4.6 Article

Image-Based Mechanical Analysis of Stent Deformation: Concept and Exemplary Implementation for Aortic Valve Stents

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2013.2273496

关键词

Biomedical image processing; biomedical imaging; biomechanical simulation; finite element analysis; implants

资金

  1. Swiss National Science Foundation [CR32I3_135044]
  2. Swiss Heart Foundation
  3. Baden-Wurttemberg Stipendium
  4. Swiss National Science Foundation (SNF) [CR32I3_135044] Funding Source: Swiss National Science Foundation (SNF)

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An approach for extracting the radial force load on an implanted stent from medical images is proposed. To exemplify the approach, a system is presented which computes a radial force estimation from computer tomography images acquired from patients who underwent transcatheter aortic valve implantation (TAVI). The deformed shape of the implanted valve prosthesis' Nitinol frame is extracted from the images. A set of displacement vectors is computed that parameterizes the observed deformation. An iterative relaxation algorithm is employed to adapt the information extracted from the images to a finite-element model of the stent, and the radial components of the interaction forces between the stent and the tissue are extracted. For the evaluation of the method, tests were run using the clinical data from 21 patients. Stent modeling and extraction of the radial forces were successful in 18 cases. Synthetic test cases were generated, in addition, for assessing the sensitivity to the measurement errors. In a sensitivity analysis, the geometric error of the stent reconstruction was below 0.3 mm, which is below the image resolution. The distribution of the radial forces was qualitatively and quantitatively reasonable. An uncertainty remains in the quantitative evaluation of the radial forces due to the uncertainty in defining a radial direction on the deformed stent. With our approach, the mechanical situation of TAVI stents after the implantation can be studied in vivo, which may help to understand the mechanisms that lead to the complications and improve stent design.

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