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^*

^*Corresponding author for this work

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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10.1007/s00259-020-04925-5

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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].",

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

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

AU - Kim, Jeonghun

AU - Kim, Yeshin

AU - Moon, Seung Hwan

AU - Park, Yu Hyun

AU - Yoo, Sole

AU - Jang, Hyemin

AU - Kim, Hee Jin

AU - Na, Duk L.

AU - Seo, Sang Won

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N1 - 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.

PY - 2020/9/1

Y1 - 2020/9/1

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

ASJC Scopus subject areas

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