Graphics processing unit accelerated one‐dimensional blood flow computation in the human arterial tree

Itu, Lucian Mihai; Sharma, Puneet; Kamen, Ali; Suciu, Constantin; Comanicìu, Dorin

doi:10.1002/cnm.2585

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…For the FFR CFD algorithm, a hybrid reduced-order for fast flow computation was used (7,8). Blood flow during a hyperemic state was computed from the geometric coronary artery model along with patient-specific boundary conditions.…”

Section: Assessment Of the Computational Ffr Results Of The Ffr Cfd Amentioning

confidence: 99%

Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Tesche¹,

Cecco²,

Baumann³

et al. 2018

Radiology

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191

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Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFR) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFR)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFR and FFR. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFR showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFR resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFR and 0.89 for FFR showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFR (AUC, 0.91) and FFR (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFR was significantly shorter compared with that of FFR (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFR algorithm performs equally in detecting lesion-specific ischemia when compared with the FFR approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.

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Section: Assessment Of the Computational Ffr Results Of The Ffr Cfd Amentioning

confidence: 99%

Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Tesche¹,

Cecco²,

Baumann³

et al. 2018

Radiology

Self Cite

191

View full text Add to dashboard Cite

show abstract

“…Hence, we conclude that the proposed framework is well suited for a clinical decision setting, where short runtimes are crucial. If required, the computational time can be further reduced by employing modern graphics processing units for massively parallelized computations with a potential speed-up of approximately a factor of 4 [32].…”

Section: Discussionmentioning

confidence: 99%

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

et al. 2017

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We introduce a parameter estimation framework for automatically and robustly personalizing aortic haemodynamic computations from four-dimensional magnetic resonance imaging data. The framework is based on a reduced-order multiscale fluid-structure interaction blood flow model, and on two calibration procedures. First, Windkessel parameters of the outlet boundary conditions are personalized by solving a system of nonlinear equations. Second, the regional mechanical wall properties of the aorta are personalized by employing a nonlinear least-squares minimization method. The two calibration procedures are run sequentially and iteratively until both procedures have converged. The parameter estimation framework was successfully evaluated on 15 datasets from patients with aortic valve disease. On average, only 1.27 ± 0.96 and 7.07 ± 1.44 iterations were required to personalize the outlet boundary conditions and the regional mechanical wall properties, respectively. Overall, the computational model was in close agreement with the clinical measurements used as objectives (pressures, flow rates, cross-sectional areas), with a maximum error of less than 1%. Given its level of automation, robustness and the short execution time (6.2 ± 1.2 min on a standard hardware configuration), the framework is potentially well suited for a clinical setting.

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“…(16). In the same way, it is possible to assign the boundary condition to the exit node of the domain.…”

Section: Boundary Conditionsmentioning

confidence: 98%

“…Although a large number of works on blood circulation have been published in the last 30 years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], the focus of these works has been limited to isothermal flow. However, understanding energy transport in circulatory systems is vital for applications such as hypothermia and hyperthermia [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Robust Finite Element Modeling Approach to Conjugate Heat Transfer in Flexible Elastic Tubes and Tube Networks

Coccarelli

Nithiarasu

2014

Numerical Heat Transfer, Part A: Applications

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In this work, heat transfer between fluid flow in elastic tubes and external environment is modeled using a robust finite element approach. The transport of energy is coupled to fluid flow that is linked to the pressure and cross-sectional area variations of the tube. The novel model developed is applied to flow and heat transfer in elastic tubes with different geometric and material properties. The effects of reflections due to discontinuities and bifurcations in the tubes are also investigated. To determine the heat transport by conduction in the elastic walls, a radial heat conduction model is also incorporated. The coupled flow equations are solved using the locally conservative Galerkin finite element method, which provides an explicit element-wise conservation of fluxes. Several simulations are performed for different parametric variations to understand the relevant aspects of heat transfer in flexible elastic tubes. The results show that small temperature fluctuations are possible, inline with the pulsatile flow boundary conditions. It is also observed that increased flexibility of tubes leads to better heat transfer between the fluid and the wall. The results clearly indicate that any flow reflections also increase the heat transfer between the fluid and the wall.

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Graphics processing unit accelerated one‐dimensional blood flow computation in the human arterial tree

Cited by 9 publications

References 51 publications

Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

A Robust Finite Element Modeling Approach to Conjugate Heat Transfer in Flexible Elastic Tubes and Tube Networks

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