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    Original Research

    Validation of Software Gating: A Practical Technology for Respiratory Motion Correction in PET

    Published Online:Mar 30 2016https://doi.org/10.1148/radiol.2016152105

    Abstract

    Compared with vendor-supplied respiratory-gating hardware methods, software gating performed favorably, both qualitatively and quantitatively.

    Purpose

    To assess the performance of hardware- and software-gating technologies in terms of qualitative and quantitative characteristics of respiratory motion in positron emission tomography (PET) imaging.

    Materials and Methods

    Between 2010 and 2013, 219 fluorine 18 fluorodeoxyglucose PET examinations were performed in 116 patients for assessment of pulmonary nodules. All patients provided informed consent in this institutional review board–approved study. Acquisitions were reconstructed as respiratory-gated images by using hardware-derived respiratory triggers and software-derived signal (via an automated postprocessing method). Asymmetry was evaluated in the joint distribution of reader preference, and linear mixed models were used to evaluate differences in outcomes according to gating type.

    Results

    In blind reviews of reconstructed gated images, software was selected as superior 16.9% of the time (111 of 657 image sets; 95% confidence interval [CI]: 14.0%, 19.8%), and hardware was selected as superior 6.2% of the time (41 of 657 image sets; 95% CI: 4.4%, 8.1%). Of the image sets, 76.9% (505 of 657; 95% CI: 73.6%, 80.1%) were judged as having indistinguishable motion quality. Quantitative analysis demonstrated that the two gating strategies exhibited similar performance, and the performance of both was significantly different from that of nongated images. The mean increase ± standard deviation in lesion maximum standardized uptake value was 42.2% ± 38.9 between nongated and software-gated images, and lesion full width at half maximum values decreased by 9.9% ± 9.6.

    Conclusion

    Compared with vendor-supplied respiratory-gating hardware methods, software gating performed favorably, both qualitatively and quantitatively. Fully automated gating is a feasible approach to motion correction of PET images.

    © RSNA, 2016

    Online supplemental material is available for this article.

