Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method

  • Pamela Sung (Department of Radiology, Seoul National University Hospital) ;
  • Jeong Min Lee (Department of Radiology, Seoul National University Hospital) ;
  • Ijin Joo (Department of Radiology, Seoul National University Hospital) ;
  • Sanghyup Lee (Department of Radiology, Seoul National University Hospital) ;
  • Tae-Hyung Kim (Department of Radiology, Seoul National University College of Medicine) ;
  • Balaji Ganeshan (Clinical Imaging Sciences Centre, Brighton and Sussex Medical School)
  • Received : 2018.06.10
  • Accepted : 2018.10.05
  • Published : 2019.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: To evaluate whether computed tomography (CT) reconstruction algorithms affect the CT texture features of the liver parenchyma. Materials and Methods: This retrospective study comprised 58 patients (normal liver, n = 34; chronic liver disease [CLD], n = 24) who underwent liver CT scans using a single CT scanner. All CT images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR) (iDOSE4), and model-based IR (IMR). On arterial phase (AP) and portal venous phase (PVP) CT imaging, quantitative texture analysis of the liver parenchyma using a single-slice region of interest was performed at the level of the hepatic hilum using a filtration-histogram statistic-based method with different filter values. Texture features were compared among the three reconstruction methods and between normal livers and those from CLD patients. Additionally, we evaluated the inter- and intra-observer reliability of the CT texture analysis by calculating intraclass correlation coefficients (ICCs). Results: IR techniques affect various CT texture features of the liver parenchyma. In particular, model-based IR frequently showed significant differences compared to FBP or hybrid IR on both AP and PVP CT imaging. Significant variation in entropy was observed between the three reconstruction algorithms on PVP imaging (p < 0.05). Comparison between normal livers and those from CLD patients revealed that AP images depend more strongly on the reconstruction method used than PVP images. For both inter- and intra-observer reliability, ICCs were acceptable (> 0.75) for CT imaging without filtration. Conclusion: CT texture features of the liver parenchyma evaluated using the filtration-histogram method were significantly affected by the CT reconstruction algorithm used.

