Laplace-Based Interpolation Method in Reduction of Metal Artifact in Computed Tomography Imaging
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Abstract
Introduction: Metal artifacts can degrade the image quality of computed tomography (CT) images which lead to errors in diagnosis. This study aims to evaluate the performance of Laplace interpolation (LI) method for metal artifacts reduction (MAR) in CT images in comparison with cubic spline (CS) interpolation. Methods: In this study, the proposed MAR algorithm was developed using MATLAB platform. Firstly, the virtual sinogram was acquired from CT image using Radon transform function. Then, dual-adaptive thresholding detected and segmented the metal part within the CT sinogram. Performance of the two interpolation methods to replace the missing part of segmented sinogram were evaluated. The interpolated sinogram was reconstructed, prior to image fusion to obtain the final corrected image. The qualitative and quantitative evaluations were performed on the corrected CT images (both phantom and clinical images) to evaluate the effectiveness of the proposed MAR technique. Results: From the findings, LI method had successfully replaced the missing data on both simple and complex thresholded sinogram as compared to CS method (p-value = 0.17). The artifact index was significantly reduced by LI method (p-value = 0.02). For qualitative analysis, the mean scores by radiologists for LI-corrected images were higher than original image and CS-corrected images. Conclusion: In conclusion, LI method for MAR produced better results as compared to CS interpolation method, as it worked more effective by successfully interpolated all the missing data within sinogram in most of the CT images.
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Boas, F. E., & Fleischmann, D. (2012). CT artifacts: Causes and reduction techniques. Imaging in Medicine, 4(2), 229-240. doi: 10.2217/iim.12.13
Yazdi, M., & Beaulieu, L. (2008). Artifacts in spiral X-ray CT scanners: problems and solutions. International J of Biological and Medical Sciences, 4(3), 135-139.
Barret, J. F., & Keat, N. (2004). Artifacts in CT: Recognition and avoidance. RadioGraphics. 24, 1679–1691. doi: 10.1148/rg.246045065. doi:10.5281/zenodo.1080245
Link, T. M., Berning W., Scherf S., Joosten U., Joist A., Engelke K., et al. (2000). CT of metal implants: reduction of artifacts using an extended CT scale technique. J. of Computer Assisted Tomography. 24(1), 165-72. doi: 10.1097/00004728-200001000-00029
Osman, N. D., Suhaimi, N. M., Saidun, H. A., & Razali, M. A. S. M. (2019). Metal Artefact Reduction with Different Transverse Angles of Metal Placement and Gantry Tilt Angulation in Spine CT Imaging. Mal J Med Health Sci. 15(SUPP9), 1-6.
Saidun, H. A., Shuaib, I. L., Daud, N. M., Sobri, N. F. M., & Osman, N. D. (2019). Evaluation of metal artefacts reduction by application of monoenergetic extrapolation of dual-energy CT: A phantom study with different metal implants. In J. of Physics: Conference Series. 1248(1), p.012004. IOP Publishing. doi: 10.1088/1742-6596/1248/1/012004
Bamberg, F., Dierks, A., Nikolaou, K., Reiser, M. F., Becker, C. R., and Johnson, T. R. C. (2011). Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol. 21, 1424–1429. doi: 10.1007/s00330-011-2062-1
Han, S. C., Chung, Y. E., Lee, Y. H., Park, K. K., Kim, M. J., Kim, K. W. (2014). Metal artifact reduction software used with abdominopelvic dual-energy CT of patients with metal hip prostheses: Assessment of image quality and clinical feasibility. American J of Radiology. 203, 788-795. doi: 10.2214/AJR.13.10980
Gjesteby, L., De Man, B., Jin, Y., Paganetti, H., Verburg, J., Giantsoudi, D., & Wang, G. (2016). Metal artifact reduction in CT: Where are we after four decades?. IEEE Access, 4, 5826-5849. doi:10.1109/ACCESS.2016.2608621
Vellarackal, A. J., & Kaim, A. H. (2021). Metal artefact reduction of different alloys with dual energy computed tomography (DECT). Scientific Reports, 11(1), 1-11. doi: 10.1038/s41598-021-81600-1
Kalendar, W. A., Hebel, R., and Ebersberger, J. (1987). Reduction of CT artifacts caused by metallic implants. Radiology. 164, 576-577. doi: 10.1148/radiology.164.2.3602406.
Lee, D., Park, C., Lim, Y. et al. (2020). A Metal Artifact Reduction Method Using a Fully Convolutional Network in the Sinogram and Image Domains for Dental Computed Tomography. J Digit Imaging, 33, 538–546. doi: 10.1007/s10278-019-00297-x.
Wang, G., Vannier, M. W., Cheng, P. C., et al. (1999). Iterative x-ray cone-beam tomography for metal artifact reduction and local region reconstruction. Microscopy and Microanalysis, 5(1), 58-65. doi: 10.1017/S1431927699000057.
Robertson, D. D., Yuan, J., Wang, G., et al. (1997). Total hip prosthesis metal-artifact suppression using iterative deblurring reconstruction. J of Comput. Assist. Tomogr., 21(2), 293-298. doi: 10.1097/00004728-199703000-00024.
Wang, G., Snyder, D. L., O’Sullivan, J. A., et al. (1996). Iterative deblurring for CT metal artifact reduction. IEEE Transactions in Medical Imaging, 15(5), 657-664. doi: 10.1109/42.538943.
