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Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy

Traction Force Microscopy (TFM) is a widespread method used to recover cellular tractions from the deformation that they cause in their surrounding substrate. Particle Image Velocimetry (PIV) is commonly used to quantify the substrate’s deformations, due to its simplicity and efficiency. However, PI...

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Autores principales: Jorge-Peñas, Alvaro, Izquierdo-Alvarez, Alicia, Aguilar-Cuenca, Rocio, Vicente-Manzanares, Miguel, Garcia-Aznar, José Manuel, Van Oosterwyck, Hans, de-Juan-Pardo, Elena M., Ortiz-de-Solorzano, Carlos, Muñoz-Barrutia, Arrate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671587/
https://www.ncbi.nlm.nih.gov/pubmed/26641883
http://dx.doi.org/10.1371/journal.pone.0144184
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author Jorge-Peñas, Alvaro
Izquierdo-Alvarez, Alicia
Aguilar-Cuenca, Rocio
Vicente-Manzanares, Miguel
Garcia-Aznar, José Manuel
Van Oosterwyck, Hans
de-Juan-Pardo, Elena M.
Ortiz-de-Solorzano, Carlos
Muñoz-Barrutia, Arrate
author_facet Jorge-Peñas, Alvaro
Izquierdo-Alvarez, Alicia
Aguilar-Cuenca, Rocio
Vicente-Manzanares, Miguel
Garcia-Aznar, José Manuel
Van Oosterwyck, Hans
de-Juan-Pardo, Elena M.
Ortiz-de-Solorzano, Carlos
Muñoz-Barrutia, Arrate
author_sort Jorge-Peñas, Alvaro
collection PubMed
description Traction Force Microscopy (TFM) is a widespread method used to recover cellular tractions from the deformation that they cause in their surrounding substrate. Particle Image Velocimetry (PIV) is commonly used to quantify the substrate’s deformations, due to its simplicity and efficiency. However, PIV relies on a block-matching scheme that easily underestimates the deformations. This is especially relevant in the case of large, locally non-uniform deformations as those usually found in the vicinity of a cell’s adhesions to the substrate. To overcome these limitations, we formulate the calculation of the deformation of the substrate in TFM as a non-rigid image registration process that warps the image of the unstressed material to match the image of the stressed one. In particular, we propose to use a B-spline -based Free Form Deformation (FFD) algorithm that uses a connected deformable mesh to model a wide range of flexible deformations caused by cellular tractions. Our FFD approach is validated in 3D fields using synthetic (simulated) data as well as with experimental data obtained using isolated endothelial cells lying on a deformable, polyacrylamide substrate. Our results show that FFD outperforms PIV providing a deformation field that allows a better recovery of the magnitude and orientation of tractions. Together, these results demonstrate the added value of the FFD algorithm for improving the accuracy of traction recovery.
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spelling pubmed-46715872015-12-10 Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy Jorge-Peñas, Alvaro Izquierdo-Alvarez, Alicia Aguilar-Cuenca, Rocio Vicente-Manzanares, Miguel Garcia-Aznar, José Manuel Van Oosterwyck, Hans de-Juan-Pardo, Elena M. Ortiz-de-Solorzano, Carlos Muñoz-Barrutia, Arrate PLoS One Research Article Traction Force Microscopy (TFM) is a widespread method used to recover cellular tractions from the deformation that they cause in their surrounding substrate. Particle Image Velocimetry (PIV) is commonly used to quantify the substrate’s deformations, due to its simplicity and efficiency. However, PIV relies on a block-matching scheme that easily underestimates the deformations. This is especially relevant in the case of large, locally non-uniform deformations as those usually found in the vicinity of a cell’s adhesions to the substrate. To overcome these limitations, we formulate the calculation of the deformation of the substrate in TFM as a non-rigid image registration process that warps the image of the unstressed material to match the image of the stressed one. In particular, we propose to use a B-spline -based Free Form Deformation (FFD) algorithm that uses a connected deformable mesh to model a wide range of flexible deformations caused by cellular tractions. Our FFD approach is validated in 3D fields using synthetic (simulated) data as well as with experimental data obtained using isolated endothelial cells lying on a deformable, polyacrylamide substrate. Our results show that FFD outperforms PIV providing a deformation field that allows a better recovery of the magnitude and orientation of tractions. Together, these results demonstrate the added value of the FFD algorithm for improving the accuracy of traction recovery. Public Library of Science 2015-12-07 /pmc/articles/PMC4671587/ /pubmed/26641883 http://dx.doi.org/10.1371/journal.pone.0144184 Text en © 2015 Jorge-Peñas et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Jorge-Peñas, Alvaro
Izquierdo-Alvarez, Alicia
Aguilar-Cuenca, Rocio
Vicente-Manzanares, Miguel
Garcia-Aznar, José Manuel
Van Oosterwyck, Hans
de-Juan-Pardo, Elena M.
Ortiz-de-Solorzano, Carlos
Muñoz-Barrutia, Arrate
Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy
title Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy
title_full Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy
title_fullStr Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy
title_full_unstemmed Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy
title_short Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy
title_sort free form deformation–based image registration improves accuracy of traction force microscopy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671587/
https://www.ncbi.nlm.nih.gov/pubmed/26641883
http://dx.doi.org/10.1371/journal.pone.0144184
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