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

Four-dimensional tomographic reconstruction by time domain decomposition

Since the beginnings of tomography, the requirement that the sample does not change during the acquisition of one tomographic rotation is unchanged. We derived and successfully implemented a tomographic reconstruction method which relaxes this decades-old requirement of static samples. In the presented method, dynamic tomographic data sets are decomposed in the temporal domain using basis functions and deploying an L1 regularization technique where the penalty factor is taken for spatial and temporal derivatives. We implemented the iterative algorithm for solving the regularization problem on modern GPU systems to demonstrate its practical use

GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging

Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. In many scientific applications, however, the number of projections that can be measured is limited due to geometric constraints, tolerable radiation dose and/or acquisition speed. Thus it becomes an important problem to obtain the best-possible reconstruction from a limited number of projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between real and reciprocal space, GENFIRE searches for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques by numerical simulations, and by experimentally by reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. Equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.Comment: 18 pages, 6 figure

Maximum-likelihood absorption tomography

Maximum-likelihood methods are applied to the problem of absorption tomography. The reconstruction is done with the help of an iterative algorithm. We show how the statistics of the illuminating beam can be incorporated into the reconstruction. The proposed reconstruction method can be considered as a useful alternative in the extreme cases where the standard ill-posed direct-inversion methods fail.Comment: 7 pages, 5 figure

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. 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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

Iterative CT reconstruction from few projections for the nondestructive post irradiation examination of nuclear fuel assemblies

The core components (e.g. fuel assemblies, spacer grids, control rods) of the nuclear reactors encounter harsh environment due to high temperature, physical stress, and a tremendous level of radiation. The integrity of these elements is crucial for safe operation of the nuclear power plants. The Post Irradiation Examination (PIE) can reveal information about the integrity of the elements during normal operations and off‐normal events. Computed tomography (CT) is a tool for evaluating the structural integrity of elements non-destructively. CT requires many projections to be acquired from different view angles after which a mathematical algorithm is adopted for reconstruction. Obtaining many projections is laborious and expensive in nuclear industries. Reconstructions from a small number of projections are explored to achieve faster and cost-efficient PIE. Classical reconstruction algorithms (e.g. filtered back projection) cannot offer stable reconstructions from few projections and create severe streaking artifacts. In this thesis, conventional algorithms are reviewed, and new algorithms are developed for reconstructions of the nuclear fuel assemblies using few projections. CT reconstruction from few projections falls into two categories: the sparse-view CT and the limited-angle CT or tomosynthesis. Iterative reconstruction algorithms are developed for both cases in the field of compressed sensing (CS). The performance of the algorithms is assessed using simulated projections and validated through real projections. The thesis also describes the systematic strategy towards establishing the conditions of reconstructions and finds the optimal imaging parameters for reconstructions of the fuel assemblies from few projections. --Abstract, page iii