Korean J Radiol.  2015 Oct;16(5):1132-1141. 10.3348/kjr.2015.16.5.1132.

Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection

Affiliations
  • 1Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea. mj1.chung@samsung.com
  • 2Department of Radiology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul 04763, Korea.
  • 3Department of Radiology, Kangbuk Samsung Hospital, Seoul 03181, Korea.

Abstract


OBJECTIVE
To assess the performance of adaptive statistical iterative reconstruction (ASIR)-applied ultra-low-dose CT (ULDCT) in detecting small lung nodules.
MATERIALS AND METHODS
Thirty patients underwent both ULDCT and standard dose CT (SCT). After determining the reference standard nodules, five observers, blinded to the reference standard reading results, independently evaluated SCT and both subsets of ASIR- and filtered back projection (FBP)-driven ULDCT images. Data assessed by observers were compared statistically.
RESULTS
Converted effective doses in SCT and ULDCT were 2.81 +/- 0.92 and 0.17 +/- 0.02 mSv, respectively. A total of 114 lung nodules were detected on SCT as a standard reference. There was no statistically significant difference in sensitivity between ASIR-driven ULDCT and SCT for three out of the five observers (p = 0.678, 0.735, < 0.01, 0.038, and < 0.868 for observers 1, 2, 3, 4, and 5, respectively). The sensitivity of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT in three out of the five observers (p < 0.01 for three observers, and p = 0.064 and 0.146 for two observers). In jackknife alternative free-response receiver operating characteristic analysis, the mean values of figure-of-merit (FOM) for FBP, ASIR-driven ULDCT, and SCT were 0.682, 0.772, and 0.821, respectively, and there were no significant differences in FOM values between ASIR-driven ULDCT and SCT (p = 0.11), but the FOM value of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT and SCT (p = 0.01 and 0.00).
CONCLUSION
Adaptive statistical iterative reconstruction-driven ULDCT delivering a radiation dose of only 0.17 mSv offers acceptable sensitivity in nodule detection compared with SCT and has better performance than FBP-driven ULDCT.

Keyword

Multidetector computed tomography; Image reconstruction; Adaptive statistical iterative reconstruction; Lung; Radiation dosage

MeSH Terms

Adult
Aged
Female
Humans
Lung/pathology/*radiography
Lung Neoplasms/*radiography/secondary
Male
Middle Aged
ROC Curve
Radiation Dosage
Radiographic Image Enhancement
Radiographic Image Interpretation, Computer-Assisted
Rectal Neoplasms/pathology
Retrospective Studies
Tomography, X-Ray Computed

Figure

  • Fig. 1 Images of 43-year-old woman with metastatic lung nodule from rectal cancer show round pulmonary nodule measuring 5 mm (arrows) in left basal lung. ASIR-driven ultra-low-dose CT (A), FBP-driven ultra-low-dose CT (B), and standard dose CT (C) images. All observers detected nodule on ASIR- and FBP-driven ultra-low-dose CT and on standard dose CT. Using ultra-low-dose CT, ASIR provides more acceptable image noise, better diagnostic acceptability, and visual sharpness of pulmonary nodule than FBP. ASIR = adaptive statistical iterative reconstruction, FBP = filtered back projection

  • Fig. 2 Images of 45-year-old man with incidental pulmonary nodule show nodule with ground-glass opacity measuring 6 mm (arrows) in diameter in right upper lobe. ASIR-driven ultra-low-dose CT (A), FBP-driven ultra-low-dose CT (B), and standard dose CT (C) images. Two observers could not detect nodule on ultra-low-dose CT scans with FBP reconstruction; however, all observers detected nodule on ASIR-driven ultra-low-dose CT scan and standard dose CT scan. ASIR = adaptive statistical iterative reconstruction, FBP = filtered back projection

  • Fig. 3 Images of 29-year-old man with metastatic nodule from rectal cancer show nodule measuring 3 mm in diameter in right upper lobe. A, B. Nodule was identified as calcified nodule by all observers on ASIR-driven ultra-low-dose CT (A) and FBP-driven ultra-low-dose CT (B) scans (arrow). C. This nodule was correctly read as metastatic nodule on standard dose CT scan (arrow). ASIR = adaptive statistical iterative reconstruction, FBP = filtered back projection


Cited by  1 articles

The Impact of Iterative Reconstruction in Low-Dose Computed Tomography on the Evaluation of Diffuse Interstitial Lung Disease
Hyun-ju Lim, Myung Jin Chung, Kyung Eun Shin, Hye Sun Hwang, Kyung Soo Lee
Korean J Radiol. 2016;17(6):950-960.    doi: 10.3348/kjr.2016.17.6.950.


