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Objectives: A novel surgical strategy using haemodynamic analyses based on virtual operations with computational simulations has been introduced for complicated pulmonary stenosis. We evaluated the efficacy of this strategy., Methods: Six patients were enrolled. Before surgery, the optimal pulmonary arteries were constructed based on computational fluid dynamics using 3-dimensional computed tomography. Energy loss (EL, mW) and wall shear stress (WSS, Pa) were calculated. We compared the shapes of preoperative and optimal pulmonary arteries to determine the surgical strategy, including the incision line and the shape of the patch (virtual surgery). EL and WSS were compared between virtual and actual surgeries using flow analysis., Results: In both the virtual and actual surgeries, postoperative EL tended to be lower than the preoperative EL, although there were no significant differences (P = 0.12 and P = 0.17, respectively). The mean WSS in the virtual surgery was significantly reduced from 112 ± 130 Pa to 25 ± 24 Pa (P = 0.028). After the actual surgery, the mean WSS was also significantly reduced to 30 ± 23 Pa (P = 0.047). There were no significant differences in the values for EL and WSS before and after surgery or between virtual and actual surgery (P = 0.94 and P = 0.85, respectively)., Conclusions: Pulmonary artery plasty, using computational fluid dynamics based on virtual surgery, is an efficient surgical strategy. This novel strategy can easily and successfully provide an optimal pulmonary artery plasty equivalent to that using the conventional approach, which is based on the surgeon's personal experience and judgement., (© The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Blood flow imaging is a novel technology in cardiovascular medicine and surgery. Today, two types of blood flow imaging tools are available: measurement-based flow visualization including 4D flow MRI (or 3D cine phase-contrast magnetic resonance imaging), or echocardiography flow visualization software, and computer flow simulation modeling based on computational fluid dynamics (CFD). MRI and echocardiography flow visualization provide measured blood flow but have limitations in temporal and spatial resolution, whereas CFD flow calculates the flow according to assumptions instead of flow measurement, and it has sufficiently fine resolution up to the computer memory limit, and it enables even virtual surgery when combined with computer graphics. Blood flow imaging provides profound insight into the pathophysiology of cardiovascular diseases, because it quantifies and visualizes mechanical stress on the vessel walls or heart ventricle. Wall shear stress (WSS) is a stress on the endothelial wall caused by the near wall blood flow, and it is thought to be a predictor of atherosclerosis progression in coronary or aortic diseases. Flow energy loss (EL) is the loss of blood flow energy caused by viscous friction of turbulent diseased flow, and it is expected to be a predictor of ventricular workload on various heart diseases including heart valve disease, cardiomyopathy, and congenital heart diseases. Blood flow imaging can provide useful information for developing predictive medicine in cardiovascular diseases, and may lead to breakthroughs in cardiovascular surgery, especially in the decision-making process.

Computational fluid dynamics (CFD) are the gold standard in studying blood flow dynamics. However, CFD results are dependent on the boundary conditions and the computation model. The purpose of this study was to validate CFD methods using comparison with actual measurements of the blood flow vector obtained with four-dimensional (4D) flow magnetic resonance imaging (MRI). 4D Flow MRI was performed on a healthy adult and a child with double-aortic arch. The aortic lumen was segmented to visualize the blood flow. The CFD analyses were performed for the same geometries based on three turbulent models: laminar, large eddy simulation (LES), and the renormalization group k-ε model (RNG k-ε). The flow-velocity vector components, namely the wall shear stress (WSS) and flow energy loss (EL), of the MRI and CFD results were compared. The flow rate of the MRI results was underestimated in small vessels, including the neck vessels. Spiral flow in the ascending aorta caused by the left ventricular twist was observed by MRI. Secondary flow distal to the aortic arch was well realized in both CFD and MRI. The average correlation coefficients of the velocity vector components of MRI and CFD for the child were the highest for the RNG k-ε model (0.530 in ascending aorta, 0.768 in the aortic arch, 0.584 in the descending aorta). The WSS and EL values of MRI were less than half of those of CFD, but the WSS distribution patterns were quite similar. The WSS and EL estimates were higher in RNG k-ε and LES than in the laminar model because of eddy viscosity. The CFD computation realized accurate flow distal to the aortic arch, and the WSS distribution was well simulated compared to actual measurement using 4D Flow MRI. However, the helical flow was not simulated in the ascending aorta. The accuracy was enhanced by using the turbulence model, and the RNG k-ε model showed the highest correlation with 4D Flow MRI.

The use of measured data as boundary conditions renders hemodynamic simulations more patient-specific. However, synchronized acquisition of data at multiple locations is often difficult in clinical practice. This study proposes a method for resynchronizing measured data for use as boundary conditions for flow simulations using frequency analyses, and discusses the optimal cut-off frequency for differentiating cardiac and respiratory variation in hemodynamic data during resynchronization. To demonstrate the utility of the method, a Fontan circulation, which is the final palliative result with single-ventricle physiology, was used. The results suggest that it is optimal to set a cut-off frequency that gives a local minimum in the power spectrum that is slightly lower than the peak frequency of the heartbeat. Additionally, the total energy loss depended on the cut-off frequency, although the overall flow patterns appeared to be similar. The method is applicable to cardiovascular systems other than the Fontan circulation, where hemodynamic data with multifactorial fluctuations are required at various locations but simultaneous measurements are not possible.

We performed error analysis of tumor blood flow (TBF) measurement using dynamic contrast-enhanced data and model-independent deconvolution analysis, based on computer simulations. For analysis, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) consisting of gamma-variate functions using an adiabatic approximation to the tissue homogeneity model under various plasma flow (F(p)), mean capillary transit time (T(c)), permeability-surface area product (PS) and signal-to-noise ratio (SNR) values. Deconvolution analyses based on truncated singular value decomposition with a fixed threshold value (TSVD-F), with an adaptive threshold value (TSVD-A) and with the threshold value determined by generalized cross validation (TSVD-G) were used to estimate F(p) values from the simulated concentration-time curves in the VOI and AIF. First, we investigated the relationship between the optimal threshold value and SNR in TSVD-F, and then derived the equation describing the relationship between the threshold value and SNR for TSVD-A. Second, we investigated the dependences of the estimated F(p) values on T(c), PS, the total duration for data acquisition and the shape of AIF. Although TSVD-F with a threshold value of 0.025, TSVD-A with the threshold value determined by the equation derived in this study and TSVD-G could estimate the F(p) values in a similar manner, the standard deviation of the estimates was the smallest and largest for TSVD-A and TSVD-G, respectively. PS did not largely affect the estimates, while T(c) did in all methods. Increasing the total duration significantly improved the variations in the estimates in all methods. TSVD-G was most sensitive to the shape of AIF, especially when the total duration was short. In conclusion, this study will be useful for understanding the reliability and limitation of model-independent deconvolution analysis when applied to TBF measurement using an extravascular contrast agent.