scholarly journals A machine-learning approach for computation of fractional flow reserve from coronary computed tomography

2016 ◽  
Vol 121 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Lucian Itu ◽  
Saikiran Rapaka ◽  
Tiziano Passerini ◽  
Bogdan Georgescu ◽  
Chris Schwemmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Fractional Flow Reserve ◽  
Learning Approach ◽  
Fractional Flow ◽  
Coronary Computed Tomography ◽  
Flow Reserve ◽  
Machine Learning Approach

scholarly journals Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio

2020 ◽  
Vol 9 (3) ◽  
pp. 714
Author(s):  
Stefan Baumann ◽  
Markus Hirt ◽  
Christina Rott ◽  
Gökce H. Özdemir ◽  
Christian Tesche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Coronary Artery ◽  
Fractional Flow Reserve ◽  
Coronary Stenosis ◽  
Fractional Flow ◽  
Luminal Diameter ◽  
Coronary Computed Tomography ◽  
Flow Reserve ◽  
Pressure Wire

Background: The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis. Methods: In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFRML software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis. Results: We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75). Conclusion: CT-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.

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