Reference

1. Greenlee RT, Murray T, Bolden S, Wingo PA. Cancer statistics, 2000. CA Cancer J Clin. 2000; 50:7–33.
2. Fry WA, Menck HR, Winchester DP. The National Cancer Data Base report on lung cancer. Cancer. 1996; 77:1947–1955.
3. Flehinger BJ, Kimmel M, Melamed MR. The effect of surgical treatment on survival from early lung cancer. Implications for screening. Chest. 1992; 101:1013–1018.
4. Melamed MR, Flehinger BJ, Zaman MB. Impact of early detection on the clinical course of lung cancer. Surg Clin North Am. 1987; 67:909–924.
5. Nesbitt JC, Putnam JB Jr, Walsh GL, Roth JA, Mountain CF. Survival in early-stage non-small cell lung cancer. Ann Thorac Surg. 1995; 60:466–472.
6. Shah R, Sabanathan S, Richardson J, Mearns AJ, Goulden C. Results of surgical treatment of stage I and II lung cancer. J Cardiovasc Surg (Torino). 1996; 37:169–172.
7. Evans SH, Davis R, Cooke J, Anderson W. A comparison of radiation doses to the breast in computed tomographic chest examinations for two scanning protocols. Clin Radiol. 1989; 40:45–46.
8. Lenzen H, Roos N, Diederich S, Meier N. [Radiation exposure in low dose computerized tomography of the thorax]. Radiologe. 1996; 36:483–488.
9. Parry RA, Glaze SA, Archer BR. The AAPM/RSNA physics tutorial for residents. Typical patient radiation doses in diagnostic radiology. Radiographics. 1999; 19:1289–1302.
10. Van Unnik JG, Broerse JJ, Geleijns J, Jansen JT, Zoetelief J, Zweers D. Survey of CT techniques and absorbed dose in various Dutch hospitals. Br J Radiol. 1997; 70:367–371.
11. 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.
12. Gartenschläger M, Schweden F, Gast K, Westermeier T, Kauczor H, von Zitzewitz H, et al. Pulmonary nodules: detection with low-dose vs conventional-dose spiral CT. Eur Radiol. 1998; 8:609–614.
13. Henschke CI, Yankelevitz DF, McCauley DI, Libby DM, Pasmantier MW, Smith JP. Guidelines for the use of spiral computed tomography in screening for lung cancer. Eur Respir J Suppl. 2003; 39:45s–51s.
14. Rusinek H, Naidich DP, McGuinness G, Leitman BS, McCauley DI, Krinsky GA, et al. Pulmonary nodule detection: low-dose versus conventional CT. Radiology. 1998; 209:243–249.
15. Diederich S, Lenzen H, Windmann R, Puskas Z, Yelbuz TM, Henneken S, et al. Pulmonary nodules: experimental and clinical studies at low-dose CT. Radiology. 1999; 213:289–298.
16. National Lung Screening Trial Research Team. Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011; 365:395–409.
17. Brenner DJ, Elliston CD. Estimated radiation risks potentially associated with full-body CT screening. Radiology. 2004; 232:735–738.
18. Brenner DJ, Hall EJ. Computed tomography--an increasing source of radiation exposure. N Engl J Med. 2007; 357:2277–2284.
19. Hara AK, Paden RG, Silva AC, Kujak JL, Lawder HJ, Pavlicek W. Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR Am J Roentgenol. 2009; 193:764–771.
20. 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.
21. Pontana F, Duhamel A, Pagniez J, Flohr T, Faivre JB, Hachulla AL, et al. Chest computed tomography using iterative reconstruction vs filtered back projection (Part 2): image quality of low-dose CT examinations in 80 patients. Eur Radiol. 2011; 21:636–643.
22. Pontana F, Pagniez J, Flohr T, Faivre JB, Duhamel A, Remy J, et al. Chest computed tomography using iterative reconstruction vs filtered back projection (Part 1): evaluation of image noise reduction in 32 patients. Eur Radiol. 2011; 21:627–635.
