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Molecular Imaging and Biology

, Volume 20, Issue 1, pp 1–3 | Cite as

Correction for Partial Volume Effect Is a Must, Not a Luxury, to Fully Exploit the Potential of Quantitative PET Imaging in Clinical Oncology

  • Abass AlaviEmail author
  • Thomas J. Werner
  • Poul Flemming Høilund-Carlsen
  • Habib Zaidi
Commentary

Abstract

The partial volume effect (PVE) is considered as one of the major degrading factors impacting image quality and hampering the accuracy of quantitative PET imaging in clinical oncology. This effect is the consequence of the limited spatial resolution of whole-body PET scanners, which results in blurring of the generated images by the scanner’s response function. A number of strategies have been devised to deal with partial volume effect. However, the lack of consensus on the clinical relevance of partial volume correction and the most appropriate technique to be used in the context of clinical oncology limited their application in clinical setting. This issue is debated in this commentary.

Key words

PET Partial volume effect Partial volume correction Quantification Clinical oncology 

Notes

Compliance with Ethical Standards

Conflict of Interest

All authors declare that they have no conflict of interest.

References

  1. 1.
    Rousset O, Rahmim A, Alavi A, Zaidi H (2007) Partial volume correction strategies in PET. PET Clin 2(2):235–249.  https://doi.org/10.1016/j.cpet.2007.10.005 CrossRefPubMedGoogle Scholar
  2. 2.
    Soret M, Bacharach SL, Buvat I (2007) Partial-volume effect in PET tumor imaging. J Nucl Med 48(6):932–945.  https://doi.org/10.2967/jnumed.106.035774 CrossRefPubMedGoogle Scholar
  3. 3.
    Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF (2012) A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol 57(21):R119–RR59.  https://doi.org/10.1088/0031-9155/57/21/R119 CrossRefPubMedGoogle Scholar
  4. 4.
    Cysouw MCF, Kramer GM, Schoonmade LJ, Boellaard R, de Vet HCW, Hoekstra OS (2017) Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 44(12):2105–2116.  https://doi.org/10.1007/s00259-017-3775-4
  5. 5.
    Flather MD, Farkouh ME, Pogue JM, Yusuf S (1997) Strengths and limitations of meta-analysis: larger studies may be more reliable. Control Clin Trials 18(6):568–579; discussion 661-66.  https://doi.org/10.1016/S0197-2456(97)00024-X CrossRefPubMedGoogle Scholar
  6. 6.
    Greco T, Zangrillo A, Biondi-Zoccai G, Landoni G (2013) Meta-analysis: pitfalls and hints. Heart Lung Vessel 5:219–225PubMedPubMedCentralGoogle Scholar
  7. 7.
    Cysouw MC, Kramer GM, Hoekstra OS, Frings V, de Langen AJ, Smit EF et al (2016) Accuracy and precision of partial volume correction in oncological PET/CT studies. J Nucl Med 57(10):1642–1649.  https://doi.org/10.2967/jnumed.116.173831 CrossRefPubMedGoogle Scholar
  8. 8.
    Roehm E (2005) Limitations of meta-analyses. http://www.improvingmedicalstatistics.com/Limitations%20of%20Meta-analysis1.htm. 09/06/2017
  9. 9.
    Chawluk JB, Alavi A, Dann R, Hurtig HI, Bais S, Kushner MJ, Zimmerman RA, Reivich M (1987) Positron emission tomography in aging and dementia: effect of cerebral atrophy. J Nucl Med 28(4):431–437PubMedGoogle Scholar
  10. 10.
    Tanna NK, Kohn MI, Horwich DN, Jolles PR, Zimmerman RA, Alves WM, Alavi A (1991) Analysis of brain and cerebrospinal fluid volumes with MR imaging: impact on PET data correction for atrophy. Part II. aging and Alzheimer dementia. Radiology 178(1):123–130.  https://doi.org/10.1148/radiology.178.1.1984290 CrossRefPubMedGoogle Scholar
  11. 11.
    Hoilund-Carlsen PF, Lauritzen SL, Marving J, Rasmussen S, Hesse B, Folke K, Godtfredsen J, Chraemmer-Jorgensen B, Gadsboll N, Dige-Petersen H (1988) The reliability of measuring left ventricular ejection fraction by radionuclide cardiography: evaluation by the method of variance components. Br Heart J 59(6):653–662.  https://doi.org/10.1136/hrt.59.6.653 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Bar-Shalom R, Valdivia AY, Blaufox MD (2000) PET imaging in oncology. Semin Nucl Med 30(3):150–185.  https://doi.org/10.1053/snuc.2000.7439 CrossRefPubMedGoogle Scholar
  13. 13.
    Duhaylongsod FG, Lowe VJ, Patz EF Jr, Vaughn AL, Coleman RE, Wolfe WG (1995) Detection of primary and recurrent lung cancer by means of F-18 fluorodeoxyglucose positron emission tomography (FDG PET). J Thorac Cardiovasc Surg 110(1):130–139; discussion 39–40.  https://doi.org/10.1016/S0022-5223(05)80018-2 CrossRefPubMedGoogle Scholar
  14. 14.
