0
Research Papers

Patient-Specific Simulation of Cardiac Blood Flow From High-Resolution Computed Tomography

[+] Author and Article Information
Jonas Lantz

Department of Medical and Health Sciences,
Center for Medical Image Science and
Visualization (CMIV), Linköping University,
Linköping SE-581 83, Sweden
e-mail: jonas.lantz@liu.se

Lilian Henriksson

Center for Medical Image Science and
Visualization (CMIV), Linköping University,
Linköping SE-581 83, Sweden

Anders Persson, Tino Ebbers

Department of Medical and Health Sciences,
Center for Medical Image Science and
Visualization (CMIV), Linköping University,
Linköping SE-581 83, Sweden

Matts Karlsson

Department of Management and Engineering,
Center for Medical Image Science and
Visualization (CMIV), Linköping University,
Linköping SE-581 83, Sweden

Manuscript received December 10, 2015; final manuscript received August 19, 2016; published online November 3, 2016. Assoc. Editor: C. Alberto Figueroa.

J Biomech Eng 138(12), 121004 (Nov 03, 2016) (9 pages) Paper No: BIO-15-1633; doi: 10.1115/1.4034652 History: Received December 10, 2015; Revised August 19, 2016

Cardiac hemodynamics can be computed from medical imaging data, and results could potentially aid in cardiac diagnosis and treatment optimization. However, simulations are often based on simplified geometries, ignoring features such as papillary muscles and trabeculae due to their complex shape, limitations in image acquisitions, and challenges in computational modeling. This severely hampers the use of computational fluid dynamics in clinical practice. The overall aim of this study was to develop a novel numerical framework that incorporated these geometrical features. The model included the left atrium, ventricle, ascending aorta, and heart valves. The framework used image registration to obtain patient-specific wall motion, automatic remeshing to handle topological changes due to the complex trabeculae motion, and a fast interpolation routine to obtain intermediate meshes during the simulations. Velocity fields and residence time were evaluated, and they indicated that papillary muscles and trabeculae strongly interacted with the blood, which could not be observed in a simplified model. The framework resulted in a model with outstanding geometrical detail, demonstrating the feasibility as well as the importance of a framework that is capable of simulating blood flow in physiologically realistic hearts.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Boyd, M. T. , Seward, J. B. , Tajik, A. J. , and Edwards, W. D. , 1987, “ Frequency and Location of Prominent Left Ventricular Trabeculations at Autopsy in 474 Normal Human Hearts: Implications for Evaluation of Mural Thrombi by Two-Dimensional Echocardiography,” J. Am. Coll. Cardiol., 9(2), pp. 323–326. [CrossRef] [PubMed]
Vedula, V. , Seo, J.-H. , Lardo, A. C. , and Mittal, R. , 2015, “ Effect of Trabeculae and Papillary Muscles on the Hemodynamics of the Left Ventricle,” Theor. Comput. Fluid Dyn., 30(1–2), pp. 3–21.
Bolger, A. F. , Heiberg, E. , Karlsson, M. , Wigström, L. , Engvall, J. , Sigfridsson, A. , Ebbers, T. , Kvitting, J.-P. E. , Carlhäll, C. J. , and Wranne, B. , 2007, “ Transit of Blood Flow Through the Human Left Ventricle Mapped by Cardiovascular Magnetic Resonance,” J. Cardiovasc. Magn. Reson., 9(5), pp. 741–747. [CrossRef] [PubMed]
Carlhäll, C. J. , and Bolger, A. , 2010, “ Passing Strange: Flow in the Failing Ventricle,” Circ.: Heart Failure, 3(2), pp. 326–331. [CrossRef]
Eriksson, J. , Dyverfeldt, P. , Engvall, J. , Bolger, A. F. , Ebbers, T. , and Carlhäll, C. J. , 2011, “ Quantification of Presystolic Blood Flow Organization and Energetics in the Human Left Ventricle,” Am. J. Physiol.: Heart Circ. Physiol., 300(6), pp. H2135–H2141. [CrossRef] [PubMed]
Markl, M. , Kilner, P. J. , and Ebbers, T. , 2011, “ Comprehensive 4D Velocity Mapping of the Heart and Great Vessels by Cardiovascular Magnetic Resonance,” J. Cardiovasc. Magn. Reson., 13(1), p. 7. [CrossRef] [PubMed]
Thavendiranathan, P. , Liu, S. , Datta, S. , Walls, M. , Nitinunu, A. , Van Houten, T. , Tomson, N. A. , Vidmar, L. , Georgescu, B. , Wang, Y. , Srinivasan, S. , De Michelis, N. , Raman, S. V. , Ryan, T. , and Vannan, M. A. , 2012, “ Automated Quantification of Mitral Inflow and Aortic Outflow Stroke Volumes by Three-Dimensional Real-Time Volume Color-Flow Doppler Transthoracic Echocardiography: Comparison With Pulsed-Wave Doppler and Cardiac Magnetic Resonance Imaging,” J. Am. Soc. Echocardiography, 25(1), pp. 56–65. [CrossRef]
