A phantom and in vivo simulation of coronary flow to calculate fractional flow reserve using a mesh-free model.
Nobuo TomizawaYui NozakiShinichiro FujimotoDaigo TakahashiAyako KudoYuki KamoChihiro AoshimaYuko KawaguchiKazuhisa TakamuraMakoto HikiTomotaka DohiShinya OkazakiTohru MinaminoShigeki AokiPublished in: The international journal of cardiovascular imaging (2021)
Moving particle semi-implicit (MPS) method is a mesh-free method to perform computational fluid dynamics (CFD). The purpose of this study was to calculate the simulated fractional flow reserve (sFFR) using a coronary stenosis model, and to validate the MPS-derived sFFR against invasive FFR using clinical coronary CT data. Coronary flow simulation included 21 stenosis models with stenosis ranging 30-70%. Patient coronary analysis was performed in 76 consecutive patients (100 vessels) who underwent coronary CT angiography and subsequent invasive FFR between November 2016 and March 2020. Accuracy of sFFR and CT angiography for diagnosis of invasive FFR ≤ 0.80 was compared. Quantitative morphological stenosis data of CT angiography were also obtained. Area stenosis showed a good correlation to sFFR (R2 = 0.996, p < 0.001) in coronary stenosis models. In the patient study, the mean FFR value was 0.82 ± 0.10, and 37 out of 100 vessels showed FFR ≤ 0.80. FFR and sFFR values showed a good correlation (R2 = 0.59, p < 0.001) with a slight underestimation of sFFR as compared with FFR (mean difference - 0.015 ± 0.096, p = 0.12). The sensitivity, specificity, positive predictive value, and negative predictive value of sFFR to predict FFR ≤ 0.80 was 86%, 89%, 82%, 92%, respectively. The accuracy to predict FFR ≤ 0.80 using sFFR was greater than using diameter stenosis and minimum lumen area (88% vs. 74%, p = 0.008). CFD using the MPS method showed feasible results validated against invasive FFR. The accuracy to predict significant stenosis was higher than morphological stenosis.
Keyphrases
- coronary artery
- coronary artery disease
- aortic stenosis
- ejection fraction
- end stage renal disease
- magnetic resonance imaging
- case report
- chronic kidney disease
- heart failure
- electronic health record
- magnetic resonance
- transcatheter aortic valve replacement
- positron emission tomography
- data analysis
- artificial intelligence
- patient reported outcomes
- pet ct