Zhang, Y., Pu, Y. F., Hu, J. R., et al. (2011). Efficient CT metal artifact reduction based on fractional-order curvature diffusion. Computational and Mathematical Methods in Medicine. 173748. doi: 10.1155/2011/173748.
Arabi, H., & Zaidi, H. (2021). Deep learning–based metal artefact reduction in PET/CT imaging. European radiology, 31(8), 6384-6396. doi: 10.1007/s00330-021-07709-z.
Du, M., Liang, K., Liu, Y., & Xing, Y. (2021). Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods. arXiv preprint arXiv:2111.12983. doi: 10.48550/arXiv.2111.12983
Ghani, M. U., & Karl, W. C. (2019). Fast enhanced CT metal artifact reduction using data domain deep learning. IEEE Transactions on Computational Imaging, 6, 181-193. doi: 10.1109/TCI.2019.2937221
Liang, K., Zhang, L., Yang, H., Yang, Y., Chen, Z., & Xing, Y. (2019). Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning. Medical Physics, 46(12), e823-e834. doi: 10.1002/mp.13644.
Glover, G. H., & Pelc, N. J. (1981). An algorithm for the reduction of metal clip artifacts in CT reconstructions. Med Phys, 8(6), 799–807. doi: 10.1118/1.595032.
Rousselle, A., Amelot, A., Thariat, J., Jacob, J., Mercy, G., De Marzi, L., & Feuvret, L. (2020). Metallic implants and CT artefacts in the CTV area: Where are we in 2020?. Cancer Radiothérapie, 24(6-7), 658-666. doi: 10.1016/j.canrad.2020.06.022.
Zhao, S., Robeltson, D., Wang, G., Whiting, B., & Bae, K. (2000). X-ray CT metal artifact reduction using wavelets: an application for imaging total hip prostheses. IEEE Trans. on Medical Imaging, 19(12), 1238-1247. doi: 10.1109/42.897816.
Gu J, Zhang L, Yu G, Xing Y, Chen Z. (2006). X-ray CT metal artifacts reduction through curvature based sinogram inpainting. J. of X-Ray Science and Technology. 14(2), 73–82.
Meyer, E., Raupach, R., Lell, M., Schmidt, B., & Kachelrieß, M. (2010). Normalized metal artifact reduction (NMAR) in computed tomography. Med Phys, 37(10), 5482–5493. doi: 10.1118/1.3484090
Veldkamp, W. J. H., Joemai, R. M. S., van der Molen, A. J., Geleijns, J. (2010). Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT. Med Phys., 37(2), 620–628. doi: 10.1118/1.3276777.
Osman, N. D., Salikin, M. S., Saripan, M. I., Aziz, M. Z. A., Daud, N. M., (2014, December). Metal artefact correction algorithm based-on DSAT technique for CT images. In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES) (pp. 324-327). IEEE. doi:10.1109/IECBES.2014.7047513
Osman, N. D., Sobri, N. F. M., Achuthan, A., Saidun, H. A., Aziz, M. Z. A., & Shuaib, I. L. (2018, December). Evaluation of Two Sinogram Interpolation Methods for Metal Artefacts Reduction in Computed Tomography. In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). pp. 137-139. IEEE.doi: 10.1109/IECBES.2018.8626670
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical recipes 3rd edition: The art of scientific computing. Cambridge university press.
Veldkamp, W J. H., Joemai, R. M. S., & Geleijns, J. (2009). SU‐FF‐I‐38: Automated Segmentation and Interpolation in Sinograms for Metal Artifact Suppression in CT. Med Phys, 36(6Part3), 2443-2443. doi: 10.1118/1.3181157
Abdoli, M., Ay, M. R., Ahmadian, A., & Zaidi, H. (2010). A virtual sinogram method to reduce dental metallic implant artefacts in computed tomography-based attenuation correction for PET. Nuclear Medicine Communications, 31(1), 22–31. doi: 10.1097/MNM.0b013e32832fa241.
Agrawal, N., Sinha, P., Kumar, A., & Bagai, S. (2015). Fast & dynamic image restoration using Laplace equation-based image inpainting. J Undergraduate Res Innovation, 1(2), 115-123.
Dong, Y., Shi, A. J., Wu, J. L., Wang, R. X., Sun, L. F., Liu, A. L., & Liu, Y. J. (2016). Metal artifact reduction using virtual monochromatic images for patients with pedicle screws implants on CT. European Spine J, 25(6), 1754-1763. doi: 10.1007/s00586-015-4053-4.
Yoo, H. J., Hong, S. H., Chung, B. M., Moon, S. J., Choi, J. Y., Chae, H. D., & Chang, M. Y. (2018). Metal artifact reduction in virtual monoenergetic spectral dual-energy CT of patients with metallic orthopedic implants in the distal radius. American J of Roentgenology, 1083-1091. doi: 10.2214/AJR.18.19514
Takayanagi, T., Suzuki, S., Katada, Y., Ishikawa, T., Fukui, R., Yamamoto, Y., & Abe, O. (2019). Comparison of Motion Artifacts on CT Images Obtained in the Ultrafast Scan Mode and Conventional Scan Mode for Unconscious Patients in the Emergency Department. American J of Roentgenology, 213(4), W153-W161. doi: 10.2214/AJR.19.21456.
Boas, F. E., & Fleischmann, D. (2011). Evaluation of two iterative techniques for reducing metal artifacts in computed tomography. Radiology, 259(3), 894-902. doi: 10.1148/radiol.11101782.