1. Validation of Blood Flow Simulations in Intracranial Aneurysms Yue Yu Brown University

2. 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.

3. 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. Right Internal Carotid Artery (RICA)‏ Dye Injection in Intracranial Trees Aneurysm

4. Data for Reconstructing Arterial Geometry *This set of data are static CT images taken from 173 different angles, and they will be used for structural reconstruction Slice set 128 Slice set 28 1.With contrast2.Without contrast3.Subtract 1 from 2

5. Angiograms of Dye Injection (Data for Comparison) T=0.22 (sec)‏T=0.72 (sec)‏T=1.22 (sec)‏ T=1.72 (sec) ‏ After subtracting the static background from the angiograms (as in the last slice): *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.

6. Construct arterial geometry from the patient-specific CT data Tools: Matlab (segmentation), Amira (smoothing), Gridgen (generating mesh)‏ Simulate the concentration of contrast for blood flow in the given geometry. Tools: Nektar (High order finite element method solver)‏ Steps to the Goal Compare the results with clinical angiograms both qualitatively and quantitatively. Tools: Matlab (registration)‏ 2 weeks <1 week

7. 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: Segmentation

8. Steps to the Goal: Registration For every iteration of the registration algorithm a 3D rigid- body 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.

9. 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. References Thank you!