Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study

Objective: The purpose of this study was to determine the diagnostic utility of low-dose CT with knowledge-based iterative model reconstruction (IMR) for the evaluation of parotid gland tumors. Materials and Methods: This prospective study included 42 consecutive patients who had undergone low-dose contrast-enhanced CT for the evaluation of suspected parotid gland tumors. Prior or subsequent non-low-dose CT scans within 12 months were available in 10 of the participants. Background noise (BN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between non-low-dose CT images and images generated using filtered back projection (FBP), hybrid iterative reconstruction (iDose4; Philips Healthcare), and knowledge-based IMR. Subjective image quality was rated by two radiologists using five-point grading scales to assess the overall image quality, delineation of lesion contour, image sharpness, and noise. Results: With the IMR algorithm, background noise (IMR, 4.24 ± 3.77; iDose4, 8.77 ± 3.85; FBP, 11.73 ± 4.06; p = 0.037 [IMR vs. iDose4] and p < 0.001 [IMR vs. FBP]) was significantly lower and SNR (IMR, 23.93 ± 7.49; iDose4, 10.20 ± 3.29; FBP, 7.33 ± 2.03; p = 0.011 [IMR vs. iDose4] and p < 0.001 [IMR vs. FBP]) was significantly higher compared with the other two algorithms. The CNR was also significantly higher with the IMR compared with the FBP (25.76 ± 11.88 vs. 9.02 ± 3.18, p < 0.001). There was no significant difference in BN, SNR, and CNR between low-dose CT with the IMR algorithm and non-low-dose CT. Subjective image analysis revealed that IMR-generated low-dose CT images showed significantly better overall image quality and delineation of lesion contour with lesser noise, compared with those generated using FBP by both reviewers 1 and 2 (4 vs. 3; 4 vs. 3; and 3-4 vs. 2; p < 0.05 for all pairs), although there was no significant difference in subjective image quality scores between IMR-generated low-dose CT and non-low-dose CT images. Conclusion: Iterative model reconstruction-generated low-dose CT is an alternative to standard non-low-dose CT without significantly affecting image quality for the evaluation of parotid gland tumors.ope

CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization

Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods

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

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