The Use of Artificial Intelligence in Computed Tomography Image Reconstruction: A Systematic Review
DOI:
https://doi.org/10.17140/ROJ-4-129Keywords:
Computed tomography (CT), Artificial Intelligence (AI), Image reconstruction (IN), Machine learning (ML), Deep learning (DL), Dose reductionAbstract
Background
Current image reconstruction techniques in computed tomography (CT) such as filtered back-projection (FBP) and iterative reconstruction
(IR) have limited use in low-dose CT imaging due to poor image quality and reconstruction times not fit for clinical
implementation. Hence, with the increasing need for radiation dose reductions in CT, the use of artificial intelligence (AI) in image
reconstruction has been an area of growing interest.
Aim
The aim of this review is to examine the use of AI in CT image reconstruction and its effectiveness in enabling further dose reductions
through improvements in image quality of low-dose CT images.
Method
A review of the literature from 2016 to 2020 was conducted using the databases Scopus, Ovid MEDLINE, and PubMed. A subsequent
search of several well-known journals was performed to obtain additional information. After careful assessment, articles
were excluded if they were not obtainable from the databases or not available in English.
Results
This review found that deep learning-based algorithms demonstrate promising results in improving the image quality of low-dose
images through noise suppression, artefact reduction, and structure preservation in addition to optimising IR methods.
Conclusion
In conclusion, with the two AI-based CT systems currently in clinical use showing favourable benefits, it is expected that AI algorithms
will continue to proliferate and enable significant dose reductions in CT imaging.
References
Brenner DJ, Hall EJ. Computed tomography--an increasing source of radiation exposure. N Engl J Med. 2007; 357(22): 2277- 2284. doi: 10.1056/NEJMra072149
Power SP, Moloney F, Twomey M, James K, O’Connor OJ, Ma-her MM. Computed tomography and patient risk: Facts, percep-tions and uncertainties. World J Radiol. 2016; 8(12): 902-915. doi: 10.4329/wjr.v8.i12.902
Willemink MJ, Noël PB. The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence. Eur Radiol. 2019; 29(5): 2185-2195. doi: 10.1007/s00330-018-5810-7
Stiller W. Basics of iterative reconstruction methods in com-puted tomography: A vendor-independent overview. Eur J Radiol. 2018; 109: 147-154. doi: 10.1016/j.ejrad.2018.10.025
Yi X, Babyn P. Sharpness-aware low-daose CT denoising using conditional generative adversarial network. Journal of Digital Imag-ing. 2018; 31(5): 655-669. doi: 10.1007/s10278-018-0056-0
Gu J, Ye JC. Multi-scale wavelet domain residual learning for lim-ited-angle CT reconstruction. adsabs Website. https://ui.adsabs. harvard.edu/abs/2017arXiv170301382G. Accessed March 1, 2017.
Ahn CK, Yang Z, Heo C, Jin H, Park B, Kim JH. A deep learn-ing-enabled iterative reconstruction of ultra-low-dose CT: Use of synthetic sinogram-based noise simulation technique. Paper presented at: Physics of Medical Imaging; 2018; Houston, Texas, USA. doi: 10.1117/12.2294013
Wu D, Kim K, Fakhri GE, Li Q. Iterative low-dose CT re-construction with priors trained by artificial neural network. IEEE Trans Med Imaging. 2017; 36(12): 2479-2486. doi: 10.1109/ TMI.2017.2753138
Canon Medical Systems USA. Aquilion ONE – Deep Intelli-gence. us.medical Web site. https://us.medical.canon/products/ computed-tomography/aquilion-one-prism/technology/#aice. Accessed March 27, 2020.
GE Healthcare. TrueFidelity. gehealthcare Web site. https:// www.gehealthcare.com/products/truefidelity. Accessed March 27, 2020.
Ongsulee P. Artificial intelligence, machine learning and deep learning. Paper presented at: 15th International Conference on ICT and Knowledge Engineering (ICT&KE); 2017; Bangkok, Thai-land. doi: 10.1109/ictke.2017.8259629
Sogani J, Allen B, Dreyer K, McGinty G. Artificial intelligence in radiology: the ecosystem essential to improving patient care. Clin Imaging. 2020; 59(1): A3-A6. doi: 10.1016/j.clinimag.2019.08.001
Giger ML. Machine learning in medical imaging. J Am Coll Ra-diol. 2018;15(3, Part B): 512-520. doi: 10.1016/j.jacr.2017.12.028
Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, et al. Machine learning in radiology: Applications beyond image interpretation. J Am Coll Radiol. 2018; 15(2): 350-359. doi: 10.1016/j.jacr.2017.09.044
Kim M, Yun J, Cho Y, Shin K, Jang R, Bae H-J, et al. Deep learning in medical imaging. Neurospine. 2019; 16(4): 657-668. doi: 10.14245/ns.1938396.198
Kim J, Hong J, Park H. Prospects of deep learning for medi-cal imaging. Precision and Future Medicine. 2018; 2. doi: 10.23838/ pfm.2018.00030
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18(8): 500-510. doi: 10.1038/s41568-018-0016-5 37 Lee T et al
Geyer LL, Schoepf UJ, Meinel FG, Nance JW, Bastarrika G, Leipsic JA, et al. State of the art: Iterative CT reconstruction techniques. Radiology. 2015; 276(2): 339-357. doi: 10.1148/radi-ol.2015132766
