Korean Journal of Radiology

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

DOI QR Code

The Impact of Iterative Reconstruction in Low-Dose Computed Tomography on the Evaluation of Diffuse Interstitial Lung Disease

  • Lim, Hyun-ju (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Chung, Myung Jin (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Shin, Kyung Eun (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Hwang, Hye Sun (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Lee, Kyung Soo (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine)
  • Received : 2015.08.29
  • Accepted : 2016.07.28
  • Published : 2016.11.01
⟨ Previous Next ⟩

Abstract

Objective: To evaluate the impact of iterative reconstruction (IR) on the assessment of diffuse interstitial lung disease (DILD) using CT. Materials and Methods: An American College of Radiology (ACR) phantom (module 4 to assess spatial resolution) was scanned with 10-100 effective mAs at 120 kVp. The images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), with blending ratios of 0%, 30%, 70% and 100%, and model-based iterative reconstruction (MBIR), and their spatial resolution was objectively assessed by the line pair structure method. The patient study was based on retrospective interpretation of prospectively acquired data, and it was approved by the institutional review board. Chest CT scans of 23 patients (mean age 64 years) were performed at 120 kVp using 1) standard dose protocol applying 142-275 mA with dose modulation (high-resolution computed tomography [HRCT]) and 2) low-dose protocol applying 20 mA (low dose CT, LDCT). HRCT images were reconstructed with FBP, and LDCT images were reconstructed using FBP, ASIR, and MBIR. Matching images were randomized and independently reviewed by chest radiologists. Subjective assessment of disease presence and radiological diagnosis was made on a 10-point scale. In addition, semi-quantitative results were compared for the extent of abnormalities estimated to the nearest 5% of parenchymal involvement. Results: In the phantom study, ASIR was comparable to FBP in terms of spatial resolution. However, for MBIR, the spatial resolution was greatly decreased under 10 mA. In the patient study, the detection of the presence of disease was not significantly different. The values for area under the curve for detection of DILD by HRCT, FBP, ASIR, and MBIR were as follows: 0.978, 0.979, 0.972, and 0.963. LDCT images reconstructed with FBP, ASIR, and MBIR tended to underestimate reticular or honeycombing opacities (-2.8%, -4.1%, and -5.3%, respectively) and overestimate ground glass opacities (+4.6%, +8.9%, and +8.5%, respectively) compared to the HRCT images. However, the reconstruction methods did not differ with respect to radiologic diagnosis. Conclusion: The diagnostic performance of LDCT with MBIR was similar to that of HRCT in typical DILD cases. However, caution should be exercised when comparing disease extent, especially in follow-up studies with IR.

