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学者姓名：刘有军
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Abstract ：
Aortic valve disease (AVD) often coexists with coronary artery disease (CAD), but whether and how the two diseases are correlated remains poorly understood. In this study, a zero-three dimensional (0-3D) multi-scale modeling method was developed to integrate coronary artery hemodynamics, aortic valve dynamics, coronary flow autoregulation mechanism, and systemic hemodynamics into a unique model system, thereby yielding a mathematical tool for quantifying the influences of aortic valve stenosis (AS) and aortic valve regurgitation (AR) on hemodynamics in large coronary arteries. The model was applied to simulate blood flows in six patient-specific left anterior descending coronary arteries (LADs) under various aortic valve conditions (i.e., control (free of AVD), AS, and AR). Obtained results showed that the space-averaged oscillatory shear index (SA-OSI) was significantly higher under the AS condition but lower under the AR condition in comparison with the control condition. Relatively, the overall magnitude of wall shear stress was less affected by AVD. Further data analysis revealed that AS induced the increase in OSI in LADs mainly through its role in augmenting the low-frequency components of coronary flow waveform. These findings imply that AS might increase the risk or progression of CAD by deteriorating the hemodynamic environment in coronary arteries.
Keyword ：
multi-scale modeling multi-scale modeling hemodynamics hemodynamics coronary artery disease coronary artery disease aortic valve disease aortic valve disease
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| GB/T 7714 | Li, Xuanyu , Simakov, Sergey , Liu, Youjun et al. The Influence of Aortic Valve Disease on Coronary Hemodynamics: A Computational Model-Based Study [J]. | BIOENGINEERING-BASEL , 2023 , 10 (6) . |
| MLA | Li, Xuanyu et al. "The Influence of Aortic Valve Disease on Coronary Hemodynamics: A Computational Model-Based Study" . | BIOENGINEERING-BASEL 10 . 6 (2023) . |
| APA | Li, Xuanyu , Simakov, Sergey , Liu, Youjun , Liu, Taiwei , Wang, Yue , Liang, Fuyou . The Influence of Aortic Valve Disease on Coronary Hemodynamics: A Computational Model-Based Study . | BIOENGINEERING-BASEL , 2023 , 10 (6) . |
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Abstract ：
The purpose of the current study was to investigate the effects of left/right coronary artery flow distribution on calculation of fractional flow reserve derived from coronary computed tomography angiography (FFRct) in different dominant types. First, 195 patients were collected to count the distribution ratios of the three categories: right dominance (RD), balanced dominance (BD), and left dominance (LD). Ratios of diameters of the left/right coronary arteries (D-LCA:D-RCA) of the three types were calculated and used to represent the ratio of flow distribution (Q(LCA):Q(RCA)) in the dominant type method. The other method was known as the fixed ratio method (Q(LCA):Q(RCA) = 6:4). Second, a total of 73 patients with coronary artery disease (CAD) were enrolled for numerical calculation. A 0D/3D geometric multiscale model was used for the numerical simulation of FFR and the results of the fixed ratio method and the dominant type method were recorded as F-FFRct and D-FFRct. Lastly, invasive FFR(clinic-FFR)was used as a standard to evaluate the consistency and diagnostic performance of F-FFRct and D-FFRct. Corresponding flow distributions for the dominant type method were Q(LCA):Q(RCA) = 5:5 for RD, Q(LCA):Q(RCA) = 5.5:4.5 for BD, and Q(LCA):Q(RCA) = 6:4 for LD. D-FFRct showed a better correlation than F-FFRct (r = 0.85 vs. r = 0.81, both p < .001); the AUC (95%CI) were 0.974 (0.906-0.997, p < .0001) and 0.960 (0.886-0.992, p < .0001). Accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive values (NPV) for D-FFRct and F-FFRct were 94.52%, 93.75%, 94.74%, 83.33%, 98.18% and 90.41%, 87.50%, 91.23%, 73.68%, 96.30%, respectively. Overall, the left/right coronary artery flow distribution was affected by the dominant type and the dominant type method was superior to the fixed ratio method in detecting coronary ischemic lesions.
