Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3)

Jun Pyo Kim; Jeonghun Kim; Yeshin Kim; Seung Hwan Moon; Yu Hyun Park; Sole Yoo; Hyemin Jang; Hee Jin Kim; Duk L. Na; Sang Won Seo; Joon Kyung Seong

doi:10.1007/s00259-020-04925-5

Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3)

Jun Pyo Kim, Jeonghun Kim, Yeshin Kim, Seung Hwan Moon, Yu Hyun Park, Sole Yoo, Hyemin Jang, Hee Jin Kim, Duk L. Na, Sang Won Seo, Joon Kyung Seong

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

The correct acknowledgments should be the below: Acknowledgements This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF- 2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health&Welfare, Republic ofKorea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2018-ER6203-02]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].

Original language	English
Pages (from-to)	2480
Number of pages	1
Journal	European Journal of Nuclear Medicine and Molecular Imaging
Volume	47
Issue number	10
DOIs	https://doi.org/10.1007/s00259-020-04925-5
Publication status	Published - 2020 Sept 1

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF-2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health&Welfare, Republic ofKorea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2018-ER6203-02]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].

Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

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Kim, J. P., Kim, J., Kim, Y., Moon, S. H., Park, Y. H., Yoo, S., Jang, H., Kim, H. J., Na, D. L., Seo, S. W., & Seong, J. K. (2020). Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3). European Journal of Nuclear Medicine and Molecular Imaging, 47(10), 2480. https://doi.org/10.1007/s00259-020-04925-5

Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3). / Kim, Jun Pyo; Kim, Jeonghun; Kim, Yeshin et al.
In: European Journal of Nuclear Medicine and Molecular Imaging, Vol. 47, No. 10, 01.09.2020, p. 2480.

Research output: Contribution to journal › Comment/debate › peer-review

Kim, JP, Kim, J, Kim, Y, Moon, SH, Park, YH, Yoo, S, Jang, H, Kim, HJ, Na, DL, Seo, SW & Seong, JK 2020, 'Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3)', European Journal of Nuclear Medicine and Molecular Imaging, vol. 47, no. 10, pp. 2480. https://doi.org/10.1007/s00259-020-04925-5

Kim JP, Kim J, Kim Y, Moon SH, Park YH, Yoo S et al. Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3). European Journal of Nuclear Medicine and Molecular Imaging. 2020 Sept 1;47(10):2480. doi: 10.1007/s00259-020-04925-5

Kim, Jun Pyo ; Kim, Jeonghun ; Kim, Yeshin et al. / Correction to : Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3). In: European Journal of Nuclear Medicine and Molecular Imaging. 2020 ; Vol. 47, No. 10. pp. 2480.

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title = "Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3)",

abstract = "The correct acknowledgments should be the below: Acknowledgements This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF- 2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health&Welfare, Republic ofKorea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2018-ER6203-02]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].",

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note = "Funding Information: Acknowledgements This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF-2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health&Welfare, Republic ofKorea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2018-ER6203-02]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034]. Publisher Copyright: {\textcopyright} 2020, Springer-Verlag GmbH Germany, part of Springer Nature.",

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AU - Kim, Jun Pyo

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N2 - The correct acknowledgments should be the below: Acknowledgements This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF- 2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health&Welfare, Republic ofKorea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2018-ER6203-02]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].

AB - The correct acknowledgments should be the below: Acknowledgements This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF- 2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health&Welfare, Republic ofKorea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2018-ER6203-02]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].

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Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes (European Journal of Nuclear Medicine and Molecular Imaging, (2020), 47, 8, (1971-1983), 10.1007/s00259-019-04663-3)

Abstract

Bibliographical note

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