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Journal Abstract Search

604 related items for PubMed ID: 35729425

  • 1. Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm.
    Schuessler M, Saner F, Al-Rashid F, Schlosser T.
    Eur Radiol; 2022 Dec; 32(12):8761-8768. PubMed ID: 35729425
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  • 2. Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.
    Brandt V, Schoepf UJ, Aquino GJ, Bekeredjian R, Varga-Szemes A, Emrich T, Bayer RR, Schwarz F, Kroencke TJ, Tesche C, Decker JA.
    Eur Radiol; 2022 Sep; 32(9):6008-6016. PubMed ID: 35359166
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  • 3. Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.
    Nous FMA, Budde RPJ, Lubbers MM, Yamasaki Y, Kardys I, Bruning TA, Akkerhuis JM, Kofflard MJM, Kietselaer B, Galema TW, Nieman K.
    Eur Radiol; 2020 Jul; 30(7):3692-3701. PubMed ID: 32166492
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  • 4. Combined cCTA and TAVR Planning for Ruling Out Significant CAD: Added Value of ML-Based CT-FFR.
    Gohmann RF, Pawelka K, Seitz P, Majunke N, Heiser L, Renatus K, Desch S, Lauten P, Holzhey D, Noack T, Wilde J, Kiefer P, Krieghoff C, Lücke C, Gottschling S, Ebel S, Borger MA, Thiele H, Panknin C, Horn M, Abdel-Wahab M, Gutberlet M.
    JACC Cardiovasc Imaging; 2022 Mar; 15(3):476-486. PubMed ID: 34801449
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  • 5. Investigation of the predictive value of a novel algorithm based on coronary CT angiography regarding fractional flow reserve and revascularization in patients with stable coronary artery disease.
    Hou C, Lu Y, Ma Y, Li Q, Liu C, Lu M, Cao C, Liu J.
    Heart Vessels; 2024 Mar; 39(3):195-205. PubMed ID: 37897523
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  • 6. Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow.
    Lin A, van Diemen PA, Motwani M, McElhinney P, Otaki Y, Han D, Kwan A, Tzolos E, Klein E, Kuronuma K, Grodecki K, Shou B, Rios R, Manral N, Cadet S, Danad I, Driessen RS, Berman DS, Nørgaard BL, Slomka PJ, Knaapen P, Dey D.
    Circ Cardiovasc Imaging; 2022 Oct; 15(10):e014369. PubMed ID: 36252116
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  • 7. Detecting lesion-specific ischemia in patients with coronary artery disease with computed tomography fractional flow reserve measured at different sites.
    Cai Z, Yu T, Yang Z, Hu H, Lin Y, Zhang H, Chen M, Shi G, Shen J.
    BMC Med Imaging; 2023 Jun 06; 23(1):76. PubMed ID: 37277697
    [Abstract] [Full Text] [Related]

  • 8. Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.
    Guo W, Zhao S, Xu H, He W, Yin L, Yao Z, Xu Z, Jin H, Wu D, Li C, Yang S, Zeng M.
    Eur Radiol; 2024 Sep 06; 34(9):5654-5665. PubMed ID: 38409549
    [Abstract] [Full Text] [Related]

  • 9. Prospective comparison of integrated on-site CT-fractional flow reserve and static CT perfusion with coronary CT angiography for detection of flow-limiting coronary stenosis.
    Guo W, Lin Y, Taniguchi A, Zhu Y, Tripathi P, Yang S, Liu J, Yun H, Jin H, Zhang J, Yang J, Zeng M.
    Eur Radiol; 2021 Jul 06; 31(7):5096-5105. PubMed ID: 33409778
    [Abstract] [Full Text] [Related]

  • 10. Comparison of Coronary Computed Tomography Angiography-Derived vs Invasive Fractional Flow Reserve Assessment: Meta-Analysis with Subgroup Evaluation of Intermediate Stenosis.
    Baumann S, Renker M, Hetjens S, Fuller SR, Becher T, Loßnitzer D, Lehmann R, Akin I, Borggrefe M, Lang S, Wichmann JL, Schoepf UJ.
    Acad Radiol; 2016 Nov 06; 23(11):1402-1411. PubMed ID: 27639627
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  • 12. Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography.
    Renker M, Baumann S, Hamm CW, Tesche C, Kim WK, Savage RH, Coenen A, Nieman K, De Geer J, Persson A, Kruk M, Kepka C, Yang DH, Schoepf UJ.
    J Cardiovasc Comput Tomogr; 2021 Nov 06; 15(6):492-498. PubMed ID: 34119471
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  • 15. A stepwise strategy integrating dynamic stress CT myocardial perfusion and deep learning-based FFRCT in the work-up of stable coronary artery disease.
    Lyu L, Pan J, Li D, Yu D, Li X, Yang W, Dong M, Han Y, Liang Y, Zhang P, Zhang M.
    Eur Radiol; 2024 Aug 06; 34(8):4939-4949. PubMed ID: 38214735
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  • 16. Real world impact of added FFR-CT to coronary CT angiography on clinical decision-making and patient prognosis - IMPACT FFR study.
    Becker LM, Peper J, Verhappen BJLA, Swart LA, Dedic A, van Dockum WG, van der Ent M, Royaards KJ, Niezen A, Hensen JJ, van Kuijk JP, Mohamed Hoesein FAA, Leiner T, Bruning TA, Swaans MJ.
    Eur Radiol; 2023 Aug 06; 33(8):5465-5475. PubMed ID: 36920521
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  • 17. Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.
    von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, Duguay TM, Albrecht MH, van Assen M, Bauer MJ, Savage RH, Pannell JT, De Santis D, Johnson AA, Varga-Szemes A, Bayer RR, Schönberg SO, Nance JW, Tesche C.
    Eur Radiol; 2019 May 06; 29(5):2378-2387. PubMed ID: 30523456
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  • 20. Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study.
    Zhang XL, Zhang B, Tang CX, Wang YN, Zhang JY, Yu MM, Hou Y, Zheng MW, Zhang DM, Hu XH, Xu L, Liu H, Sun ZY, Zhang LJ.
    Eur J Radiol; 2023 Nov 06; 168():111133. PubMed ID: 37827088
    [Abstract] [Full Text] [Related]

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