Advertisement

Abdominal Imaging

, Volume 40, Issue 7, pp 2850–2860 | Cite as

Effect of radiologists’ experience with an adaptive statistical iterative reconstruction algorithm on detection of hypervascular liver lesions and perception of image quality

  • Daniele MarinEmail author
  • Achille Mileto
  • Rajan T. Gupta
  • Lisa M. Ho
  • Brian C. Allen
  • Kingshuk Roy Choudhury
  • Rendon C. Nelson
Article

Abstract

Purpose

To prospectively evaluate whether clinical experience with an adaptive statistical iterative reconstruction algorithm (ASiR) has an effect on radiologists’ diagnostic performance and confidence for the diagnosis of hypervascular liver tumors, as well as on their subjective perception of image quality.

Materials and methods

Forty patients, having 65 hypervascular liver tumors, underwent contrast-enhanced MDCT during the hepatic arterial phase. Image datasets were reconstructed with filtered backprojection algorithm and ASiR (20%, 40%, 60%, and 80% blending). During two reading sessions, performed before and after a three-year period of clinical experience with ASiR, three readers assessed datasets for lesion detection, likelihood of malignancy, and image quality.

Results

For all reconstruction algorithms, there was no significant change in readers’ diagnostic accuracy and sensitivity for the detection of liver lesions, between the two reading sessions. However, a 60% ASiR dataset yielded a significant improvement in specificity, lesion conspicuity, and confidence for lesion likelihood of malignancy during the second reading session (P < 0.0001). The 60% ASiR dataset resulted in significant improvement in readers’ perception of image quality during the second reading session (P < 0.0001).

Conclusions

Clinical experience using an ASiR algorithm may improve radiologists’ diagnostic performance for the diagnosis of hypervascular liver tumors, as well as their perception of image quality.

Keywords

Adaptive statistical iterative reconstruction Filtered backprojection Hypervascular liver tumors Diagnostic accuracy Image quality 