Konstantopoulou, A. , Tsikrikas, S. , Asvestas, D. , Korantzopoulos, P. , and Letsas, K. P. , 2013, “ Coronary CT Angiography; Dose Reduction Strategies,” World J. Cardiol., 5(6), pp. 175–185. [CrossRef] [PubMed]
Long, Q. , Merrifield, R. , Xu, X. , Kilner, P. , Firmin, D. , and Yang, G. , 2008, “ Subject-Specific Computational Simulation of Left Ventricular Flow Based on Magnetic Resonance Imaging,” Proc. Inst. Mech. Eng., Part H, 222(4), pp. 475–485. [CrossRef]
Mihalef, V. , Ionasec, R. I. , Sharma, P. , Georgescu, B. , Voigt, I. , Suehling, M. , and Comaniciu, D. , 2011, “ Patient-Specific Modelling of Whole Heart Anatomy, Dynamics and Haemodynamics From Four-Dimensional Cardiac CT Images,” Interface Focus, 1(3), pp. 286–296. [CrossRef] [PubMed]
Domenichini, F. , Pedrizzetti, G. , and Baccani, B. , 2005, “ Three-Dimensional Filling Flow Into a Model Left Ventricle,” J. Fluid Mech., 539, pp. 179–198. [CrossRef]
Le, T. B. , and Sotiropoulos, F. , 2012, “ On the Three-Dimensional Vortical Structure of Early Diastolic Flow in a Patient-Specific Left Ventricle,” Eur. J. Mech. B/Fluids, 35, pp. 20–24. [CrossRef]
Seo, J. H. , and Mittal, R. , 2013, “ Effect of Diastolic Flow Patterns on the Function of the Left Ventricle,” Phys. Fluids, 25(11), p. 110801. [CrossRef]
Vedula, V. , George, R. , Younes, L. , and Mittal, R. , 2015, “ Hemodynamics in the Left Atrium and Its Effect on Ventricular Flow Patterns,” ASME J. Biomech. Eng., 137(11), p. 111003.
Kulp, S. , Gao, M. , Zhang, S. , Qian, Z. , Voros, S. , Metaxas, D. , and Axel, L. , 2011, “ Using High Resolution Cardiac CT Data to Model and Visualize Patient-Specific Interactions Between Trabeculae and Blood Flow,” Medical Image Computing and Computer-Assisted Intervention 14th International Conference (MICCAI 2011), Toronto, Canada, Sept. 18–22, pp. 468–475.
Heiberg, E. , Sjogren, J. , Ugander, M. , Carlsson, M. , Engblom, H. , and Arheden, H. , 2010, “ Design and Validation of Segment–Freely Available Software for Cardiovascular Image Analysis,” BMC Med. Imaging, 10(1), p. 1. [CrossRef] [PubMed]
Thirion, J.-P. , 1998, “ Image Matching as a Diffusion Process: An Analogy With Maxwell's Demons,” Med. Image Anal., 2(3), pp. 243–260. [CrossRef] [PubMed]
Vercauteren, T. , Pennec, X. , Perchant, A. , and Ayache, N. , 2009, “ Diffeomorphic Demons: Efficient Non-Parametric Image Registration,” NeuroImage, 45(1), pp. S61–S72. [CrossRef] [PubMed]
Charonko, J. J. , Kumar, R. , Stewart, K. , Little, W. C. , and Vlachos, P. P. , 2013, “ Vortices Formed on the Mitral Valve Tips Aid Normal Left Ventricular Filling,” Ann. Biomed. Eng., 41(5), pp. 1049–1061. [CrossRef] [PubMed]
Chnafa, C. , Mendez, S. , and Nicoud, F. , 2014, “ Image-Based Large-Eddy Simulation in a Realistic Left Heart,” Comput. Fluids, 94, pp. 173–187. [CrossRef]
Lantz, J. , Dyverfeldt, P. , and Ebbers, T. , 2014, “ Improving Blood Flow Simulations by Incorporating Measured Subject-Specific Wall Motion,” Cardiovasc. Eng. Technol., 5(3), pp. 261–269. [CrossRef]
Mottram, P. M. , and Marwick, T. H. , 2005, “ Assessment of Diastolic Function: What the General Cardiologist Needs to Know,” Heart (British Cardiac Society), 91(5), pp. 681–695. [CrossRef] [PubMed]
Choi, Y. J. , Vedula, V. , and Mittal, R. , 2014, “ Computational Study of the Dynamics of a Bileaflet Mechanical Heart Valve in the Mitral Position,” Ann. Biomed. Eng., 42(8), pp. 1668–1680. [CrossRef] [PubMed]
Hunt, J. C. , Wray, A. , and Moin, P. , 1988, “ Eddies, Streams, and Convergence Zones in Turbulent Flows,” Center for Turbulence Research, Stanford, CA, Report No. CTR-S88.
Zajac, J. , Eriksson, J. , Dyverfeldt, P. , Bolger, A. F. , Ebbers, T. , and Carlhäll, C. J. , 2014, “ Turbulent Kinetic Energy in Normal and Myopathic Left Ventricles,” J. Magn. Reson. Imaging, 41(4), pp. 1021–1029. [CrossRef] [PubMed]
Dahl, S. K. , Thomassen, E. , Hellevik, L. R. , and Skallerud, B. , 2012, “ Impact of Pulmonary Venous Locations on the Intra-Atrial Flow and the Mitral Valve Plane Velocity Profile,” Cardiovasc. Eng. Technol., 3(3), pp. 269–281. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Top row: The two models used in the study: a baseline model including papillary muscles and trabeculae, and a smoothed model without any geometrical details or papillary muscles in the left ventricle. Bottom row: systolic and diastolic geometries for the baseline model, highlighting the open and closed valves (AV, MV) and the papillary muscles (PM).

