/2014/008 of the Generalitat Valenciana of Spain.Parcero Iglesias, E.; Flores, L.; Sánchez, MG.; Vidal-Gimeno, V.; Verdú Martín, GJ. (2017). Impact of view reduction in CT on radiation dose for patients. Radiation Physics and Chemistry. 137:173-175. https://doi.org/10.1016/j.radphyschem.2016.01.038S17317513

Análisis del Filtro FPGA en Imágenes de Tomografía Computarizada para la Reducción de Dosis Radiactiva

[EN] X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.[ES] Las imágenes de Rayos-X o de tomografía computarizada (CT) pueden contener ruido debido al proceso de adquisición. Este ruido complica sustancialmente el proceso diagnóstico, por lo que será necesario el desarrollo de filtros efectivos. En este trabajo se estudia el comportamiento del filtro Fuzzy Peer Group Averaging (FPGA) sobre una colección de imágenes mamográficas que ha sido previamente contaminada con ruido impulsivo y gaussiano. El objetivo del trabajo es averiguar si FPGA es adecuado para la mejora de imágenes CT obtenidas con una dosis de radiación reducida. Los resultados indican que FPGA se comporta, efectivamente, mejor que el resto de métodos estudiados en este trabajo y por tanto resulta un candidato adecuado.This work was partially funded by ANITRAN PROMETEO/2010/039, the Spanish Ministry of Science and Innovation (Project TIN2008-06570-C04-04), and the spin-off Titania (Grupo Dominguis).Parcero Iglesias, E.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Josep Arnal García; Mayo Nogueira, P. (2014). Análisis del Filtro FPGA en Imágenes de Tomografía Computarizada para la Reducción de Dosis Radiactiva. Sociedad Nuclear Española. http://hdl.handle.net/10251/70824