23. Prakash P, Kalra MK, Digumarthy SR, Hsieh J, Pien H, Singh S, et al. Radiation dose reduction with chest computed tomography using adaptive statistical iterative reconstruction technique: initial experience. J Comput Assist Tomogr. 2010; 34:40–45.
24. Prakash P, Kalra MK, Ackman JB, Digumarthy SR, Hsieh J, Do S, et al. Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique. Radiology. 2010; 256:261–269.
25. Yanagawa M, Honda O, Yoshida S, Kikuyama A, Inoue A, Sumikawa H, et al. Adaptive statistical iterative reconstruction technique for pulmonary CT: image quality of the cadaveric lung on standard- and reduced-dose CT. Acad Radiol. 2010; 17:1259–1266.
26. Leipsic J, Nguyen G, Brown J, Sin D, Mayo JR. A prospective evaluation of dose reduction and image quality in chest CT using adaptive statistical iterative reconstruction. AJR Am J Roentgenol. 2010; 195:1095–1099.
27. 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.
28. 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; 23:1632–1642.
29. Naidich DP, Marshall CH, Gribbin C, Arams RS, McCauley DI. Low-dose CT of the lungs: preliminary observations. Radiology. 1990; 175:729–731.
30. Chakraborty DP, Berbaum KS. Observer studies involving detection and localization: modeling, analysis, and validation. Med Phys. 2004; 31:2313–2330.
31. Chakraborty DP. Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. Acad Radiol. 2006; 13:1187–1193.
32. Vikgren J, Zachrisson S, Svalkvist A, Johnsson AA, Boijsen M, Flinck A, et al. Comparison of chest tomosynthesis and chest radiography for detection of pulmonary nodules: human observer study of clinical cases. Radiology. 2008; 249:1034–1041.
33. Hirose T, Nitta N, Shiraishi J, Nagatani Y, Takahashi M, Murata K. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy. Acad Radiol. 2008; 15:1505–1512.
34. Zachrisson S, Vikgren J, Svalkvist A, Johnsson AA, Boijsen M, Flinck A, et al. Effect of clinical experience of chest tomosynthesis on detection of pulmonary nodules. Acta Radiol. 2009; 50:884–891.
35. Yanagawa M, Honda O, Yoshida S, Ono Y, Inoue A, Daimon T, et al. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases. Acad Radiol. 2009; 16:924–933.
36. Larke FJ, Kruger RL, Cagnon CH, Flynn MJ, McNitt-Gray MM, Wu X, et al. Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial. AJR Am J Roentgenol. 2011; 197:1165–1169.
37. Boone JM, Strauss KJ, Cody DD, McCollough CH, McNitt-Gray MF, Toth TL, et al. Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. Report of AAPM Task Group 204. College Park: American Association of Physicists in Medicine;2011.
38. Katsura M, Matsuda I, Akahane M, Yasaka K, Hanaoka S, Akai H, et al. Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol. 2013; 48:206–212.
39. Neroladaki A, Botsikas D, Boudabbous S, Becker CD, Montet X. Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol. 2013; 23:360–366.
40. Wormanns D, Ludwig K, Beyer F, Heindel W, Diederich S. Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT. Eur Radiol. 2005; 15:14–22.
41. Seltzer SE, Judy PF, Adams DF, Jacobson FL, Stark P, Kikinis R, et al. Spiral CT of the chest: comparison of cine and film-based viewing. Radiology. 1995; 197:73–78.
42. Diederich S, Semik M, Lentschig MG, Winter F, Scheld HH, Roos N, et al. Helical CT of pulmonary nodules in patients with extrathoracic malignancy: CT-surgical correlation. AJR Am J Roentgenol. 1999; 172:353–360.
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