Treatment of rabbit elastase-induced aneurysm models by flow diverters: development of quantifiable indexes of device performance using digital subtraction angiography

Abstract

It has been known for more than a decade that intracranial aneurysms can be successfully treated by deploying a porous meshed tube in the parent vessel of the aneurysm. Such devices are currently called flow diverters because they promote intraneurysmal flow stasis and thrombosis by diverting blood flow away from the aneurysm sac. The objective of this study was to use angiographic data to quantify and compare the performance of flow diverters of original design in successfully occluding an experimental aneurysm model. Three different configurations of a novel flow diverter with varying porosities and pore densities were implanted in 30 rabbit elastase-induced aneurysms. Temporal variations in angiographic contrast intensity within the aneurysms were fit to a mathematical model. Optimized model parameters were supplemented by the angiographic percentage aneurysm occlusion and an angiographic measure of device flexibility to derive composite scores of performance. Angiographic quantification further suggested a parameter, which could be employed to estimate long-term aneurysm occlusion probabilities immediately after treatment. Performance scores showed that the device with a porosity of 70% and pore density of 18 pores/mm (2) performed better than devices with 65% porosity, 14 pores/mm (2), and 70% porosity, 12 pores/mm (2) with relative efficacies of 100%, 84%, and 76%, respectively. The pore density of flow diverters, rather than porosity, may thus be a critical factor modulating device efficacy. A value of the prognostic parameter of less than 30 predicted greater than 97% angiographic aneurysm occlusion over six months with a sensitivity of 73% and specificity of 82%.

Fig. 1

Overview of methods.

Fig. 2

Image of one the flow diverter configurations used in the study (at right) in comparison with a commercial intracranial stent (Neuroform™, Boston Scientific). The diameter of the flow diverter is 3.7 mm.

Fig. 3

Angiograms of an aneurysm (a) before device implantation, arrowhead shows aneurysmal sac; (b) immediately after device implantation (device II), arrows indicate proximal and distal edges of the flow diverter; (c) at follow-up (180 days). Angiographically, the aneurysm is completely occluded.

Fig. 4

Calculation of respiratory rate of the animal. a) sum of image pixels with values different than the static background template (see text), normalized; b) region enclosed by rectangle in panel (a) filtered, and c) thresholded.

Fig. 5

(a) Mean percentage occlusion of the aneurysm measured by angiography acquired at follow-up for the 3 flow diverters. (b) Mean values of arc-to-chord ratios of vessel segments after implantation of a device as a percentage of the corresponding values before device implantation. Number of cases, device I (n=10), device II (n=10), device III (n=9). Error bars represent standard error of the mean.

Fig. 6

Aneurysmal washout curves. (a) Raw washout curve; fluctuations can be observed at the respiratory period of the animal (∼0.67 s). (b) Washout curve after notch filter at the respiratory frequency (∼1.5 Hz). (c) Mathematical model fit to the normalized washout curve; optimized model parameters are listed; washout curve (dots); model-fit (solid line); convective (dotted line) and diffusive (dashed line) components of model are superposed for visualization.

Fig. 7

Indices of device efficacy obtained from the analysis of aneurysmal washout curves. (a) Mean (standard error) percentage ratios of amplitudes of the convective (ρ_conv, dashed line) and diffusive (ρ_diff, solid line) components immediately after device implantation as a percentage of the corresponding values before device implantation. * The ρ_conv^POST% value for device II was significantly lower than that for device I (p = 0.016). (b) & (c) Corresponding mean (standard error) values for the diffusive time constant and washout curve amplitude, respectively. Number of samples; device I (n=9), device II (n=10), device III (n=8).

Fig. 8

Goodness of fit of the mathematical model to the washout curves obtained before device implantation assessed by plotting all the data points against the corresponding model-fits. Washout curve data versus model-fits (dots); line of identity (solid line); 95% confidence intervals (dashed lines).