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Using computational models to develop patient-specific treatments

Abdominal aortic aneurysm (AAA), an enlargement of the abdominal aorta by 50% or more, occurs in more than 8% of people over 65. It can lead to a fatal rupture, and is the tenth leading cause of death for men over 50. Current medical practice lacks the ability to fully assess AAA risk of rupture - particularly AAA wall stress, for which there are no reliable in vivomeasurement techniques.

Vessel segmentation. Image courtesy Carnegie Mellon University, US

An aneurysm ruptures when the stress placed on the inner wall surpasses the strength of the tissue involved. Ender Finol, director of the Vascular Biomechanics and Biofluids Laboratory at The University of Texas at San Antonio, US, has developed computational models of patient-specific AAA features. "We are developing software-based tools that can predict when an aneurysm is going to rupture. Our initial research focused on the stresses, formations, and forces these aneurysms are subject to, with the goal of being able to predict what will happen after reaching a particular threshold."

When an AAA reaches a transverse diameter of 5.5cm and an expansion rate of more than 1cm per year (the traditional standards for elective intervention), open surgery or endovascular aneurysm repair become options. However, many key parameters of these aneurysms vary widely among people. Small aneurysms with a diameter of less than 5.5cm can rupture, and larger aneurysms with a diameter more than 5.5cm can be stable for a lifetime.

Finol collaborated with Allegheny General Hospital in Pittsburgh, Pennsylvania, US, to gather imaging data of AAA patients. "We now have software to make computational models from medical images of individual patients," says Finol. "We take into account their aortic wall thickness, slice by slice, in vivo, and from that predict wall-stress distribution. No one else has done this before with this level of accuracy."

Curvature distributions for (a) small (b) unruptured (c) ruptured AAA. Image courtesy Ender Finol.

Computation, coding, and modeling take place at Pittsburgh Supercomputing Center (PSC), a joint project of the University of Pittsburgh and Carnegie Mellon University, Pittsburgh, US. Under the guidance of XSEDE consultant Anirban Jana, Finol has been able to use PSC's Blacklight and computational solid-stress modeling to demonstrate that wall stresses in AAAs are most sensitive to changes in geometry. "Anirban's input has been invaluable - not only has he helped launch our finite-element models setting boundary conditions from patient-specific profiles, but he served on the advisory committee for doctoral students involved in the modeling," notes Finol.

In research scheduled for publication next month, Finol reveals surface curvature to be a classifier of AAAs. "In our modeling, we have to specify both the material properties of the AAA - which are based on tissue samples - and the pressure involved. We found that the maximum stress variable used to predict risk of rupture seemed to be unaffected by the material variables specified in the simulation. In other words, the peak wall stress is insensitive to the mechanical properties of specific materials."

"Our code takes the medical images (the computed tomography scans), segments them, and identifies the regions of interest - where an aneurysm is located. It then creates a 3D model quantifying the geometry and identifying characteristics you cannot see with the naked eye. This shows surface curvatures and tells you how concave or convex the surface material can be before it ruptures," explains Finol. "Surgeons typically focus on the maximum diameter of the aneurysm, but our feedback will be to focus on surface curvatures instead."

Finol's work is revolutionary in that it could provide physicians with a powerful tool to determine AAA's geometry, which cannot be done simply by observing 3D images alone. "The goal is to use this modeling to assess the risk for rupture for individual patients, and ultimately to help guide decisions about surgical intervention."

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