Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study

  • Park, Chae Jung (Department of Radiology, Yonsei University College of Medicine) ;
  • Kim, Ki Wook (Department of Radiology, Yonsei University College of Medicine) ;
  • Lee, Ho-Joon (Department of Radiology, Yonsei University College of Medicine) ;
  • Kim, Myeong-Jin (Department of Radiology, Yonsei University College of Medicine) ;
  • Kim, Jinna (Department of Radiology, Yonsei University College of Medicine)
  • Received : 2017.09.09
  • Accepted : 2018.02.10
  • Published : 2018.10.01
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Abstract

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 ($iDose^4$; 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, $4.24{\pm}3.77$; $iDose^4$, $8.77{\pm}3.85$; FBP, $11.73{\pm}4.06$; p = 0.037 [IMR vs. $iDose^4$] and p < 0.001 [IMR vs. FBP]) was significantly lower and SNR (IMR, $23.93{\pm}7.49$; $iDose^4$, $10.20{\pm}3.29$; FBP, $7.33{\pm}2.03$; p = 0.011 [IMR vs. $iDose^4$] 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 ($25.76{\pm}11.88$ vs. $9.02{\pm}3.18$, 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.

Keywords

Acknowledgement

Supported by : Yonsei University College of Medicine

References

  1. Lin CC, Tsai MH, Huang CC, Hua CH, Tseng HC, Huang ST. Parotid tumors: a 10-year experience. Am J Otolaryngol 2008;29:94-100 https://doi.org/10.1016/j.amjoto.2007.03.002
  2. Barrett JF, Keat N. Artifacts in CT: recognition and avoidance. Radiographics 2004;24:1679-1691 https://doi.org/10.1148/rg.246045065
  3. Geyer LL, Schoepf UJ, Meinel FG, Nance JW Jr, Bastarrika G, Leipsic JA, et al. State of the art: iterative CT reconstruction techniques. Radiology 2015;276:339-357 https://doi.org/10.1148/radiol.2015132766
  4. Halpern EJ, Gingold EL, White H, Read K. Evaluation of coronary artery image quality with knowledge-based iterative model reconstruction. Acad Radiol 2014;21:805-811 https://doi.org/10.1016/j.acra.2014.02.017
  5. Khawaja RDA, Singh S, Blake M, Harisinghani M, Choy G, Karaosmanoglu A, et al. Ultra-low dose abdominal MDCT: using a knowledge-based iterative model reconstruction technique for substantial dose reduction in a prospective clinical study. Eur J Radiol 2015;84:2-10 https://doi.org/10.1016/j.ejrad.2014.09.022
  6. Park SB, Kim YS, Lee JB, Park HJ. Knowledge-based iterative model reconstruction (IMR) algorithm in ultralow-dose CT for evaluation of urolithiasis: evaluation of radiation dose reduction, image quality, and diagnostic performance. Abdom Imaging 2015;40:3137-3146 https://doi.org/10.1007/s00261-015-0504-y
  7. Ryu YJ, Choi YH, Cheon JE, Ha S, Kim WS, Kim IO. Knowledgebased iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom. Pediatr Radiol 2016;46:303-315 https://doi.org/10.1007/s00247-015-3486-6
  8. Shrimpton PC, Hillier MC, Lewis MA, Dunn M. National survey of doses from CT in the UK: 2003. Br J Radiol 2006;79:968-980 https://doi.org/10.1259/bjr/93277434
  9. Jessen KA, Panzer W, Shrimpton PC, Bongartz G, Gelejins J, Tosi G, et al. EUR 16262: European guidelines on quality criteria for computed tomography. Luxembourg: Office for Official Publications of the European Communities, 2000
  10. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-174 https://doi.org/10.2307/2529310
  11. Hu Y, Pan S, Zhao X, Guo W, He M, Guo Q. Value and clinical application of orthopedic metal artifact reduction algorithm in CT scans after orthopedic metal implantation. Korean J Radiol 2017;18:526-535 https://doi.org/10.3348/kjr.2017.18.3.526
  12. Lim HJ, Chung MJ, Shin KE, Hwang HS, Lee KS. The impact of iterative reconstruction in low-dose computed tomography on the evaluation of diffuse interstitial lung disease. Korean J Radiol 2016;17:950-960 https://doi.org/10.3348/kjr.2016.17.6.950
  13. Choi DS, Na DG, Byun HS, Ko YH, Kim CK, Cho JM, et al. Salivary gland tumors: evaluation with two-phase helical CT. Radiology 2000;214:231-236 https://doi.org/10.1148/radiology.214.1.r00ja05231
  14. Shepp LA, Logan BF. The Fourier reconstruction of a head section. IEEE Trans Nucl Sci 1974;21:21-43
  15. Seibert JA. Iterative reconstruction: how it works, how to apply it. Pediatr Radiol 2014;44 Suppl 3:431-439 https://doi.org/10.1007/s00247-014-3102-1
  16. Hendrick RE. Breast MRI: fundamentals and technical aspects. New York, NY: Springer Science & Business Media, 2007
  17. Yasaka K, Katsura M, Akahane M, Sato J, Matsuda I, Ohtomo K. Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction. Springerplus 2013;2:209 https://doi.org/10.1186/2193-1801-2-209
  18. Sidky EY, Pan X. Image reconstruction in circular conebeam computed tomography by constrained, total-variation minimization. Phys Med Biol 2008;53:4777-4807 https://doi.org/10.1088/0031-9155/53/17/021
  19. Qi H, Chen Z, Zhou L. CT image reconstruction from sparse projections using adaptive TpV regularization. Comput Math Methods Med 2015;2015:354869
  20. Mehta D, Thompson R, Morton T, Dhanantwari A, Shefer E. Iterative model reconstruction: simultaneously lowered computed tomography radiation dose and improved image quality. Med Phys Int J 2013;2:147-155
  21. Boone JM. Determination of the presampled MTF in computed tomography. Med Phys 2001;28:356-360 https://doi.org/10.1118/1.1350438
  22. Siewerdsen JH, Cunningham IA, Jaffray DA. A framework for noise-power spectrum analysis of multidimensional images. Med Phys 2002;29:2655-2671 https://doi.org/10.1118/1.1513158

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