Keyword ：
coronary artery dominance coronary artery dominance fractional flow reserve fractional flow reserve coronary artery disease coronary artery disease flow distribution flow distribution
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| GB/T 7714 | Wang, Xue , Liu, Jincheng , Li, Na et al. Left and right coronary artery blood flow distribution method based on dominant type [J]. | INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING , 2023 , 39 (10) . |
| MLA | Wang, Xue et al. "Left and right coronary artery blood flow distribution method based on dominant type" . | INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 39 . 10 (2023) . |
| APA | Wang, Xue , Liu, Jincheng , Li, Na , Ma, Junling , Chen, Mingyan , Feng, Yili et al. Left and right coronary artery blood flow distribution method based on dominant type . | INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING , 2023 , 39 (10) . |
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Abstract ：
Bone has a special structure that is both stiff and elastic, and the composition of bone confers it with an exceptional mechanical property. However, bone substitute materials that are made of the same hydroxyapatite (HA) and collagen do not offer the same mechanical properties. It is important for bionic bone preparation to understand the structure of bone and the mineralization process and factors. In this paper, the research on the mineralization of collagen is reviewed in terms of the mechanical properties in recent years. Firstly, the structure and mechanical properties of bone are analyzed, and the differences of bone in different parts are described. Then, different scaffolds for bone repair are suggested considering bone repair sites. Mineralized collagen seems to be a better option for new composite scaffolds. Last, the paper introduces the most common method to prepare mineralized collagen and summarizes the factors influencing collagen mineralization and methods to analyze its mechanical properties. In conclusion, mineralized collagen is thought to be an ideal bone substitute material because it promotes faster development. Among the factors that promote collagen mineralization, more attention should be given to the mechanical loading factors of bone.
Keyword ：
bone bone biomaterial biomaterial collagen collagen biomechanics biomechanics mineralization mineralization
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| GB/T 7714 | Niu, Yumiao , Du, Tianming , Liu, Youjun . Biomechanical Characteristics and Analysis Approaches of Bone and Bone Substitute Materials [J]. | JOURNAL OF FUNCTIONAL BIOMATERIALS , 2023 , 14 (4) . |
| MLA | Niu, Yumiao et al. "Biomechanical Characteristics and Analysis Approaches of Bone and Bone Substitute Materials" . | JOURNAL OF FUNCTIONAL BIOMATERIALS 14 . 4 (2023) . |
| APA | Niu, Yumiao , Du, Tianming , Liu, Youjun . Biomechanical Characteristics and Analysis Approaches of Bone and Bone Substitute Materials . | JOURNAL OF FUNCTIONAL BIOMATERIALS , 2023 , 14 (4) . |
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Abstract ：
Low-voltage fast (LVF) seizure-onset is one of the two frequently observed temporal lobe seizure-onset patterns. Depth electroencephalogram profile analysis illustrated that the peak amplitude of LVF onset was deep temporal areas, e.g., hippocampus. However, the specific dynamic transition mechanisms between normal hippocampal rhythmic activity and LVF seizure-onset remain unclear. Recently, the optogenetic approach to gain control over epileptic hyper-excitability both in vitro and in vivo has become a novel noninvasive modulation strategy. Here, we combined biophysical modeling to study LVF dynamics following changes in crucial physiological parameters, and investigated the potential optogenetic intervention mechanism for both excitatory and inhibitory control. In an Ammon's horn 3 (CA3) biophysical model with light-sensitive protein channelrhodopsin 2 (ChR2), we found that the cooperative effects of excessive extracellular potassium concentration of parvalbumin-positive (PV+) inhibitory interneurons and synaptic links could induce abundant types of discharges of the hippocampus, and lead to transitions from gamma oscillations to LVF seizure-onset. Simulations of optogenetic stimulation revealed that the LVF seizure-onset and morbid fast spiking could not be eliminated by targeting PV+ neurons, whereas the epileptic network was more sensitive to the excitatory control of principal neurons with strong optogenetic currents. We illustrate that in the epileptic hippocampal network, the trajectories of the normal and the seizure state are in close vicinity and optogenetic perturbations therefore may result in transitions. The network model system developed in this study represents a scientific instrument to disclose the underlying principles of LVF, to characterize the effects of optogenetic neuromodulation, and to guide future treatment for specific types of seizures.