Grahic Jump Location
Fig. 2

Schematic figure representing the registration process and computation of displacement vectors. The image registration was applied sequentially to the time frames and the displacement field D(n, n + 1) that aligned time frame n with n + 1 was obtained. Using the perimeter of the segmentation in the first time frame as a binary mask, displacement vectors at the wall could be computed. The process was then repeated using the displaced wall as a new binary mask. For visualization purposes, the images are shown in 2D, but the image registration and displacement vectors were in 3D.

Grahic Jump Location
Fig. 3

Examples of the volumetric mesh in the LV. The motion of the papillary muscles (PM, highlighted in red) and the folding and unfolding of trabeculae at the LV wall created several different geometrical topologies during the cardiac cycle. By monitoring mesh quality, the framework was able to handle these topological changes by automatically triggering a remesh. (T = duration of cardiac cycle.)

Grahic Jump Location
Fig. 4

Flow chart describing all the steps in the method. From the acquired images, a geometry was segmented at one time frame and image registration was used to obtain the wall motion. The segmented geometry was meshed, and the wall motion was then applied, and the deformation was computed. If the mesh quality became too low, a automatic remesh was triggered. In this was meshes were obtained every 0.01 s. Then, the flow solver was started and using PCHIP interpolation in time intermediate meshes were obtained for each time step in the flow simulation.

Grahic Jump Location
Fig. 5

Flow rates at the pulmonary veins (PV), mitral valve, and aortic valve for the baseline model

Grahic Jump Location
Fig. 6

Visualization of particle traces near the papillary muscles and trabeculated structures. (a) anterior papillary muscle, (b) posterior papillary muscle, and (c) geometrical structure below the anterior papillary muscle

Grahic Jump Location
Fig. 7

Cross section through the two models, showing velocity magnitude and in-plane velocity vectors. Upper row is the baseline model, while lower is the smoothed model. Left part of the figure show the flow field during systole (0.1 and 0.2T, T = duration of cardiac cycle), while the right part shows the flow field during diastole (0.5, 0.7, and 0.9T). Notice the different color range between systole and diastole.

Grahic Jump Location
Fig. 8

Vortical structures identified by the Q-criterion at Q = 300 s−2 for the two models at five time points

Grahic Jump Location
Fig. 9

Visualization of mixing of blood using the residence time variable. At time = 0 the residence time is 0 everywhere, and then, during the first heart beat (time period T) blood with value 1 is injected from the pulmonary veins. Mixing is shown at different cardiac cycles at the end of diastole. The arrow indicates the region with no mixing and stagnant flow. Upper row: baseline model, lower row: smoothed model.

Grahic Jump Location
Fig. 10

Illustration of the residence time at the LV of the baseline model after ten simulated cardiac cycles. Highlighted areas show that some of the trabeculae have elevated residence time compared to the surrounding LV wall. Minimum, maximum, and mean ± std of the residence time at the wall are indicated in lower right panel.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In