Judith Zimmermann1,2,3, Kathrin Bäumler1, Michael Loecher1,3, Alison Marsden4,5, and Daniel Ennis1,3,5
1Radiology, Stanford University, Stanford, CA, United States, 2Computer Science, Technical University of Munich, Munich, Germany, 3Radiology, Veterans Administration Health Care System, Palo Alto, CA, United States, 4Pediatrics, Stanford University, Stanford, CA, United States, 5Cardiovascular Institute, Stanford, CA, United States
Synopsis
A
detailed understanding of flow dynamics in the aorta is of clinical interest
and enabled by 4D-flow MRI. This work presents an in vitro flow circuit that embeds a subject-specific 3D-printed compliant
aorta model to demonstrate feasibility while also exploring the impact of
vessel wall characteristics (thickness, stiffness) and heart rate on
quantitative flow dynamics. The experimental setup will be a valuable tool for
assessing 4D flow MRI sampling requirements, generating high-quality ground
truth data for CFD and FSI validation, as well as studying flow dynamics in
different vascular pathologies.
Introduction
Pulse
wave velocity (PWV) is recognized as a metric of aortic wall stiffness and may
inform the diagnosis and disease management of several vascular pathologies.1-3 4D-flow MRI outputs velocity maps from which PWV can
be estimated.4,5 The sensitivity of PWV to true changes in stiffness
and wall thickening, however, remains uncertain and is impacted by the 4D-flow
image quality which – in the clinical setting – has modest spatiotemporal
resolution and signal-to-noise-ratio (SNR). This work utilizes a novel approach
to subject-specific 3D-printing of compliant thoracic aorta models. The objectives of this work were: (1) to
demonstrate feasibility of an in vitro flow circuit setup using a subject specific
healthy thoracic aorta model; and (2) to analyze the impact of vessel wall
characteristics (thickness, stiffness) and heart rate (HR) variability on
quantitative flow dynamics.Methods
Thoracic aorta models: A 4D-flow MRI dataset of a healthy subject was used to
generate a polygon mesh model of the thoracic aorta wall (Fig. 1A). The model
was printed (scale 1:1) using 3D additive manufacturing (J735 PolyJet,
Stratasys) and a compliant photopolymer material (Agilus, Stratasys) in three
configurations (Fig. 1B):
- M1_1.6_soft
with 1.6 mm wall thickness and Young’s modulus = 0.7 MPa (Agilus30);
- M2_2.0_soft
with 2 mm wall thickness and Young’s modulus = 0.7 MPa (Agilus30);
- M3_2.0_hardened
with 2 mm wall thickness and Young’s modulus = 3.5 MPa (Agilus60).
M1_1.6_soft and M2_2.0_soft
closely resemble the healthy aortic stiffness; M3_2.0_hardened resembles
extreme aortic hardening.
4D-flow: Each model was
embedded into a pulsatile flow circuit (Fig. 2, animated) that includes an MRI-compatible
and programmable pump system (CardioFlow 5000, Shelley Medical). Six liters of glycerol-water
fluid (ratio = 2/3) infused with T1-shortening contrast agent (Ferumoxytol,
0.75 ml per liter of fluid) was used to mimic blood viscosity with increased
SNR. Six 4D-flow MRI datasets were acquired with: TE/TR [ms] = 2.6/5.2; flip
angle [degree] = 12; spatial resolution [mm3] = 2.5x2.5x2.5; FOV [mm2]
= 320x320; matrix = 128x128; num. slices = 36; temporal resolution [ms] = 20.6;
averages = 1; VENC [cm/s] = 150; 2x GRAPPA (ref. lines = 24); and prospective external
triggering. For each model, we applied the subject-specific flow rate waveform
- derived from in vivo 4D-flow MRI
data, scaled to meet 300ml/s peak flow rate pump limit -, and acquired data with
two heart rates (both settings a total flow volume of 4.38l/min):
- HR=60/min (RR=1008ms, frames=48);
- HR=100/min (RR=594ms,
frames=28).
Analysis: Image
data was pre-processed as follows:
- image
background noise filtering to improve performance of automated analysis;
- fully-automated
phase unwrapping based on PRELUDE;6
- 3D watershed segmentation
of the aortic lumen;
- lumen centerline
detection.7
PWV was measured
as follows:
- definition of descending
aorta (DAo) centerline (distal to left subclavian artery to end of model,
length=240mm);
- computation of
flow rate curves at equidistant cross-sectional DAo planes (N=48, spacing=5 mm);
- computation of
temporal shifts for each flow curve via cross-correlation (travelling-time-for-fixed-distance
approach);
- PWV was defined
as the slope of the fitted linear regression line.
In addition, we analyzed flow rate curves and absolute velocity maps at
the model inlet and at mid DAo. Refer to Fig. 2 for landmark definition.
Results
Absolute velocity maps qualitatively
demonstrated the impact of varying wall characteristics. Compliant wall recoiling
effect was present in all models and overall higher velocities were measured
in the hardened model (Fig. 3). Measured inlet flow curves were consistent
with the programmed flow curve (though dampened) and in good agreement between all models (Fig. 4A).
For all models, peak flow rate was dampened through the inflow tubing (firm, length=3m)
from 300ml/s (programmed) to 258-265 ml/s (HR=60) and 278-284 ml/s (HR=100) at
the inlet landmark (Fig. 4B). Measured peak flow rate through the models was
further dampened at the DAo landmark, with the strongest dampening effect in
the most compliant model. PWV (Fig. 5) was lowest in M1_1.6_soft (2.22/2.56 m/s
for HR=60/100) and increased by a factor of 2.5 (HR=60) and 3.5 (HR=100) for
the hardened model.Discussion
Embedding
compliant aorta models into the described experimental setup is technically
feasible and allows acquiring high-quality 4D-flow MRI data. Measured PWV
numbers are within a plausible range when compared to prior in vivo studies.5 Results suggest that waveform dampening and PWV in
the 3D-printed thoracic aorta model are influenced by both vessel wall
thickness and stiffness. Therefore, choosing a model that best resembles in
vivo aortic wall characteristics is important, since it directly impacts both
flow dynamics and flow quantitation. In addition, changes in HR influence the
quantitation of flow dynamics. Therefore, HR variations need to be considered for
large-scale clinical studies, especially of PWV. The presented experimental
setup is also valuable for assessing 4D-flow MRI sampling requirements,
generating high-quality ground truth data for CFD/FSI validation, and for
studying flow dynamics in different vascular pathologies under controlled
conditions (e.g. atherosclerosis, dissections, aneurysms). This work is limited
by not incorporating pressure measurements to tune the setup toward optimal
boundary conditions, which will be approached next.Conclusion
This
work demonstrates the feasibility of in vitro flow measurements in
subject-specific and compliant 3D-printed aorta models and emphasizes the
importance of choosing compliant materials of appropriate stiffness and
thickness for quantitative 4D-flow studies. Acknowledgements
We
thank Frandics Chan and Dominik Fleischmann for providing the volunteer
dataset, Kyle Gifford for manufacturing the models, and Tabitha Bandy-Vizcaino
for technical support with the experimental setup. JZ receives financial
support from DAAD Germany. This project was supported, in part, by NIH R01 HL131975
to DBE.References
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