Keyword ：
Hippocampus Hippocampus Low-voltage fast seizure-onset Low-voltage fast seizure-onset Rhythmic oscillation Rhythmic oscillation Parvalbumin-positive inhibitory neurons Parvalbumin-positive inhibitory neurons Optogenetic stimulation Optogenetic stimulation
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| GB/T 7714 | Zhang, Liyuan , Ma, Zhiyuan , Yu, Ying et al. Examining the low-voltage fast seizure-onset and its response to optogenetic stimulation in a biophysical network model of the hippocampus [J]. | COGNITIVE NEURODYNAMICS , 2023 , 18 (1) : 265-282 . |
| MLA | Zhang, Liyuan et al. "Examining the low-voltage fast seizure-onset and its response to optogenetic stimulation in a biophysical network model of the hippocampus" . | COGNITIVE NEURODYNAMICS 18 . 1 (2023) : 265-282 . |
| APA | Zhang, Liyuan , Ma, Zhiyuan , Yu, Ying , Li, Bao , Wu, Shuicai , Liu, Youjun et al. Examining the low-voltage fast seizure-onset and its response to optogenetic stimulation in a biophysical network model of the hippocampus . | COGNITIVE NEURODYNAMICS , 2023 , 18 (1) , 265-282 . |
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Abstract ：
Objective: After coronary artery bypass grafting (CABG) surgery, the main causes of poor instant patency of left internal mammary arteries (LIMAs) are competitive flow and anastomotic stenosis, but how to determine the cause of LIMA non-patency without interfering with the native coronary artery is still a difficult problem to be solved urgently.Methods: In this study, a 0D-3D coupled multiscaled CABG model of anastomotic stenosis and competitive flow was constructed. After calculation, the flow waveform of the LIMA was extracted, and the waveform shape, common clinical parameters (average flow, PI, and DF), and graft flow FFT ratio results (F0/H1 and F0/H2) were analyzed.Results: For LIMA, these three common clinical parameters did not differ significantly between the anastomotic stenosis group and competitive flow group. However, the waveform shape and FFT ratio (especially F0/H2) of the competitive flow group were significantly different from those of the anastomotic stenosis group. When the cause was competitive flow, there was systolic backflow, and F0/H2 was too high (>14.89). When the cause was anastomotic stenosis, the waveform maintained a bimodal state and F0/H2 was in a normal state (about 1.17).Conclusion: When poor instant patency of the LIMA is found after CABG, the causes can be determined by graft flow waveform shape and F0/H2.
Keyword ：
graft patency graft patency multiscaled model multiscaled model lumped parameter model lumped parameter model computational fluid dynamics computational fluid dynamics coronary artery bypass grafting coronary artery bypass grafting
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| GB/T 7714 | Mao, Boyan , Feng, Yue , Duan, Mengyao et al. A Novel Method to Determine the Cause of Left Internal Mammary Artery Instant Non-Patency Based on Transit Time Flow Measurement [J]. | FRONTIERS IN PHYSIOLOGY , 2022 , 13 . |
| MLA | Mao, Boyan et al. "A Novel Method to Determine the Cause of Left Internal Mammary Artery Instant Non-Patency Based on Transit Time Flow Measurement" . | FRONTIERS IN PHYSIOLOGY 13 (2022) . |
| APA | Mao, Boyan , Feng, Yue , Duan, Mengyao , Dong, Yihang , Li, Gaoyang , Li, Bao et al. A Novel Method to Determine the Cause of Left Internal Mammary Artery Instant Non-Patency Based on Transit Time Flow Measurement . | FRONTIERS IN PHYSIOLOGY , 2022 , 13 . |
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Abstract ：
Background Fractional flow reserve (FFR) is considered to be the criterion standard for the clinical diagnosis of functional myocardial ischemia. In this study, we explored the effect of the coronary arterial diameter derived from coronary computed tomography angiography on FFR. Method We retrospectively reviewed the clinical information of 131 patients with moderate coronary artery stenosis. To compare the mean diameter of stenotic vessels, patients were divided into ischemic and nonischemic groups. According to the clinical statistics of the diameter of the ischemic group and the nonischemic group, we established 8 ideal models of coronary artery diameter of 4 mm (40%, 50%, 60%, and 70% stenosis) and diameter of 3 mm (40%, 50%, 60%, and 70% stenosis). Two sets of numerical simulation experiments were carried out: experiment 1 evaluated the variation rate of CT-based computation of non-invasive fractional flow reserve (FFRCT) with vessel diameters of 4 mm and 3 mm under different stenosis rates, and experiment 2 explored the variation of FFRCT with vessel diameters of 4 mm and 3 mm under different cardiac outputs. We simulated changes in the flow of narrow blood vessels by changes in cardiac output. Results According to clinical statistics, the mean +/- SD diameter of stenotic vessels in the ischemic and nonischemic groups was 3.67 +/- 0.77 mm and 3.31 +/- 0.64 mm (P < 0.05 for difference), respectively. In experiment 1, the FFRCT of coronary with a diameter of 4 mm was 0.86, 0.80, 0.66, and 0.35, and that with a diameter of 3 mm was 0.90, 0.84, 0.71, and 0.50, respectively. In experiment 2, the FFRCT of the coronary vessel diameter of 4 mm was 0.84, 0.80, 0.76, and 0.72, respectively. The FFRCT coronary vessels with a diameter of 3 mm were 0.87, 0.84, 0.80, and 0.76, respectively. Conclusions As the stenosis increases, compared with narrow blood vessel of small diameter, the narrow blood vessel with larger diameter is accompanied by faster flow rate changes and is more prone to ischemia.