    References

    • 1. Polycarpou I, Tsoumpas C, King AP, Marsden PK. Impact of respiratory motion correction and spatial resolution on lesion detection in PET: a simulation study based on real MR dynamic data. Phys Med Biol 2014;59(3):697–713.
      MedlineGoogle Scholar
    • 2. Grootjans W, de Geus-Oei LF, Troost EG, Visser EP, Oyen WJ, Bussink J. PET in the management of locally advanced and metastatic NSCLC. Nat Rev Clin Oncol 2015;12(7):395–407.
      MedlineGoogle Scholar
    • 3. Fayad H, Schmidt H, Wuerslin C, Visvikis D. Reconstruction-incorporated respiratory motion correction in clinical simultaneous PET/MR imaging for oncology applications. J Nucl Med 2015;56(6):884–889.
      MedlineGoogle Scholar
    • 4. Wells RG, Ruddy TD, DeKemp RA, DaSilva JN, Beanlands RS. Single-phase CT aligned to gated PET for respiratory motion correction in cardiac PET/CT. J Nucl Med 2010;51(8):1182–1190.
      MedlineGoogle Scholar
    • 5. Guerra L, De Ponti E, Elisei F, et al. Respiratory gated PET/CT in a European multicentre retrospective study: added diagnostic value in detection and characterization of lung lesions. Eur J Nucl Med Mol Imaging 2012;39(9):1381–1390.
      MedlineGoogle Scholar
    • 6. Kesner AL, Kuntner C. A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Med Phys 2010;37(10):5550–5559.
      MedlineGoogle Scholar
    • 7. Büther F, Ernst I, Dawood M, et al. Detection of respiratory tumour motion using intrinsic list mode-driven gating in positron emission tomography. Eur J Nucl Med Mol Imaging 2010;37(12):2315–2327.
      MedlineGoogle Scholar
    • 8. Schleyer PJ, Thielemans K, Marsden PK. Extracting a respiratory signal from raw dynamic PET data that contain tracer kinetics. Presented at IEEE MIC 2013, Seoul, South Korea, 2013.
      Google Scholar
    • 9. Thielemans K, Rathore S, Engbrant F, Razifar P. Device-less gating for PET/CT using PCA. Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE 2011; 3904–3910.
      Google Scholar
    • 10. He J, O’Keefe GJ, Gong SJ, et al. A novel method for respiratory motion gated with geometric sensitivity of the scanner in 3D PET. IEEE Trans Nucl Sci 2008;55(5):2557–2565.
      Google Scholar
    • 11. Kesner AL, Abourbeh G, Mishani E, Chisin R, Tshori S, Freedman N. Gating, enhanced gating, and beyond: information utilization strategies for motion management, applied to preclinical PET. EJNMMI Res 2013;3(1):29.
      MedlineGoogle Scholar
    • 12. Schleyer PJ, O’Doherty MJ, Marsden PK. Extension of a data-driven gating technique to 3D, whole body PET studies. Phys Med Biol 2011;56(13):3953–3965.
      MedlineGoogle Scholar
    • 13. Manber R, Thielemans K, Hutton BF, et al. Practical PET respiratory motion correction in clinical PET/MR. J Nucl Med 2015;56(6):890–896.
      MedlineGoogle Scholar
    • 14. Klingenberg B, Agresti A. Multivariate extensions of McNemar’s test. Biometrics. 2006;62(3):921–928.
      MedlineGoogle Scholar
    • 15. Klingenberg B. SAS code for the Wald and score-type statistics W and W0 for testing simultaneous marginal homogeneity (SMH) for the adverse event data discussed in Klingenberg and Agresti. http://lanfiles.williams.edu/∼bklingen/sascode.html. Published 2005. Accessed December 1, 2015.
      Google Scholar
    • 16. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982;38(4):963–974.
      MedlineGoogle Scholar
    • 17. Kenward MG, Roger JH. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 1997;53(3):983–997.
      MedlineGoogle Scholar
    • 18. Kesner AL. A simulation of noise effects on SUV measurements in gated/ungated data—a story of apples and oranges. European Association on Nuclear Medicine, Vienna, Austria, 2010.
      Google Scholar
    • 19. Boucher L, Rodrigue S, Lecomte R, Bénard F. Respiratory gating for 3-dimensional PET of the thorax: feasibility and initial results. J Nucl Med 2004;45(2):214–219.
      MedlineGoogle Scholar
    • 20. Nehmeh SA, Erdi YE, Ling CC, et al. Effect of respiratory gating on quantifying PET images of lung cancer. J Nucl Med 2002;43(7):876–881.
      MedlineGoogle Scholar
    • 21. Weber WA, Gatsonis CA, Mozley PD, et al. Repeatability of 18F-FDG PET/CT in advanced non-small cell lung cancer: prospective assessment in 2 multicenter trials. J Nucl Med 2015;56(8):1137–1143.
      MedlineGoogle Scholar
    • 22. Koivumäki T, Teuho J, Teräs M, Vauhkonen M, Hakulinen MA. A novel respiratory gating method for oncologic positron emission tomography based on bioimpedance approach. Ann Nucl Med 2015;29(4):351–358.
      MedlineGoogle Scholar
    • 23. Nehmeh SA, Erdi YE. Respiratory motion in positron emission tomography/computed tomography: a review. Semin Nucl Med 2008;38(3):167–176.
      MedlineGoogle Scholar
    • 24. Bettinardi V, Picchio M, Di Muzio N, Gianolli L, Gilardi MC, Messa C. Detection and compensation of organ/lesion motion using 4D-PET/CT respiratory gated acquisition techniques. Radiother Oncol 2010;96(3):311–316.
      MedlineGoogle Scholar
    • 25. Daou D. Respiratory motion handling is mandatory to accomplish the high-resolution PET destiny. Eur J Nucl Med Mol Imaging 2008;35(11):1961–1970.
      MedlineGoogle Scholar
    • 26. Rahmim A, Rousset O, Zaidi H. Strategies for motion tracking and correction in PET. PET Clin 2007;2(2):251–266.
      MedlineGoogle Scholar
    • 27. Liu C, Pierce LA 2nd, Alessio AM, Kinahan PE. The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging. Phys Med Biol 2009;54(24):7345–7362.
      MedlineGoogle Scholar
    • 28. Dawood M, Buther F, Lang N, Jiang X, Schafers KP. Transforming static CT in gated PET/CT studies to multiple respiratory phases. Pattern Recognition, 2006 ICPR 2006 18th International Conference on 2006; 1026–1029.
      Google Scholar
    • 29. Fayad H, Lamare F, Bettinardi V, Roux C, Visvikis D. Respiratory synchronized CT image generation from 4D PET acquisitions. Nuclear Science Symposium Conference Record, 2008 NSS ‘08 IEEE 2008; 5488–5492.
      Google Scholar
    • 30. Kesner AL, Chung JH, Lind KE, et al. Frequency based gating: An alternative, conformal, approach to 4D PET data utilization. Med Phys. 2016;43(3):1451.
      MedlineGoogle Scholar
    • 31. Kesner AL, Daou D, Schindler TH, Koo PJ. Carpe datum: a consideration of the barriers and potential of data-driven PET innovation. J Am Coll Radiol 2016;13(1):106–108.
      MedlineGoogle Scholar
    • 32. Kesner AL, Schleyer PJ, Büther F, Walter MA, Schäfers KP, Koo PJ. On transcending the impasse of respiratory motion correction applications in routine clinical imaging—a consideration of a fully automated data driven motion control framework. EJNMMI Phys 2014;1(1):8.
      MedlineGoogle Scholar
    • 33. Callahan J, Kron T, Schneider ME, Hicks RJ. A prospective investigation into the clinical impact of 4D-PET/CT in the characterisation of solitary pulmonary nodules. Cancer Imaging 2014;14:24.
      MedlineGoogle Scholar
    • 34. Grootjans W, Hermsen R, van der Heijden EH, et al. The impact of respiratory gated positron emission tomography on clinical staging and management of patients with lung cancer. Lung Cancer 2015;90(2):217–223.
      MedlineGoogle Scholar

    Article History

    Received September 28, 2015; revision requested November 9; revision received December 1; accepted December 18; final version accepted February 5, 2016.
    Published online: Mar 30 2016
    Published in print: Oct 2016