Purpose: To evaluate in an in vitro model the feasibility and accuracy of Roentgen stereophotogrammetric analysis (RSA) versus computed tomography (CT) for the ability to detect stent-graft migration.
Methods: An aortic model was constructed from a 22-mm-diameter Plexiglas tube with 6-mm polytetrafluoroethylene inlays to mimic the renal arteries. Six tantalum markers were placed in the wall of the aortic tube proximal to the renal arteries. Another 6 markers were added to a Gianturco stent, which was cast in Plexiglas and placed inside the aorta and fixed to a micromanipulator to precisely control displacement of the stent along the longitudinal axis. Sixteen migrations were analyzed with RSA software and compared to the micromanipulator. Thirty-two migrations were measured by 3 observers from CT images acquired with 16x0.5-mm beam collimation and reconstructed with a 0.5-mm slice thickness and a 0.4-mm reconstruction interval. Measurements were made with Vitrea postprocessing software using a standard clinical protocol and central lumen line reconstruction. Results of CT were also compared to the micromanipulator.
Results: The mean RSA measurement error compared to the micromanipulator was 0.002+/-0.044 mm, and the maximum error was 0.10 mm. There was no statistically significant interobserver variability for CT (p=0.17). The pooled mean (maximum) measurement error of CT was 0.14+/-0.29 (1.00) mm, which was significantly different from the RSA measurement error (p<0.0001).
Conclusion: Detection of endograft migration by RSA is feasible and was significantly more accurate than CT in this nonpulsatile in vitro model.
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http://dx.doi.org/10.1583/06-1842MR.1 | DOI Listing |
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Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time series modeling, and system identification. Echo state networks (ESNs), a type of reservoir computer, mirror neural networks but simplify training. They apply fixed, random linear transformations to the internal state, followed by nonlinear changes.
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