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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.

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Figures

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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.)

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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.

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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).

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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.

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Fig. 5

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

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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

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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.

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Fig. 8

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

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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.

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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.

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