Keyword ：
coronary artery disease coronary artery disease vessel diameter vessel diameter fractional flow reserve fractional flow reserve influencing factors of myocardial ischemia influencing factors of myocardial ischemia
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| GB/T 7714 | Liu, Jincheng , Huang, Suqin , Wang, Xue et al. Effect of the Coronary Arterial Diameter Derived From Coronary Computed Tomography Angiography on Fractional Flow Reserve [J]. | JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY , 2022 , 46 (3) : 397-405 . |
| MLA | Liu, Jincheng et al. "Effect of the Coronary Arterial Diameter Derived From Coronary Computed Tomography Angiography on Fractional Flow Reserve" . | JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY 46 . 3 (2022) : 397-405 . |
| APA | Liu, Jincheng , Huang, Suqin , Wang, Xue , Li, Bao , Ma, Junling , Sun, Hao et al. Effect of the Coronary Arterial Diameter Derived From Coronary Computed Tomography Angiography on Fractional Flow Reserve . | JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY , 2022 , 46 (3) , 397-405 . |
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Abstract ：
It was hypothesized that the microcirculatory resistance of resting state (Rm-res) might be a good predictor for ischemia. In this study, the quantitative relationship between Rm-res and myocardial ischemia in different stenosed degrees was explored and verified through retrospective analysis, and the diagnostic performance was evaluated. 136 patients were screened and divided into a training set (90 patients) and a validation set (46 patients). In the training set, Rm-res was calculated, and thresholds were determined by exploring the relationship between Rm-res and myocardial ischemia in different stenosed degrees. In the validation set, the diagnostic performance of the thresholds was verified. It was found that the 90 data mean difference (95%CI) of Rm-res between the ischemic group and the non-ischemic group was 63.03 (95 %CI: 25.72-100.34), p < 0.05. In the training set with stenosed degree 41-60%, 61-70%, 71-80%, and > 81%, the average of Rm-res in the ischemic and non-ischemic groups were (80.79, 136.87), (96.41, 172.62), (128.99, 198.94) and (175.95, 310.79) mmHg/ s/ml. The Rm-res thresholds were 87.18, 118.96, 142.35, and 177.39 mmHg/s/ml. In the validation set, the overall sensitivity, specificity, PPV, NPV, and accuracy were 73.3%, 77.4%, 61.1%, 85.7%, and 76.1%. In conclusion, Rm-res had a significant predictor on myocardial ischemia. As a smaller Rm-res represents greater myocardial mass perfusion, it is more likely that a stenosis will have a functional impact. Threshold analysis showed that Rm-res of different stenosed degrees was a quantitative predictor of myocardial ischemia, which could assist physicians with clinical treatment strategies.
