393 related articles for article (PubMed ID: 30421020)
1. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis.
van Hamersvelt RW; Zreik M; Voskuil M; Viergever MA; Išgum I; Leiner T
Eur Radiol; 2019 May; 29(5):2350-2359. PubMed ID: 30421020
[TBL] [Abstract][Full Text] [Related]
2. Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis.
Zreik M; Lessmann N; van Hamersvelt RW; Wolterink JM; Voskuil M; Viergever MA; Leiner T; Išgum I
Med Image Anal; 2018 Feb; 44():72-85. PubMed ID: 29197253
[TBL] [Abstract][Full Text] [Related]
3. Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR.
Hu X; Yang M; Han L; Du Y
Int J Cardiovasc Imaging; 2018 Dec; 34(12):1987-1996. PubMed ID: 30062537
[TBL] [Abstract][Full Text] [Related]
4. 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; 29(5):2378-2387. PubMed ID: 30523456
[TBL] [Abstract][Full Text] [Related]
5. Transluminal attenuation gradient in coronary computed tomography angiography is a novel noninvasive approach to the identification of functionally significant coronary artery stenosis: a comparison with fractional flow reserve.
Wong DT; Ko BS; Cameron JD; Nerlekar N; Leung MC; Malaiapan Y; Crossett M; Leong DP; Worthley SG; Troupis J; Meredith IT; Seneviratne SK
J Am Coll Cardiol; 2013 Mar; 61(12):1271-9. PubMed ID: 23414792
[TBL] [Abstract][Full Text] [Related]
6. Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.
Takahashi M; Kosuda R; Takaoka H; Yokota H; Mori Y; Ota J; Horikoshi T; Tachibana Y; Kitahara H; Sugawara M; Kanaeda T; Suyari H; Uno T; Kobayashi Y
Heart Vessels; 2023 Nov; 38(11):1318-1328. PubMed ID: 37552271
[TBL] [Abstract][Full Text] [Related]
7. Noninvasive diagnosis of ischemia-causing coronary stenosis using CT angiography: diagnostic value of transluminal attenuation gradient and fractional flow reserve computed from coronary CT angiography compared to invasively measured fractional flow reserve.
Yoon YE; Choi JH; Kim JH; Park KW; Doh JH; Kim YJ; Koo BK; Min JK; Erglis A; Gwon HC; Choe YH; Choi DJ; Kim HS; Oh BH; Park YB
JACC Cardiovasc Imaging; 2012 Nov; 5(11):1088-96. PubMed ID: 23153908
[TBL] [Abstract][Full Text] [Related]
8. Diagnostic performance of on-site computed CT-fractional flow reserve based on fluid structure interactions: comparison with invasive fractional flow reserve and instantaneous wave-free ratio.
Fujimoto S; Kawasaki T; Kumamaru KK; Kawaguchi Y; Dohi T; Okonogi T; Ri K; Yamada S; Takamura K; Kato E; Kato Y; Hiki M; Okazaki S; Aoki S; Mitsouras D; Rybicki FJ; Daida H
Eur Heart J Cardiovasc Imaging; 2019 Mar; 20(3):343-352. PubMed ID: 30107511
[TBL] [Abstract][Full Text] [Related]
9. Diagnostic performance of quantitative coronary computed tomography angiography and quantitative coronary angiography to predict hemodynamic significance of intermediate-grade stenoses.
Ghekiere O; Dewilde W; Bellekens M; Hoa D; Couvreur T; Djekic J; Coolen T; Mancini I; Vanhoenacker PK; Dendale P; Nchimi A
Int J Cardiovasc Imaging; 2015 Dec; 31(8):1651-61. PubMed ID: 26323355
[TBL] [Abstract][Full Text] [Related]
10. Diagnostic performance of transluminal attenuation gradient and fractional flow reserve by coronary computed tomographic angiography (FFR(CT)) compared to invasive FFR: a sub-group analysis from the DISCOVER-FLOW and DeFACTO studies.
