Validation of Blood Flow Simulations
in Intracranial Aneurysms
Yue Yu
Brown University
Objective
–By comparing the concentration field (calculated in
vitro with patient-specific arterial geometry) and the CT
images (from in vivo dye injection), we aim to validate
our spectral element simulations.
–The complex geometry of aneurysms may cause
instabilities in velocity field (Lighthill)*. We will
investigate if the concentration field can also reveal
this phenomenon.
*H. Baek, M. V. Jayaraman, P.D. Richardson, and G. E. Karniadakis. Flow instability and wall
shear stress variation in intracranial aneurysms. J Roy Soc Interface, 7:967-988, 2010.
Dye Injection in Intracranial Trees
• Dye can be used to mark and visualize particular
regions of flow or individual fluid streamlines.
• The contrast agent is iodixanol. The change in signal
intensity should be proportional to its concentration.
Aneurysm
Right Internal
Carotid Artery
(RICA)
Data for Reconstructing Arterial Geometry
1.With contrast
2.Without contrast
3.Subtract 1 from 2
Slice set 28
Slice set 128
*This set of data are static CT images taken from 173 different angles, and they
will be used for structural reconstruction
Angiograms of Dye Injection (Data for Comparison)
After subtracting the static background from the angiograms
(as in the last slice):
T=0.22 (sec)
T=0.72 (sec)
T=1.22 (sec)
T=1.72 (sec)
*The frame rate is 2 images per second, and the injection of contrast begins at
T=0.0 and ends at T=1.0. The idea is to simulate the contrast concentration
numerically and compare it with these dye images.
Steps to the Goal
Construct arterial geometry from the patient-specific CT data
Tools: Matlab (segmentation), Amira (smoothing), Gridgen
(generating mesh)
2 weeks
Simulate the concentration of contrast for blood flow in the given
geometry.
Tools: Nektar (High order finite element method solver)
<1 week
Compare the results with clinical angiograms both qualitatively
and quantitatively.
Tools: Matlab (registration)
2 weeks
Steps to the Goal: Segmentation
• Get signal density information for every voxels with filtered
back-projection.
• Setting appropriate threshold for each 2D slice manually
(because the sizes of arteries vary a lot, the threshold
might vary slice by slice), then reconstruct the 3D arterial
geometry by interpolating between these slices.
Steps to the Goal: Registration
• For every iteration of the registration algorithm a 3D rigidbody geometric transform is applied to the CT volume to
produce a change in the 3D position of the arteries. The
3D volume is then reduced to a 2D digitally reconstructed
radiograph (DRR) by summing the voxel values of the
transformed CT volume in the z direction.
• Assume pixel values of the filtered DRR are denoted by Ii
and pixel values of the filtered fluoroscopy frame are
denoted by Ri, by minimizing the objective function
where
and
is the histogram bin which includes Ri.
References
• Juan R. Cebral, Alessandro Radaelli, Alejandro Frangi, and
Christopher M. Putman, Qualitative Comparison of Intra-aneurysmal
Flow Structures Determined from Conventional and Virtual
Angiograms, Medical Imaging 2007: Physiology, Function, and
Structure from Medical Images.
• Matthew D. Ford, Gordan R. Stuhne, Hristo N. Nikolov, Damiaan F.
Habets, Stephen P. Lownie, David W. Holdsworth, and David A.
Steinman, Virtual Angiography for Visualization and Validation of
Computational Models of Aneurysm Hemodynamics, IEEE
Transactions on Medical Imaging, Vol. 25, No. 12, 2005.
• M. Pickering, A. Muhit, J. Scarvell, and P. Smith, A new multimodal
similarity measure for fast gradient-based 2D-3D image registration, in
Proc. IEEE Int. Conf. on Engineering in Medicine and Biology
(EMBC), Minneapolis, USA, 2009, pp. 5821-5824.
Thank you!