Prog Med Phys.  2021 Dec;32(4):92-98. 10.14316/pmp.2021.32.4.92.

Estimation of Noise Level and Edge Preservation for Computed Tomography Images: Comparisons in Iterative Reconstruction

• Kim S
• Ahn C
• Jeong WK
• Kim JH
• Chun M

• KMID: 2524154
• DOI: http://doi.org/10.14316/pmp.2021.32.4.92

Abstract

Purpose
This study automatically discriminates homogeneous and structure edge regions on computed tomography (CT) images, and it evaluates the noise level and edge preservation ratio (EPR) according to the different types of iterative reconstruction (IR).
Methods
The dataset consisted of CT scans of 10 patients reconstructed with filtered back projection (FBP), statistical IR (iDose ⁴ ), and iterative model-based reconstruction (IMR). Using the 10th and 85th percentiles of the structure coherence feature, homogeneous and structure edge regions were localized. The noise level was estimated using the averages of the standard deviations for five regions of interests (ROIs), and the EPR was calculated as the ratio of standard deviations between homogeneous and structural edge regions on subtraction CT between the FBP and IR.
Results
The noise levels were 20.86±1.77 Hounsfield unit (HU), 13.50±1.14 HU, and 7.70±0.46 HU for FBP, iDose⁴, and IMR, respectively, which indicates that iDose ⁴and IMR could achieve noise reductions of approximately 35.17% and 62.97%, respectively. The EPR had values of 1.14±0.48 and 1.22±0.51 for iDose⁴ and IMR, respectively.
Conclusions
The iDose 4 and IMR algorithms can effectively reduce noise levels while maintaining the anatomical structure. This study suggested automated evaluation measurements of noise levels and EPRs, which are important aspects in CT image quality with patients’ cases of FBP, iDose⁴ , and IMR. We expect that the inclusion of other important image quality indices with a greater number of patients’ cases will enable the establishment of integrated platforms for monitoring both CT image quality and radiation dose.

Keyword

Computed tomography; Image quality; Subtraction image; Noise level; Structure edge preservation

Figure

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  Fig. 1 Procedures to extract structural transition region in enhanced hepatic region. (a) Original image, (b) candidate evaluation mask, (c) SCF map, and (d) edge regions extracted using SCF threshold greater than the 85th percentile. SCF, structure coherence feature.

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  Fig. 2 Sample results of ROI placement on homogeneous area on the liver parenchyma. ROI, regions of interest.

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  Fig. 3 Sample images reconstructed with (a) FBP, (b) iDose4, and (c) IMR. SCT between (d) FBP and iDose4 as well as (e) FBP and IMR. FBP, filtered back projection; IMR, iterative model-based reconstruction; SCT, subtraction computed tography.

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  Fig. 4 Sample localization results of homogeneous (red) and structure transition (blue) regions displayed on (a) FBP image and (b) SCT domain. FBP, filtered back projection; SCT, subtraction computed tography.

Reference

References

1. Khong PL, Ringertz H, Donoghue V, Frush D, Rehani M, et al. ICRP. 2013; ICRP publication 121: radiological protection in paediatric diagnostic and interventional radiology. Ann ICRP. 42:1–63. DOI: 10.1016/j.icrp.2012.10.001. PMID: 23218172.

2. Söderberg M, Gunnarsson M. 2010; Automatic exposure control in computed tomography--an evaluation of systems from different manufacturers. Acta Radiol. 51:625–634. DOI: 10.3109/02841851003698206. PMID: 20429764.

3. Singh S, Kalra MK, Thrall JH, Mahesh M. 2011; Automatic exposure control in CT: applications and limitations. J Am Coll Radiol. 8:446–449. DOI: 10.1016/j.jacr.2011.03.001. PMID: 21636062.

4. Ha S, Jung S, Chang HJ, Park EA, Shim H. 2015; Effects of iterative reconstruction algorithm, automatic exposure control on image quality, and radiation dose: phantom experiments with coronary CT angiography protocols. Prog Med Phys. 26:28–35. DOI: 10.14316/pmp.2015.26.1.28.

5. Ju EB, Ahn SH, Choi SG, Lee R. 2016; Optimization of energy modulation filter for dual energy CBCT using Geant4 Monte-Carlo simulation. Prog Med Phys. 27:125–130. DOI: 10.14316/pmp.2016.27.3.125.

6. Hutton BF. 2011; Recent advances in iterative reconstruction for clinical SPECT/PET and CT. Acta Oncol. 50:851–858. DOI: 10.3109/0284186X.2011.580001. PMID: 21767184.

7. Geyer LL, Schoepf UJ, Meinel FG, Nance JW Jr, Bastarrika G, Leipsic JA, et al. 2015; State of the Art: iterative CT reconstruction techniques. Radiology. 276:339–357. DOI: 10.1148/radiol.2015132766. PMID: 26203706.

8. Greffier J, Larbi A, Frandon J, Daviau PA, Beregi JP, Pereira F. 2019; Influence of iterative reconstruction and dose levels on metallic artifact reduction: a phantom study within four CT systems. Diagn Interv Imaging. 100:269–277. DOI: 10.1016/j.diii.2018.12.007. PMID: 30709793.

9. Kuo Y, Lin YY, Lee RC, Lin CJ, Chiou YY, Guo WY. 2016; Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography. Medicine (Baltimore). 95:e4456. DOI: 10.1097/MD.0000000000004456. PMID: 27495078. PMCID: PMC4979832.