Padole A, Ali Khawaja RD, Kalra MK, Singh S. CT Radiation dose and iterative reconstruction techniques. Am J Roentgenol. 2015; 204(4): W384-W392. doi: 10.1148/radiol.2015132766
Beister M, Kolditz D, Kalender WA. Iterative reconstruction methods in X-ray CT. Phys Med. 2012; 28(2): 94-108. doi: 10.1016/j. ejmp.2012.01.003
Hsieh J, Nett B, Yu Z, Sauer K, Thibault J-B, Bouman CA. Re-cent advances in CT image reconstruction. Curr Radiol Rep. 2013; 1(1): 39-51. doi: 10.1007/s40134-012-0003-7
Lell MM, Kachelrieß M. Recent and upcoming technological developments in computed tomography: High speed, low dose, deep learning, multienergy. Invest Radiol. 2020; 55(1): 8-19. doi: 10.1097/RLI.0000000000000601
Dodge CT, Tamm EP, Cody DD, Liu X, Jensen CT, Wei W, et al. Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction - a phantom study. J Appl Clin Med Phys. 2016; 17(2): 511-531. doi: 10.1120/jacmp.v17i2.5709
Willemink MJ, Leiner T, de Jong PA, de Heer LM, Nievelstein RA, Schilham AM, et al. Iterative reconstruction techniques for computed tomography part 2: Initial results in dose reduction and image quality. Eur Radiol. 2013; 23(6): 1632-1642. doi: 10.1007/ s00330-012-2764-z
Willemink MJ, Takx RA, de Jong PA, Budde RP, Bleys RL, Das M, et al. Computed tomography radiation dose reduction: effect of different iterative reconstruction algorithms on image quality. J Comput Assist Tomogr. 2014; 38(6): 815-823. doi: 10.1097/ RCT.0000000000000128
Sauter A, Koehler T, Fingerle AA, Brendel B, Richter V, Rasper M, et al. Ultra low dose CT pulmonary angiography with iterative reconstruction. PLoS One. 2016; 11(9): e0162716. doi: 10.1371/ journal.pone.0162716
den Harder AM, Willemink MJ, de Ruiter QMB, Schilham AMR, Krestin GP, Leiner T, et al. Achievable dose reduction us-ing iterative reconstruction for chest computed tomography: A systematic review. Eur J Radiol. 2015; 84(11): 2307-2313. doi: 10.1016/j.ejrad.2015.07.011
den Harder AM, Willemink MJ, De Ruiter QM, De Jong PA, Schilham AM, Krestin GP, et al. Dose reduction with iterative re-construction for coronary CT angiography: A systematic review and meta-analysis. Br J Radiol. 2016; 89(1058): 20150068. doi: 10.1259/bjr.20150068
Willemink MJ, de Jong PA, Leiner T, de Heer LM, Nievelstein RA, Budde RP, et al. Iterative reconstruction techniques for com-Systematic Review | Volume 4 | Issue 2| puted tomography Part 1: Technical principles. Eur Radiol. 2013; 23(6): 1623-1631. doi: 10.1007/s00330-012-2765-y
Skornitzke S. [Iterative algorithms for artifact reduction in computed tomography]. [In: German]. Radiologe. 2018; 58(3): 202- 210. doi: 10.1007/s00117-017-0327-7
Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, et al. Low-dose CT with a residual encoder-decoder convolutional neu-ral network. IEEE Trans Med Imaging. 2017; 36(12): 2524-2535. doi: 10.1109/TMI.2017.2715284
Cong W, Wang G. Monochromatic CT image reconstruc-tion from current-integrating data via deep learning. arX-iv e-prints. adsabs Web site. https://ui.adsabs.harvard.edu/ abs/2017arXiv171003784C. Accessed October 1, 2017.
Kang E, Min J, Ye JC. A deep convolutional neural network us-ing directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017; 44(10): e360-e375. doi: 10.1002/mp.12344
Han YS, Yoo J, Ye JC. Deep Residual learning for com-pressed sensing CT reconstruction via persistent homol-ogy analysis. adsabs Web site. https://ui.adsabs.harvard.edu/ abs/2016arXiv161106391H. Accessed November 1, 2016. Submit your article to this journal | https://openventio.org/submit-manuscript/ Systematic Review | Volume 4 | Issue 2| Lee T et al
Wolterink JM, Leiner T, Viergever MA, Isgum I. Genera-tive adversarial networks for noise reduction in low-dose CT. IEEE Trans Medi Imaging. 2017; 36(12): 2536-2545. doi: 10.1109/ TMI.2017.2708987
Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, et al. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017; 8(2): 679-694. doi: 10.1364/BOE.8.000679
Du W, Chen H, Wu Z, Sun H, Liao P, Zhang Y. Stacked com-petitive networks for noise reduction in low-dose CT. PLoS One. 2017; 12(12): e0190069. doi: 10.1371/journal.pone.0190069
Ziabari A, Ye DH, Srivastava S, Sauer K, Thibault J-B, Bouman C. 2.5D deep learning for CT image reconstruction using a multi-GPU implementation. 2018.
Hsieh J, Liu E, Nett B, Tang J, Thibault, J, Sahney S. A new era of image reconstruction: TrueFidelity. gehealthcare Web site. https://www.gehealthcare.com.au/-/jssmedia/040dd213fa89463 287155151fdb01922.pdf. Accessed March 29, 2020.
Boedeker K. AiCE deep learning reconstruction: Bring-ing the power of ultra-high RESolution CT to routine imaging. ssl.cdn Web site. Available from: https://mfl.ssl.cdn.sdlmedia. com/636837173033229994OU.pdf. Accessed March 29, 2020.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in Radiology (ROJ) is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows anyone to copy, distribute, transmit, and adapt the work — including for commercial purposes — provided appropriate credit is given to the original author(s) and source.