Keyword ：
Quantitative relationship Quantitative relationship Coronary microcirculatory resistance Coronary microcirculatory resistance Myocardial ischemia Myocardial ischemia
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| GB/T 7714 | Li, Na , Li, Bao , Liu, Jincheng et al. The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease [J]. | JOURNAL OF BIOMECHANICS , 2022 , 140 . |
| MLA | Li, Na et al. "The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease" . | JOURNAL OF BIOMECHANICS 140 (2022) . |
| APA | Li, Na , Li, Bao , Liu, Jincheng , Feng, Yili , Zhang, Liyuan , Liu, Jian et al. The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease . | JOURNAL OF BIOMECHANICS , 2022 , 140 . |
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Abstract ：
Background and objectives A quick calculation approach of steady-state fractional flow reserve (FFRss) based on computed tomography angiography (CTA) images is a reliable non-invasive way of calculate FFR, the assumptions used in the research should be study further to increase forecast accuracy. The effect of inlet and outlet boundary conditions on FFRss was investigated. Methods 15 patients who had been diagnosed with coronary artery disease were enrolled in this study. We investigated the sensitivity of calculating FFR to boundary circumstances, using invasive FFR as a benchmark. There are two types of inlet: (1) aortic pressure based on clinically measured. (2) mean pressure calculated based on physiological formula; we further studied the outlet changes of FFRss under different coronary vasodilation responses (24%, 48%, 72%). Results According to the calculate FFR results of all patients, FFRSST (based on the clinical experiment) and FFRSSM (based on the physiological formula) {r = 0.99, [95% confidence interval (CI):0.0.94 to 1.14] (p < 0.001)}. Although the pressure difference between the two pressure boundary conditions is 15 mmHg, the calculated FFR result does not change significantly. The microcirculation resistance of the outlet gradually rose as the vasodilation state changed, and the computed FFR increased. Conclusions A numerical analysis of the effects of proximal and distal boundary constraints of computational models on computed CT-FFR is presented. The findings revealed that distal boundary circumstances (hyperemic vasodilation response of coronary micro-vessels) have a significant impact on FFR, providing evidence to guide the development and application of a computational model for estimating FFR.
Keyword ：
aortic pressure aortic pressure calculating FFR calculating FFR Coronary heart disease Coronary heart disease microcirculation resistance microcirculation resistance myocardial ischemia myocardial ischemia
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| GB/T 7714 | Liu, Jincheng , Huang, Suqin , Wang, Xue et al. On inlet pressure boundary conditions for CT-based computation of fractional flow reserve: clinical measurement of aortic pressure [J]. | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING , 2022 , 26 (5) : 517-526 . |
| MLA | Liu, Jincheng et al. "On inlet pressure boundary conditions for CT-based computation of fractional flow reserve: clinical measurement of aortic pressure" . | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 26 . 5 (2022) : 517-526 . |
| APA | Liu, Jincheng , Huang, Suqin , Wang, Xue , Li, Bao , Ma, Junling , Sun, Yutong et al. On inlet pressure boundary conditions for CT-based computation of fractional flow reserve: clinical measurement of aortic pressure . | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING , 2022 , 26 (5) , 517-526 . |
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Objective: To explore the influence of the blood flow-diameter scaling laws of Q alpha D-3, Q alpha D-2.7 and Q alpha D-7/3 on the numerical simulation of fraction flow reserve based on CTA images and to find the optimal exponents. Methods: 1) 26 patients with coronary artery disease were screened according to the inclusion criteria; 2) Microcirculation resistance (R-m) was calculated under the 3, 2.7 and 7/3 power of the flow-diameter scaling law, which were recorded as 3R(m), 2.7R(m) and 7/3R(m), respectively; 3) 3R(m), 2.7R(m) and 7/3R(m) were used as exit boundary conditions to simulate FFRCT, quoted as 3FFRCT, 2.7FFRCT and 7/3FFRCT, respectively; 4) The correlation and diagnostic performance between three kinds of FFRCT and FFR were analyzed. Results: The p-values of comparing 3Rm, 2.7Rm and 7/3Rm with FFR were 0.004, 0.005 and 0.010, respectively; the r value between 7/3FFRCT and FFR (0.96) was better than that of 3FFRCT (0.95) and 2.7FFRCT (0.95); the 95% LoA between 7/3FFRCT and FFR (-0.08 similar to 0.11) was smaller than that of 3FFRCT (-0.10 similar to 0.12) and 2.7FFRCT (-0.09 similar to 0.11); the AUC and accuracy of 7/3FFRCT [0.962 (0.805-0.999), 96.15%] were the same as those of 2.7FFRCT [0.962 (0.805-0.999), 96.15%] and better than those of 3FFRCT [0.944 (0.777-0.996), 92.3%]. The prediction threshold of 7/3FFRCT (0.791) was closer to 0.8 than that of 3FFRCT (0.816) and 2.7FFRCT (0.787). Conclusion: The blood flow-diameter scaling law affects the FFRCT simulation by influencing the exit boundary condition R-m of the calculation. With Q alpha D-7/3, FFRCT had the highest diagnostic performance. The blood flow-diameter scaling law provides theoretical support for the blood flow distribution in the bifurcated vessel and improves the FFRCT model.