Keywords

References

  1. Kubo T, Lin PJ, Stiller W, Takahashi M, Kauczor HU, Ohno Y, et al. Radiation dose reduction in chest CT: a review. AJR Am J Roentgenol 2008;190:335-343 https://doi.org/10.2214/AJR.07.2556
  2. Mayo JR. CT evaluation of diffuse infiltrative lung disease: dose considerations and optimal technique. J Thorac Imaging 2009;24:252-259 https://doi.org/10.1097/RTI.0b013e3181c227b2
  3. Tsapaki V, Aldrich JE, Sharma R, Staniszewska MA, Krisanachinda A, Rehani M, et al. Dose reduction in CT while maintaining diagnostic confidence: diagnostic reference levels at routine head, chest, and abdominal CT--IAEA-coordinated research project. Radiology 2006;240:828-834 https://doi.org/10.1148/radiol.2403050993
  4. Kalra MK, Maher MM, Toth TL, Hamberg LM, Blake MA, Shepard JA, et al. Strategies for CT radiation dose optimization. Radiology 2004;230:619-628 https://doi.org/10.1148/radiol.2303021726
  5. Heyer CM, Mohr PS, Lemburg SP, Peters SA, Nicolas V. Image quality and radiation exposure at pulmonary CT angiography with 100- or 120-kVp protocol: prospective randomized study. Radiology 2007;245:577-583 https://doi.org/10.1148/radiol.2452061919
  6. Diel J, Perlmutter S, Venkataramanan N, Mueller R, Lane MJ, Katz DS. Unenhanced helical CT using increased pitch for suspected renal colic: an effective technique for radiation dose reduction?. J Comput Assist Tomogr 2000;24:795-801 https://doi.org/10.1097/00004728-200009000-00023
  7. Kalra MK, Maher MM, Sahani DV, Blake MA, Hahn PF, Avinash GB, et al. Low-dose CT of the abdomen: evaluation of image improvement with use of noise reduction filters pilot study. Radiology 2003;228:251-256 https://doi.org/10.1148/radiol.2281020693
  8. Thibault JB, Sauer KD, Bouman CA, Hsieh J. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 2007;34:4526-4544 https://doi.org/10.1118/1.2789499
  9. Machida H, Tanaka I, Fukui R, Kita K, Shen Y, Ueno E, et al. Improved delineation of the anterior spinal artery with model-based iterative reconstruction in CT angiography: a clinical pilot study. AJR Am J Roentgenol 2013;200:442-446 https://doi.org/10.2214/AJR.11.7826
  10. Shuman WP, Green DE, Busey JM, Kolokythas O, Mitsumori LM, Koprowicz KM, et al. Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise. AJR Am J Roentgenol 2013;200:1071-1076 https://doi.org/10.2214/AJR.12.8986
  11. Goldman LW. Principles of CT: radiation dose and image quality. J Nucl Med Technol 2007;35:213-225; quiz 226-228 https://doi.org/10.2967/jnmt.106.037846
  12. Goldman LW. Principles of CT and CT technology. J Nucl Med Technol 2007;35:115-128; quiz 129-130 https://doi.org/10.2967/jnmt.107.042978
  13. Ichikawa Y, Kitagawa K, Nagasawa N, Murashima S, Sakuma H. CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction. BMC Med Imaging 2013;13:27 https://doi.org/10.1186/1471-2342-13-27
  14. Pontana F, Billard AS, Duhamel A, Schmidt B, Faivre JB, Hachulla E, et al. Effect of iterative reconstruction on the detection of systemic sclerosis-related interstitial lung disease: clinical experience in 55 patients. Radiology 2016;279:297-305 https://doi.org/10.1148/radiol.2015150849
  15. AAPM. The measurement, reporting, and management of radiation dose in CT. Alexandria, VA: AAPM, 2008
  16. Yoon HJ, Chung MJ, Hwang HS, Moon JW, Lee KS. Adaptive statistical iterative reconstruction-applied ultra-low-dose CT with radiography-comparable radiation dose: usefulness for lung nodule detection. Korean J Radiol 2015;16:1132-1141 https://doi.org/10.3348/kjr.2015.16.5.1132
  17. Staude A, Goebbels J. Determining the spatial resolution in computed tomography-comparison of MTF and line-pair structures. International symposium on digital industrial radiology and computed tomography-Tu.4.1;2011 June 20-22;Berlin, Germany
  18. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-310
  19. Dorfman DD, Berbaum KS, Metz CE. Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. Invest Radiol 1992;27:723-731 https://doi.org/10.1097/00004424-199209000-00015
  20. Obuchowski NA. New methodological tools for multiple-reader ROC studies. Radiology 2007;243:10-12 https://doi.org/10.1148/radiol.2432060387
  21. Richeldi L, du Bois RM, Raghu G, Azuma A, Brown KK, Costabel U, et al. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis. N Engl J Med 2014;370:2071-2082 https://doi.org/10.1056/NEJMoa1402584
  22. King TE Jr, Bradford WZ, Castro-Bernardini S, Fagan EA, Glaspole I, Glassberg MK, et al. A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis. N Engl J Med 2014;370:2083-2092 https://doi.org/10.1056/NEJMoa1402582
  23. Sverzellati N, Zompatori M, De Luca G, Chetta A, Bnà C, Ormitti F, et al. Evaluation of quantitative CT indexes in idiopathic interstitial pneumonitis using a low-dose technique. Eur J Radiol 2005;56:370-375 https://doi.org/10.1016/j.ejrad.2005.05.012
  24. Yabuuchi H, Matsuo Y, Tsukamoto H, Horiuchi T, Sunami S, Kamitani T, et al. Evaluation of the extent of ground-glass opacity on high-resolution CT in patients with interstitial pneumonia associated with systemic sclerosis: comparison between quantitative and qualitative analysis. Clin Radiol 2014;69:758-764 https://doi.org/10.1016/j.crad.2014.03.008
  25. Bendaoud S, Remy-Jardin M, Wallaert B, Deken V, Duhamel A, Faivre JB, et al. Sequential versus volumetric computed tomography in the follow-up of chronic bronchopulmonary diseases: comparison of diagnostic information and radiation dose in 63 adults. J Thorac Imaging 2011;26:190-195 https://doi.org/10.1097/RTI.0b013e3181f3a30e
  26. Xu J, Mahesh M, Tsui BM. Is iterative reconstruction ready for MDCT? J Am Coll Radiol 2009;6:274-276 https://doi.org/10.1016/j.jacr.2008.12.014
  27. Silva AC, Lawder HJ, Hara A, Kujak J, Pavlicek W. Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. AJR Am J Roentgenol 2010;194:191-199 https://doi.org/10.2214/AJR.09.2953
  28. Jiao de C, Li TF, Han XW, Wu G, Ma J, Fu MT, et al. Clinical applications of the C-arm cone-beam CT-based 3D needle guidance system in performing percutaneous transthoracic needle biopsy of pulmonary lesions. Diagn Interv Radiol 2014;20:470-474 https://doi.org/10.5152/dir.2014.13463
  29. Smith EA, Dillman JR, Goodsitt MM, Christodoulou EG, Keshavarzi N, Strouse PJ. Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT. Radiology 2014;270:526-534 https://doi.org/10.1148/radiol.13130362
  30. Ghetti C, Ortenzia O, Serreli G. CT iterative reconstruction in image space: a phantom study. Phys Med 2012;28:161-165 https://doi.org/10.1016/j.ejmp.2011.03.003
  31. Yamada Y, Jinzaki M, Tanami Y, Shiomi E, Sugiura H, Abe T, et al. Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study. Invest Radiol 2012;47:482-489 https://doi.org/10.1097/RLI.0b013e3182562a89
  32. Katsura M, Matsuda I, Akahane M, Sato J, Akai H, Yasaka K, et al. Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 2012;22:1613-1623 https://doi.org/10.1007/s00330-012-2452-z
  33. Yu Z, Thibault JB, Bouman CA, Sauer KD, Hsieh J. Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization. IEEE Trans Image Process 2011;20:161-175 https://doi.org/10.1109/TIP.2010.2058811
  34. McCollough CH, Chen GH, Kalender W, Leng S, Samei E, Taguchi K, et al. Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. Radiology 2012;264:567-580 https://doi.org/10.1148/radiol.12112265
  35. Wall BF, Hart D. Revised radiation doses for typical X-ray examinations. Report on a recent review of doses to patients from medical X-ray examinations in the UK by NRPB. National Radiological Protection Board. Br J Radiol 1997;70:437-439 https://doi.org/10.1259/bjr.70.833.9227222