References

  1. 1.
    Hara AK, Paden RG, Silva AC, et al. (2009) Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR Am J Roentgenol 193:764–771CrossRefPubMedGoogle Scholar
  2. 2.
    Thibault JB, Sauer KD, Bouman CA, Hsieh J (2007) A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 34:4526–4544CrossRefPubMedGoogle Scholar
  3. 3.
    Neroladaki A, Botsikas D, Boudabbous S, Becker CD, Montet X (2013) Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol 23:360–366CrossRefPubMedGoogle Scholar
  4. 4.
    Flicek KT, Hara AK, Silva AC, et al. (2010) Reducing the radiation dose for CT colonography using adaptive statistical iterative reconstruction: A pilot study. AJR Am J Roentgenol 195:126–131CrossRefPubMedGoogle Scholar
  5. 5.
    Fletcher JG, Grant KL, Fidler JL, et al. (2012) Validation of dual-source single-tube reconstruction as a method to obtain half-dose images to evaluate radiation dose and noise reduction: phantom and human assessment using CT colonography and sinogram-affirmed iterative reconstruction (SAFIRE). J Comput Assist Tomogr 36:560–569CrossRefPubMedGoogle Scholar
  6. 6.
    Brady SL, Yee BS, Kaufman RA (2012) Characterization of adaptive statistical iterative reconstruction algorithm for dose reduction in CT: a pediatric oncology perspective. Med Phys 39:5520–5531CrossRefPubMedGoogle Scholar
  7. 7.
    Kambadakone AR, Chaudhary NA, Desai GS, et al. (2011) Low-dose MDCT and CT enterography of patients with Crohn disease: feasibility of adaptive statistical iterative reconstruction. AJR Am J Roentgenol 196:W743–W752CrossRefPubMedGoogle Scholar
  8. 8.
    Lee SJ, Park SH, Kim AY, et al. (2011) A prospective comparison of standard-dose CT enterography and 50% reduced-dose CT enterography with and without noise reduction for evaluating Crohn disease. AJR Am J Roentgenol 197:50–57CrossRefPubMedGoogle Scholar
  9. 9.
    Singh S, Kalra MK, Hsieh J, et al. (2010) Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology 257:373–383CrossRefPubMedGoogle Scholar
  10. 10.
    Marin D, Choudhury KR, Gupta RT, et al. (2013) Clinical impact of an adaptive statistical iterative reconstruction algorithm for detection of hypervascular liver tumors using a low tube voltage, high tube current MDCT technique. Eur Radiol 23:3325–3335CrossRefPubMedGoogle Scholar
  11. 11.
    Ghetti C, Palleri F, Serreli G, Ortenzia O, Ruffini L (2013) Physical characterization of a new CT iterative reconstruction method operating in sinogram space. J Appl Clin Med Phys 14:43–47Google Scholar
  12. 12.
    Marin D, Nelson RC, Schindera ST, et al. (2010) Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm–initial clinical experience. Radiology 254:145–153CrossRefPubMedGoogle Scholar
  13. 13.
    Richard S, Husarik DB, Yadava G, Murphy SN, Samei E (2012) Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms. Med Phys 39:4115–4122CrossRefPubMedGoogle Scholar
  14. 14.
    Bossuyt PM, Reitsma JB, Bruns DE, et al. (2003) Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Clin Chem Lab Med 41:68–73PubMedGoogle Scholar
  15. 15.
    Sultana S, Awai K, Nakayama Y, et al. (2007) Hypervascular hepatocellular carcinomas: bolus tracking with a 40-Detector CT scanner to time arterial phase imaging. Radiology 243:140–147CrossRefPubMedGoogle Scholar
  16. 16.
    Miéville FA, Ayestaran P, Argaud C, et al. (2010) Potential benefit of the CT adaptive statistical iterative reconstruction method for pediatric cardiac diagnosis. Procedings of SPIE, 76222D1-D11Google Scholar
  17. 17.
    Sica GT (2006) Bias in research studies. Radiology 238:780–789CrossRefPubMedGoogle Scholar
  18. 18.
    Couinaud C (1957) Le foie: etudes anatomiques et chirurgicales. Paris: Masson et CieGoogle Scholar
  19. 19.
    Bismuth H (1982) Surgical anatomy and anatomical surgery of the liver. World J Surg 6:3–9CrossRefPubMedGoogle Scholar
  20. 20.
    Nino-Murcia M, Olcott EW, Jeffrey RB, et al. (2000) Focal liver lesions: pattern-based classification Scheme for enhancement at arterial phase CT. Radiology 215:746–751CrossRefPubMedGoogle Scholar
  21. 21.
    Medical imaging: the assessment of image quality (report no. 54) (1996) International Commission on Radiation Units and Measurements. Bethesda, MD: International Commission on Radiation Units and MeasurementsGoogle Scholar
  22. 22.
    Jessen K, Panzer W, Shrimpton P, et al. (2000) EUR 16262: European guidelines on quality criteria for computed tomography. Luxembourg: Office for Official Publications of the European CommunitiesGoogle Scholar
  23. 23.
    Obuchowski NA, Rockette HE (1995) Hypothesis testing of diagnostic accuracy for multiple readers and multiple tests: an ANOVA approach with dependent observations. Commun Stat Simul 24:285–308CrossRefGoogle Scholar
  24. 24.
    Brancatelli G, Baron RL, Peterson MS, Marsh W (2003) Helical CT screening for hepatocellular carcinoma in patients with cirrhosis: frequency and causes of false-positive interpretation. AJR Am J Roentgenol 180:1007–1014CrossRefPubMedGoogle Scholar
  25. 25.
    Hayashi PH, Trotter JF, Forman L, et al. (2004) Impact of pretransplant diagnosis of hepatocellular carcinoma on cadveric liver allocation in the era of MELD. Liver Transpl 10:42–48CrossRefPubMedGoogle Scholar
  26. 26.
    Freeman RB, Mithoefer A, Ruthazer R, et al. (2006) Optimizing staging for hepatocellular carcinoma before liver transplantation: a retrospective analysis of the UNOS/OPTN database. Liver Transpl 12:1504–1511CrossRefPubMedGoogle Scholar
  27. 27.
    Barnes E (2010) Better images are next frontier for CT iterative recon. AuntMinnie.com. Retrieved September 16, 2010 from http://www.auntminnie.com/print/print.asp?sec=sup&sub=cto&pag=dis&ItemId=91818&printpage=true
  28. 28.
    Barnes E (2010) MBIR aims to outshine ASIR for sharpness, CT dose reduction. AuntMinnie.com. Retrieved September 17, 2010 from: http://www.auntminnie.com/print/print.asp?sec=sup&sub=cto&pag=dis&ItemId=90625&printpage=true
  29. 29.
    Mieville FA, Gudinchet F, Brunelle F, Bochud FO, Verdun FR (2013) Iterative reconstruction methods in two different MDCT scanners: physical metrics and 4-alternative forced-choice detectability experiments–a phantom approach. Med Phys 29:99–110CrossRefGoogle Scholar
  30. 30.
    Peterson MS, Baron RL, Marsh JW, et al. (2000) Pretransplantation surveillance for possible hepatocellular carcinoma in patients with cirrhosis: epidemiology and CT-based tumor detection rate in 430 cases with surgical pathologic correlation. Radiology 217:743–749CrossRefPubMedGoogle Scholar
  31. 31.
    Lim JH, Kim CK, Lee WJ, et al. (2000) Detection of hepatocellular carcinomas and dysplastic nodules in cirrhotic livers: accuracy of helical CT in transplant patients. AJR Am J Roentgenol 175:693–698CrossRefPubMedGoogle Scholar
  32. 32.
    Krinsky GA, Lee VS, Theise ND, et al. (2001) Hepatocellular carcinoma and dysplastic nodules in patients with cirrhosis: prospective diagnosis with MR imaging and explantation correlation. Radiology 219:445–454CrossRefPubMedGoogle Scholar
  33. 33.
    Krinsky GA, Lee VS, Theise ND, et al. (2002) Transplantation for hepatocellular carcinoma and cirrhosis: sensitivity of magnetic resonance imaging. Liver Transpl 8:1156–1164CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Daniele Marin
    • 1
    Email author
  • Achille Mileto
    • 1
  • Rajan T. Gupta
    • 1
  • Lisa M. Ho
    • 1
  • Brian C. Allen
    • 2
  • Kingshuk Roy Choudhury
    • 3
  • Rendon C. Nelson
    • 1
  1. 1.Department of RadiologyDuke University Medical CenterDurhamUSA
  2. 2.Department of RadiologyWake Forest Baptist Medical CenterWinston-SalemUSA
  3. 3.Carl E. Ravin Advanced Imaging Laboratories (RAI Labs)Duke University Medical CenterDurhamUSA

Personalised recommendations