10. Bernard A, Comby PO, Lemogne B, Haioun K, Ricolfi F, Chevallier O, et al. 2021; Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality. Quant Imaging Med Surg. 11:392–401. DOI: 10.21037/qims-20-626. PMID: 33392038. PMCID: PMC7719916.

11. Lenfant M, Chevallier O, Comby PO, Secco G, Haioun K, Ricolfi F, et al. 2020; Deep learning versus iterative reconstruction for CT pulmonary angiography in the emergency setting: improved image quality and reduced radiation dose. Diagnostics (Basel). 10:558. DOI: 10.3390/diagnostics10080558. PMID: 32759874. PMCID: PMC7460033.

12. Kawashima H, Ichikawa K, Matsubara K, Nagata H, Takata T, Kobayashi S. 2019; Quality evaluation of image-based iterative reconstruction for CT: comparison with hybrid iterative reconstruction. J Appl Clin Med Phys. 20:199–205. DOI: 10.1002/acm2.12597. PMID: 31050148. PMCID: PMC6560231.

13. Peng C, Qiu B, Li M, Guan Y, Zhang C, Wu Z, et al. 2017; Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction. Biomed Eng Online. 16:1. DOI: 10.1186/s12938-016-0292-9. PMID: 28086973. PMCID: PMC5234134.

14. Chun M, Choi YH, Kim JH. 2015; Automated measurement of CT noise in patient images with a novel structure coherence feature. Phys Med Biol. 60:9107–9122. DOI: 10.1088/0031-9155/60/23/9107. PMID: 26561914.

15. Nakaura T, Awai K, Oda S, Funama Y, Harada K, Uemura S, et al. 2011; Low-kilovoltage, high-tube-current MDCT of liver in thin adults: pilot study evaluating radiation dose, image quality, and display settings. AJR Am J Roentgenol. 196:1332–1338. DOI: 10.2214/AJR.10.5698. PMID: 21606297.

16. Lin PJP, Beck TJ, Borras C, Cohen G, Jucius RA, Kriz RJ, et al. 1993. AAPM report No. 39. Specification and acceptance testing of computed tomography scanners. American Association of Physicists in Medicine;Alexandria: DOI: 10.37206/38.

17. Tian X, Samei E. 2016; Accurate assessment and prediction of noise in clinical CT images. Med Phys. 43:475. DOI: 10.1118/1.4938588. PMID: 26745940.

18. Christianson O, Winslow J, Frush DP, Samei E. 2015; Automated technique to measure noise in clinical CT examinations. AJR Am J Roentgenol. 205:W93–W99. DOI: 10.2214/AJR.14.13613. PMID: 26102424.

19. Onoue K, Yakami M, Nishio M, Sakamoto R, Aoyama G, Nakagomi K, et al. 2021; Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study. Sci Rep. 11:18422. DOI: 10.1038/s41598-021-97607-7. PMID: 34531429. PMCID: PMC8446090.

20. Li M, Zheng J, Zhang T, Guan Y, Xu P, Sun M. 2015; A prior-based metal artifact reduction algorithm for x-ray CT. J Xray Sci Technol. 23:229–241. DOI: 10.3233/XST-150483. PMID: 25882733.

21. Subhas N, Primak AN, Obuchowski NA, Gupta A, Polster JM, Krauss A, et al. 2014; Iterative metal artifact reduction: evaluation and optimization of technique. Skeletal Radiol. 43:1729–1735. DOI: 10.1007/s00256-014-1987-2. PMID: 25172218.

22. Große Hokamp N, Eck B, Siedek F, Pinto Dos Santos D, Holz JA, Maintz D, et al. 2020; Quantification of metal artifacts in computed tomography: methodological considerations. Quant Imaging Med Surg. 10:1033–1044. DOI: 10.21037/qims.2020.04.03. PMID: 32489927. PMCID: PMC7242301.

23. Prell D, Kyriakou Y, Struffert T, Dörfler A, Kalender WA. 2010; Metal artifact reduction for clipping and coiling in interventional C-arm CT. AJNR Am J Neuroradiol. 31:634–639. DOI: 10.3174/ajnr.A1883. PMID: 19942707. PMCID: PMC7964233.

24. Fukui R, Matsuura R, Kida K, Goto S. 2021; Effect of the number of projected images on the noise characteristics in tomosynthesis imaging. Prog Med Phys. 32:50–58. DOI: 10.14316/pmp.2021.32.2.50.

25. Prakash P, Kalra MK, Kambadakone AK, Pien H, Hsieh J, Blake MA, et al. 2010; Reducing abdominal CT radiation dose with adaptive statistical iterative reconstruction technique. Invest Radiol. 45:202–210. DOI: 10.1097/RLI.ob013e3181dzfeec. PMID: 20177389.

26. Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L. 2019; State of the Art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology. 293:491–503. DOI: 10.1148/radiol.2019191422. PMID: 31660806.

27. McCollough CH, Yu L, Kofler JM, Leng S, Zhang Y, Li Z, et al. 2015; Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology. 276:499–506. DOI: 10.1148/radiol.15142047. PMID: 25811326. PMCID: PMC4514568.

28. Goenka AH, Herts BR, Obuchowski NA, Primak AN, Dong F, Karim W, et al. 2014; Effect of reduced radiation exposure and iterative reconstruction on detection of low-contrast low-attenuation lesions in an anthropomorphic liver phantom: an 18-reader study. Radiology. 272:154–163. DOI: 10.1148/radiol.14131928. PMID: 24620913.

29. Stephenson I, Saunders A. 2007; Simulating film grain using the noise-power spectrum. EG UK Theory and practice of computer graphics. The Eurographics Association. 69–72.