CT-based radiomics and machine learning for the prediction of myocardial ischemia: Toward increasing quantification

Andrew Lin; Damini Dey

doi:10.1007/s12350-020-02261-7

CT-based radiomics and machine learning for the prediction of myocardial ischemia: Toward increasing quantification

Research output: Contribution to journal › Editorial › Other › peer-review

Original language	English
Pages (from-to)	275-277
Number of pages	3
Journal	Journal of Nuclear Cardiology
Volume	29
Issue number	1
DOIs	https://doi.org/10.1007/s12350-020-02261-7
Publication status	Published - Feb 2022
Externally published	Yes

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10.1007/s12350-020-02261-7

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title = "CT-based radiomics and machine learning for the prediction of myocardial ischemia: Toward increasing quantification",

author = "Andrew Lin and Damini Dey",

note = "Funding Information: Dr Andrew Lin and Dr Damini Dey are supported by a grant from the National Heart, Lung, and Blood Institute, USA [1R01HL133616]. ",

year = "2022",

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language = "English",

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pages = "275--277",

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T2 - Toward increasing quantification

AU - Lin, Andrew

AU - Dey, Damini

N1 - Funding Information: Dr Andrew Lin and Dr Damini Dey are supported by a grant from the National Heart, Lung, and Blood Institute, USA [1R01HL133616].

PY - 2022/2

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U2 - 10.1007/s12350-020-02261-7

DO - 10.1007/s12350-020-02261-7

M3 - Editorial

C2 - 32676906

AN - SCOPUS:85087951835

SN - 1071-3581

VL - 29

SP - 275

EP - 277

JO - Journal of Nuclear Cardiology

JF - Journal of Nuclear Cardiology

IS - 1

ER -

CT-based radiomics and machine learning for the prediction of myocardial ischemia: Toward increasing quantification

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