Cited by

  1. Selection and Reporting of Statistical Methods to Assess Reliability of a Diagnostic Test: Conformity to Recommended Methods in a Peer-Reviewed Journal vol.18, pp.6, 2017, https://doi.org/10.3348/kjr.2017.18.6.888
  2. Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT vol.19, pp.1, 2016, https://doi.org/10.3348/kjr.2018.19.1.119
  3. Comparison of Filtered Back Projection, Hybrid Iterative Reconstruction, Model-Based Iterative Reconstruction, and Virtual Monoenergetic Reconstruction Images at Both Low- and Standard-Dose Settings i vol.19, pp.4, 2016, https://doi.org/10.3348/kjr.2018.19.4.809
  4. Effect of Hybrid Kernel and Iterative Reconstruction on Objective and Subjective Analysis of Lung Nodule Calcification in Low-Dose Chest CT vol.19, pp.5, 2018, https://doi.org/10.3348/kjr.2018.19.5.888
  5. Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study vol.19, pp.5, 2016, https://doi.org/10.3348/kjr.2018.19.5.957
  6. 인체등가형 흉부팬텀과 유리선량계를 이용한 고해상력 및 저선량 CT의 선량측정 vol.13, pp.7, 2019, https://doi.org/10.7742/jksr.2019.13.7.933
  7. Thoracic computed tomography in the progressive fibrotic phenotype vol.27, pp.5, 2016, https://doi.org/10.1097/mcp.0000000000000804