Nakanishi R; Matsumoto S; Alani A; Li D; Kitslaar PH; Broersen A; Koo BK; Min JK; Budoff MJ
Int J Cardiovasc Imaging; 2015 Aug; 31(6):1251-9. PubMed ID: 25904402
[TBL] [Abstract][Full Text] [Related]
11. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.
Hae H; Kang SJ; Kim WJ; Choi SY; Lee JG; Bae Y; Cho H; Yang DH; Kang JW; Lim TH; Lee CH; Kang DY; Lee PH; Ahn JM; Park DW; Lee SW; Kim YH; Lee CW; Park SW; Park SJ
PLoS Med; 2018 Nov; 15(11):e1002693. PubMed ID: 30422987
[TBL] [Abstract][Full Text] [Related]
12. 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; 23(11):1402-1411. PubMed ID: 27639627
[TBL] [Abstract][Full Text] [Related]
13. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium.
Coenen A; Kim YH; Kruk M; Tesche C; De Geer J; Kurata A; Lubbers ML; Daemen J; Itu L; Rapaka S; Sharma P; Schwemmer C; Persson A; Schoepf UJ; Kepka C; Hyun Yang D; Nieman K
Circ Cardiovasc Imaging; 2018 Jun; 11(6):e007217. PubMed ID: 29914866
[TBL] [Abstract][Full Text] [Related]
14. CT morphological index provides incremental value to machine learning based CT-FFR for predicting hemodynamically significant coronary stenosis.
Yu M; Lu Z; Li W; Wei M; Yan J; Zhang J
Int J Cardiol; 2018 Aug; 265():256-261. PubMed ID: 29885695
[TBL] [Abstract][Full Text] [Related]
15. Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making.
Tesche C; Vliegenthart R; Duguay TM; De Cecco CN; Albrecht MH; De Santis D; Langenbach MC; Varga-Szemes A; Jacobs BE; Jochheim D; Baquet M; Bayer RR; Litwin SE; Hoffmann E; Steinberg DH; Schoepf UJ
Am J Cardiol; 2017 Dec; 120(12):2121-2127. PubMed ID: 29102036
[TBL] [Abstract][Full Text] [Related]
16. Simplified Bernoulli formula to predict flow limiting stenosis at coronary computed tomography angiography.
Tomizawa N; Yamamoto K; Inoh S; Nojo T; Nakamura S
Clin Imaging; 2018; 51():104-110. PubMed ID: 29454266
[TBL] [Abstract][Full Text] [Related]
17. High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.
Giannopoulos AA; Keller L; Sepulcri D; Boehm R; Garefa C; Venugopal P; Mitra J; Ghose S; Deak P; Pack JD; Davis CL; Stähli BE; Stehli J; Pazhenkottil AP; Kaufmann PA; Buechel RR
AJR Am J Roentgenol; 2023 Oct; 221(4):460-470. PubMed ID: 37132550
[No Abstract] [Full Text] [Related]
18. 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
[TBL] [Abstract][Full Text] [Related]
19. Correlation of FFR-derived from CT and stress perfusion CMR with invasive FFR in intermediate-grade coronary artery stenosis.
Ghekiere O; Bielen J; Leipsic J; Dewilde W; Mancini I; Hansen D; Dendale P; Nchimi A
Int J Cardiovasc Imaging; 2019 Mar; 35(3):559-568. PubMed ID: 30284138
[TBL] [Abstract][Full Text] [Related]
20. Computationally simulated fractional flow reserve from coronary computed tomography angiography based on fractional myocardial mass.
Han H; Bae YG; Hwang ST; Kim HY; Park I; Kim SM; Choe Y; Moon YJ; Choi JH
Int J Cardiovasc Imaging; 2019 Jan; 35(1):185-193. PubMed ID: 30128848
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
