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Journal Abstract Search

329 related items for PubMed ID: 30903334

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  • 22. Relationship of the Duke jeopardy score combined with minimal lumen diameter as assessed by computed tomography angiography to the hemodynamic relevance of coronary artery stenosis.
    Yu M, Zhao Y, Li W, Lu Z, Wei M, Zhou W, Zhang J.
    J Cardiovasc Comput Tomogr; 2018; 12(3):247-254. PubMed ID: 29598929
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  • 31. Optimized interpretation of fractional flow reserve derived from computed tomography: Comparison of three interpretation methods.
    Takagi H, Ishikawa Y, Orii M, Ota H, Niiyama M, Tanaka R, Morino Y, Yoshioka K.
    J Cardiovasc Comput Tomogr; 2019; 13(2):134-141. PubMed ID: 30385326
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  • 33. Diagnostic Performance of Transluminal Attenuation Gradient and Noninvasive Fractional Flow Reserve Derived from 320-Detector Row CT Angiography to Diagnose Hemodynamically Significant Coronary Stenosis: An NXT Substudy.
    Ko BS, Wong DT, Nørgaard BL, Leong DP, Cameron JD, Gaur S, Marwan M, Achenbach S, Kuribayashi S, Kimura T, Meredith IT, Seneviratne SK.
    Radiology; 2016 Apr; 279(1):75-83. PubMed ID: 26444662
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  • 35. Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve.
    Li Y, Yu M, Dai X, Lu Z, Shen C, Wang Y, Lu B, Zhang J.
    Radiology; 2019 Nov; 293(2):305-314. PubMed ID: 31549943
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  • 36. Diagnostic accuracy and discrimination of ischemia by fractional flow reserve CT using a clinical use rule: results from the Determination of Fractional Flow Reserve by Anatomic Computed Tomographic Angiography study.
    Thompson AG, Raju R, Blanke P, Yang TH, Mancini GB, Budoff MJ, Norgaard BL, Min JK, Leipsic JA.
    J Cardiovasc Comput Tomogr; 2015 Nov; 9(2):120-8. PubMed ID: 25819194
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  • 37. Diagnostic efficacy of fractional flow reserve with coronary angiography in dual-source computed tomography scanner.
    Xia G, Fan D, Yao X, Guan G, Wang J.
    Acta Cardiol; 2018 Feb; 73(1):76-83. PubMed ID: 28830299
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  • 38. CT Angiographic and Plaque Predictors of Functionally Significant Coronary Disease and Outcome Using Machine Learning.
    Yang S, Koo BK, Hoshino M, Lee JM, Murai T, Park J, Zhang J, Hwang D, Shin ES, Doh JH, Nam CW, Wang J, Chen S, Tanaka N, Matsuo H, Akasaka T, Choi G, Petersen K, Chang HJ, Kakuta T, Narula J.
    JACC Cardiovasc Imaging; 2021 Mar; 14(3):629-641. PubMed ID: 33248965
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  • 39. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study.
    Dey D, Gaur S, Ovrehus KA, Slomka PJ, Betancur J, Goeller M, Hell MM, Gransar H, Berman DS, Achenbach S, Botker HE, Jensen JM, Lassen JF, Norgaard BL.
    Eur Radiol; 2018 Jun; 28(6):2655-2664. PubMed ID: 29352380
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  • 40. [Value of fractional flow reserve derived from coronary computed tomographic angiography and plaque quantitative analysis in predicting adverse outcomes of non-obstructive coronary heart disease].
    Liu J, Wu Y, Huang H, Wang P, Wu Q, Qiao H.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jun; 35(6):615-619. PubMed ID: 37366128
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