Keyword ：
0D/3D geometric multi-scale model 0D/3D geometric multi-scale model blood flow-diameter scaling law blood flow-diameter scaling law microcirculation resistance microcirculation resistance FFRCT FFRCT numerical calculation numerical calculation
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| GB/T 7714 | Li, Na , Li, Bao , Feng, Yili et al. Impact of coronary bifurcated vessels flow-diameter scaling laws on fractional flow reserve based on computed tomography images (FFRCT) [J]. | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (3) : 3127-3146 . |
| MLA | Li, Na et al. "Impact of coronary bifurcated vessels flow-diameter scaling laws on fractional flow reserve based on computed tomography images (FFRCT)" . | MATHEMATICAL BIOSCIENCES AND ENGINEERING 19 . 3 (2022) : 3127-3146 . |
| APA | Li, Na , Li, Bao , Feng, Yili , Ma, Junling , Zhang, Liyuan , Liu, Jian et al. Impact of coronary bifurcated vessels flow-diameter scaling laws on fractional flow reserve based on computed tomography images (FFRCT) . | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (3) , 3127-3146 . |
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Abstract ：
Coronary artery stenosis resistance (SR) is a key factor for noninvasive calculations of fractional flow reserve derived from coronary CT angiography (FFRCT). Existing computational fluid dynamics (CFD) methods, including three-dimensional (3-D) computational and zero-dimensional (0-D) analytical models, are usually limited by high calculation cost or low precision. In this study, we have developed a multi-input back-propagation neural network (BPNN) that can rapidly and accurately predict coronary SR. A training data set including 3,028 idealized anatomic coronary artery stenosis models was constructed for 3-D CFD calculation of SR with specific blood flow boundaries. Based on 3-D calculation results, we established a BPNN whose input is geometric parameters and blood flow, whereas output is SR. Then, a test set (324 cases) was constructed to evaluate the performance of the BPNN model. To verify the validity and practicability of the network, BPNN prediction results were compared with 3-D CFD and O-D analytical model results from patient-specific models. For test set, the mean square error (MSE) between CFD and prediction results was 2.97%, linear regression analysis indicating a good correlation between the two (P < 0.001). For 30 patient-specific models, the MSE of BPNN and the 0-D model were 3.26 and 9.7%, respectively. The calculation time for BPNN and the 3-D CFD model for 30 cases was about 2.15 s and 2 h, respectively. The present results demonstrate the practicability of using deep learning methods for fast and accurate predictions of coronary artery SR. Our study represents an advance in noninvasive calculations of FFRCT. NEW & NOTEWORTHY This study developed a multi-input back-propagation neural network (BPNN) that can be used to predict coronary artery stenosis resistance by inputting vascular geometric parameters and blood flow. Compared with previous studies, the network developed in this study can accurately and rapidly predict coronary artery stenosis resistance, which can not only meet clinical requirements but also reduce the cost of calculation duration. This study contributes to the noninvasive methods for the numerical calculation of fractional flow reserve derived from coronary CT angiography (FFRCT) and indicates that this technique can potentially be used for evaluating myocardial ischemia.
Keyword ：
fractional flow reserve fractional flow reserve coronary artery coronary artery stenosis resistance stenosis resistance deep learning deep learning
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| GB/T 7714 | Sun, Hao , Liu, Jincheng , Feng, Yili et al. Deep learning-based prediction of coronary artery stenosis resistance [J]. | AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY , 2022 , 323 (6) : H1194-H1205 . |
| MLA | Sun, Hao et al. "Deep learning-based prediction of coronary artery stenosis resistance" . | AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY 323 . 6 (2022) : H1194-H1205 . |
| APA | Sun, Hao , Liu, Jincheng , Feng, Yili , Xi, Xiaolu , Xu, Ke , Zhang, Liyuan et al. Deep learning-based prediction of coronary artery stenosis resistance . | AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY , 2022 , 323 (6) , H1194-H1205 . |
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