WITHDRAWN: Coronary artery segmentation based on ACMA-Net and unscented Kalman filter algorithm.

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Deep-learning-based real-time individualization for reduce-order haemodynamic model.

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms. The data were expanded to 5000 synthesised cases that all fell within the physiological range of each indicator. LPM of the human blood circulation system was established. A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm. Clinically collected data from the other 100 cases were used as the test set to verify the accuracy of the individual LPM parameters predicted by DPNN. The results show that DPNN provided good convergence in the training process. In the test set, compared with clinical measurements, the mean differences between each haemodynamic indicator and the estimate calculated by the individual LPM based on the DPNN were about 10 %. Furthermore, DPNN prediction only takes 4 s for 100 cases. The DPNN proposed in this study permits real-time and accurate individualization of LPM's. When facing medical issues involving haemodynamics, it lays the foundation for patient-specific numerical simulation, which may be beneficial for potential clinical application.

Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance.

BACKGROUND AND OBJECTIVE: Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. METHODS: A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. RESULTS: A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984. CONCLUSION: In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

Treatment strategy of different enhanced external counterpulsation frequencies for coronary heart disease and cerebral ischemic stroke: A hemodynamic numerical simulation study.

BACKGROUND AND OBJECTIVES: Currently, enhanced external counterpulsation (EECP) devices mainly produce one counterpulsation per cardiac cycle. However, the effect of other frequencies of EECP on the hemodynamics of coronary and cerebral arteries is still unclear. It should be investigated whether one counterpulsation per cardiac cycle leads to the optimal therapeutic effect in patients with different clinical indications. Therefore, we measured the effects of different frequencies of EECP on the hemodynamics of coronary and cerebral arteries to determine the optimal counterpulsation frequency for the treatment of coronary heart disease and cerebral ischemic stroke. METHODS: We established 0D/3D geometric multi-scale hemodynamics model of coronary and cerebral arteries in two healthy individuals, and performed clinical trials of EECP to verify the accuracy of the multi-scale hemodynamics model. The pressure amplitude (35 kPa) and pressurization duration (0.6 s) were fixed. The global and local hemodynamics of coronary and cerebral arteries were studied by changing counterpulsation frequency. Three frequency modes, including one counterpulsation in one, two and three cardiac cycles, were applied. Global hemodynamic indicators included diastolic / systolic blood pressure (D/S), mean arterial pressure (MAP), coronary artery flow (CAF), and cerebral blood flow (CBF), whereas local hemodynamic effects included area-time-averaged wall shear stress (ATAWSS) and oscillatory shear index (OSI). The optimal counterpulsation frequency was verified by analyzing the hemodynamic effects of different frequency modes of counterpulsation cycles and full cycles. RESULTS: In the full cycle, CAF, CBF and ATAWSS of coronary and cerebral arteries were the highest when one counterpulsation per cardiac cycle was applied. However, in the counterpulsation cycle, the global and local hemodynamic indicators of coronary and cerebral artery reached the highest when one counterpulsation in one cardiac cycle or two cardiac cycles was applied. CONCLUSIONS: For clinical application, the results of global hemodynamic indicators in the full cycle have more clinical practical significance. Combined with the comprehensive analysis of local hemodynamic indicators, it can be concluded that for coronary heart disease and cerebral ischemic stroke, applying one counterpulsation per cardiac cycle may provide the optimal benefit.

Reliability of using generic flow conditions to quantify aneurysmal haemodynamics: A comparison against simulations incorporating boundary conditions measured in vivo.

BACKGROUND AND OBJECTIVES: Initiation, growth, and rupture of intracranial aneurysms are believed to be closely related to their local haemodynamic environment. While haemodynamics can be characterised by use of computational fluid dynamics (CFD), its reliability depends heavily upon accurate assumption of the boundary conditions. Herein, we compared the simulated aneurysmal haemodynamics obtained by use of generic boundary conditions against those obtained under flow conditions measured in vivo. METHODS: We prospectively recruited 19 patients with intracranial aneurysms requiring 3-dimensional rotational angiography, during which blood pressure at the internal carotid artery was probed by catheter and flowrate measured by a dedicated software tool. Using these flow conditions measured in vivo, we quantified the aneurysmal haemodynamics for each patient by CFD, and then compared the results with those derived from a generic condition reported in the literature, in terms of the time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and percentage of the intra-aneurysmal flow (PIAF). In addition, the effects on aneurysmal haemodynamics of different outflow strategies (splitting method vs. Murray's Law) and simulation schemes (transient vs. steady-state) relative to each flow condition were also assessed. RESULTS: Differences in the simulated TAWSS (-6.08 ± 10.64 Pa, p = 0.001), OSI (0.06 ± 0.13, p = 0.001), and PIAF (-0.05 ± 0.20, p = 0.012) between the patient-specific and generic boundary conditions were found to be statistically significant, in contrast to that in the RRT (49 ± 307 Pa-1, p = 0.062). Outflow strategies did not yield statistically significant differences in any of the investigated parameters (all p > 0.05); rather, the resulting parameters were found to be in good correlations (all r > 0.71, p < 0.001). Difference between the aneurysmal TAWSS and the WSS derived from cycle-averaged flowrate condition was found to be minor (0.66 ± 1.36 Pa, p = 0.000), so was that between PIAFs obtained respectively from the transient and steady-state simulations (0.02 ± 0.05, p = 0.000). CONCLUSIONS: Incorporating into simulation the patient-specific boundary conditions is critical for CFD to characterise aneurysmal haemodynamics, while outflow strategies may not introduce significant uncertainties. Steady-state simulation incorporating the cycle-averaged flow condition may produce unbiased WSS and PIAF compared to the transient analysis.