    Patz EF Jr, Lowe VJ, Hoffman JM, Paine SS, Burrowes P, Coleman RE, Goodman PC (1993) Focal pulmonary abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning. Radiology 188(2):487–490.  https://doi.org/10.1148/radiology.188.2.8327702 CrossRefPubMedGoogle Scholar
  15. 15.
    Prauer HW, Weber WA, Romer W, Treumann T, Ziegler SI, Schwaiger M (1998) Controlled prospective study of positron emission tomography using the glucose analogue [18f] fluorodeoxyglucose in the evaluation of pulmonary nodules. Br J Surg 85(11):1506–1511.  https://doi.org/10.1046/j.1365-2168.1998.00915.x CrossRefPubMedGoogle Scholar
  16. 16.
    Hickeson M, Yun M, Matthies A, Zhuang H, Adam LE, Lacorte L, Alavi A (2002) Use of a corrected standardized uptake value based on the lesion size on CT permits accurate characterization of lung nodules on FDG-PET. Eur J Nucl Med Mol Imaging 29(12):1639–1647.  https://doi.org/10.1007/s00259-002-0924-0 CrossRefPubMedGoogle Scholar
  17. 17.
    Kwee TC, Cheng G, Lam MG, Basu S, Alavi A (2013) SUVmax of 2.5 should not be embraced as a magic threshold for separating benign from malignant lesions. Eur J Nucl Med Mol Imaging 40:1475–1477CrossRefPubMedGoogle Scholar
  18. 18.
    Salavati A, Borofsky S, Boon-Keng TK, Houshmand S, Khiewvan B, Saboury B, Codreanu I, Torigian DA, Zaidi H, Alavi A (2015) Application of partial volume effect correction and 4D PET in the quantification of FDG avid lung lesions. Mol Imaging Biol 17(1):140–148.  https://doi.org/10.1007/s11307-014-0776-6 CrossRefPubMedGoogle Scholar
  19. 19.
    Teo BK, Saboury B, Munbodh R, Scheuermann J, Torigian DA, Zaidi H, Alavi A (2012) The effect of breathing irregularities on quantitative accuracy of respiratory gated PETCT. Med Phys 39(12):7390–7397.  https://doi.org/10.1118/1.4766876 CrossRefPubMedGoogle Scholar
  20. 20.
    Alavi A, Newberg AB, Souder E, Berlin JA (1993) Quantitative analysis of PET and MRI data in normal aging and Alzheimer’s disease: atrophy weighted total brain metabolism and absolute whole brain metabolism as reliable discriminators. J Nucl Med 34(10):1681–1687PubMedGoogle Scholar
  21. 21.
    Berkowitz A, Basu S, Srinivas S, Sankaran S, Schuster S, Alavi A (2008) Determination of whole-body metabolic burden as a quantitative measure of disease activity in lymphoma: a novel approach with fluorodeoxyglucose-PET. Nucl Med Commun 29(6):521–526.  https://doi.org/10.1097/MNM.0b013e3282f813a4 CrossRefPubMedGoogle Scholar
  22. 22.
    Basu S, Zaidi H, Salavati A, Hess S, Carlsen PF, Alavi A (2014) FDG PET/CT methodology for evaluation of treatment response in lymphoma: from “graded visual analysis” and “semiquantitative SUVmax” to global disease burden assessment. Eur J Nucl Med Mol Imaging 41(11):2158–2160.  https://doi.org/10.1007/s00259-014-2826-3 CrossRefPubMedGoogle Scholar
  23. 23.
    Houshmand S, Salavati A, Hess S, Werner TJ, Alavi A, Zaidi H (2015) An update on novel quantitative techniques in the context of evolving whole-body PET imaging. PET Clin 10(1):45–58.  https://doi.org/10.1016/j.cpet.2014.09.004 CrossRefPubMedGoogle Scholar
  24. 24.
    Torigian DA, Lopez RF, Alapati S, Bodapati G, Hofheinz F, van den Hoff J, Saboury B, Alavi A (2011) Feasibility and performance of novel software to quantify metabolically active volumes and 3D partial volume corrected SUV and metabolic volumetric products of spinal bone marrow metastases on 18F-FDG-PET/CT. Hell J Nucl Med 14(1):8–14PubMedGoogle Scholar
  25. 25.
    Joshi NV, Vesey AT, Williams MC, Shah AS, Calvert PA, Craighead FH et al (2014) 18F-fluoride positron emission tomography for identification of ruptured and high-risk coronary atherosclerotic plaques: a prospective clinical trial. Lancet 383(9918):705–713.  https://doi.org/10.1016/S0140-6736(13)61754-7 CrossRefPubMedGoogle Scholar
  26. 26.
    Dweck MR, Aikawa E, Newby DE, Tarkin JM, Rudd JH, Narula J et al (2016) Noninvasive molecular imaging of disease activity in atherosclerosis. Circ Res 119(2):330–340.  https://doi.org/10.1161/CIRCRESAHA.116.307971 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© World Molecular Imaging Society 2017

Authors and Affiliations

  • Abass Alavi
    • 1
    Email author
  • Thomas J. Werner
    • 1
  • Poul Flemming Høilund-Carlsen
    • 2
  • Habib Zaidi
    • 2
    • 3
    • 4
    • 5
  1. 1.Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of Nuclear MedicineUniversity of Southern DenmarkOdenseDenmark
  3. 3.Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
  4. 4.Geneva Neuroscience CentreUniversity of GenevaGenevaSwitzerland
  5. 5.Department of Nuclear Medicine and Molecular ImagingUniversity of GroningenGroningenNetherlands

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