Atherosclerotic Plaque Segmentation Based on Strain Gradients: A Theoretical Framework

Álvaro T. Latorre (), Miguel A. Martínez, Myriam Cilla, Jacques Ohayon and Estefanía Peña ()
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Álvaro T. Latorre: Aragón Institute for Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain
Miguel A. Martínez: Aragón Institute for Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain
Myriam Cilla: Aragón Institute for Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain
Jacques Ohayon: Laboratory TIMC-IMAG, CNRS UMR 5525, Grenoble-Alpes University, 38400 Grenoble, France
Estefanía Peña: Aragón Institute for Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain

Mathematics, 2022, vol. 10, issue 21, 1-20

Abstract: Background: Atherosclerotic plaque detection is a clinical and technological problem that has been approached by different studies. Nowadays, intravascular ultrasound (IVUS) is the standard used to capture images of the coronary walls and to detect plaques. However, IVUS images are difficult to segment, which complicates obtaining geometric measurements of the plaque. Objective: IVUS, in combination with new techniques, allows estimation of strains in the coronary section. In this study, we have proposed the use of estimated strains to develop a methodology for plaque segmentation. Methods: The process is based on the representation of strain gradients and the combination of the Watershed and Gradient Vector Flow algorithms. Since it is a theoretical framework, the methodology was tested with idealized and real IVUS geometries. Results: We achieved measurements of the lipid area and fibrous cap thickness, which are essential clinical information, with promising results. The success of the segmentation depends on the plaque geometry and the strain gradient variable (SGV) that was selected. However, there are some SGV combinations that yield good results regardless of plaque geometry such as ▽ ε v M i s e s + ▽ ε r θ , ▽ ε y y + ▽ ε r r or ▽ ε m i n + ▽ ε T r e s c a . These combinations of SGVs achieve good segmentations, with an accuracy between 97.10% and 94.39% in the best pairs. Conclusions: The new methodology provides fast segmentation from different strain variables, without an optimization step.

Keywords: atherosclerosis; fibrous cap thickness; finite element model; intravascular ultrasound; segmentation method; strain gradient (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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