Keywords

References

  1. Grootjans W, Tixier F, van der Vos CS, Vriens D, Le Rest CC, Bussink J, et al. The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG PET imaging of lung cancer. J Nucl Med 2016;57:1692-1698  https://doi.org/10.2967/jnumed.116.173112
  2. Yun BL, Cho N, Li M, Jang MH, Park SY, Kang HC, et al. Intratumoral heterogeneity of breast cancer xenograft models: texture analysis of diffusion-weighted MR imaging. Korean J Radiol 2014;15:591-604  https://doi.org/10.3348/kjr.2014.15.5.591
  3. Bashir U, Siddique MM, Mclean E, Goh V, Cook GJ. Imaging heterogeneity in lung cancer: techniques, applications, and challenges. AJR Am J Roentgenol 2016;207:534-543  https://doi.org/10.2214/AJR.15.15864
  4. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017;37:1483-1503  https://doi.org/10.1148/rg.2017170056
  5. Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging 2013;13:140-149  https://doi.org/10.1102/1470-7330.2013.0015
  6. Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 2013;13:400-406  https://doi.org/10.1102/1470-7330.2013.9045
  7. Daginawala N, Li B, Buch K, Yu H, Tischler B, Qureshi MM, et al. Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 2016;85:511-517  https://doi.org/10.1016/j.ejrad.2015.12.009
  8. Lubner MG, Malecki K, Kloke J, Ganeshan B, Pickhardt PJ. Texture analysis of the liver at MDCT for assessing hepatic fibrosis. Abdom Radiol (NY) 2017;42:2069-2078  https://doi.org/10.1007/s00261-017-1096-5
  9. Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010;10:137-143  https://doi.org/10.1102/1470-7330.2010.0021
  10. Chae HD, Park CM, Park SJ, Lee SM, Kim KG, Goo JM. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 2014;273:285-293  https://doi.org/10.1148/radiol.14132187
  11. Miles KA, Ganeshan B, Griffiths MR, Young RC, Chatwin CR. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 2009;250:444-452  https://doi.org/10.1148/radiol.2502071879
  12. Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E. Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: dependency on radiation dose and reconstruction algorithm. Radiology 2016;279:185-194  https://doi.org/10.1148/radiol.2015150892
  13. Yasaka K, Akai H, Mackin D, Court L, Moros E, Ohtomo K, et al. Precision of quantitative computed tomography texture analysis using image filtering: a phantom study for scanner variability. Medicine (Baltimore) 2017;96:e6993 
  14. Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol 2015;50:757-765  https://doi.org/10.1097/RLI.0000000000000180
  15. Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 2016;6:23428 
  16. Berrington de Gonzalez A, Mahesh M, Kim KP, Bhargavan M, Lewis R, Mettler F, et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med 2009;169:2071-2077  https://doi.org/10.1001/archinternmed.2009.440
  17. Liu L. Model-based iterative reconstruction: a promising algorithm for today's computed tomography imaging. J Med Imaging Radiat Sci 2014;45:131-136  https://doi.org/10.1016/j.jmir.2014.02.002
  18. Yu MH, Lee JM, Yoon JH, Baek JH, Han JK, Choi BI, et al. Low tube voltage intermediate tube current liver MDCT: sinogram-affirmed iterative reconstruction algorithm for detection of hypervascular hepatocellular carcinoma. AJR Am J Roentgenol 2013;201:23-32  https://doi.org/10.2214/AJR.12.10000
  19. Yoon JH, Lee JM, Yu MH, Baek JH, Jeon JH, Hur BY, et al. Comparison of iterative model-based reconstruction versus conventional filtered back projection and hybrid iterative reconstruction techniques: lesion conspicuity and influence of body size in anthropomorphic liver phantoms. J Comput Assist Tomogr 2014;38:859-868  https://doi.org/10.1097/RCT.0000000000000145
  20. Chang W, Lee JM, Lee K, Yoon JH, Yu MH, Han JK, et al. Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 2013;48:598-606  https://doi.org/10.1097/RLI.0b013e3182899104
  21. Kim H, Park CM, Lee M, Park SJ, Song YS, Lee JH, et al. Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS One 2016;11:e0164924 
  22. Barrett HH, Myers KJ, Hoeschen C, Kupinski MA, Little MP. Task-based measures of image quality and their relation to radiation dose and patient risk. Phys Med Biol 2015;60:R1-R75  https://doi.org/10.1088/0031-9155/60/2/R1
  23. Shin JM, Kim TH, Haam S, Han K, Byun MK, Chang YS, et al. The repeatability of computed tomography lung volume measurements: comparisons in healthy subjects, patients with obstructive lung disease, and patients with restrictive lung disease. PLoS One 2017;12:e0182849 
  24. 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
  25. Willemink MJ, de Jong PA, Leiner T, de Heer LM, Nievelstein RA, Budde RP, et al. Iterative reconstruction techniques for computed tomography Part 1: technical principles. Eur Radiol 2013;23:1623-1631  https://doi.org/10.1007/s00330-012-2765-y
  26. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 2013;266:177-184  https://doi.org/10.1148/radiol.12120254
  27. Elpek GO. Angiogenesis and liver fibrosis. World J Hepatol 2015;7:377-391  https://doi.org/10.4254/wjh.v7.i3.377
  28. Lautt WW. Mechanism and role of intrinsic regulation of hepatic arterial blood flow: hepatic arterial buffer response. Am J Physiol 1985;249(5 Pt 1):G549-G556  https://doi.org/10.1152/ajpgi.1985.249.5.G549
  29. Yoon JH, Lee JM, Klotz E, Jeon JH, Lee KB, Han JK, et al. Estimation of hepatic extracellular volume fraction using multiphasic liver computed tomography for hepatic fibrosis grading. Invest Radiol 2015;50:290-296  https://doi.org/10.1097/RLI.0000000000000123
  30. 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
  31. Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep 2015;5:11075 
  32. Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E. Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 2018;8:10545