Challenges for Optical Flow Estimates in Elastography

In this paper, we consider visualization of displacement fields via optical flow methods in elastographic experiments consisting of a static compression of a sample. We propose an elastographic optical flow method (EOFM) which takes into account experimental constraints, such as appropriate boundary conditions, the use of speckle information, as well as the inclusion of structural information derived from knowledge of the background material. We present numerical results based on both simulated and experimental data from an elastography experiment in order to demonstrate the relevance of our proposed approach.

[1]  Eldad Haber,et al.  A Multilevel Method for Image Registration , 2005, SIAM J. Sci. Comput..

[2]  Jan Modersitzki,et al.  FAIR: Flexible Algorithms for Image Registration , 2009 .

[3]  J. Schmitt,et al.  OCT elastography: imaging microscopic deformation and strain of tissue. , 1998, Optics express.

[4]  Otmar Scherzer,et al.  Texture generation in compressional photoacoustic elastography , 2015, Photonics West - Biomedical Optics.

[5]  Javier Sánchez Pérez,et al.  Horn-Schunck Optical Flow with a Multi-Scale Strategy , 2013, Image Process. Line.

[6]  Michael J. Black,et al.  A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2013, International Journal of Computer Vision.

[7]  M M Doyley,et al.  Model-based elastography: a survey of approaches to the inverse elasticity problem , 2012, Physics in medicine and biology.

[8]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Otmar Scherzer,et al.  Lamé Parameter Estimation from Static Displacement Field Measurements in the Framework of Nonlinear Inverse Problems , 2017, SIAM J. Imaging Sci..

[10]  S J Kirkpatrick,et al.  Processing algorithms for tracking speckle shifts in optical elastography of biological tissues. , 2001, Journal of biomedical optics.

[11]  A Coarse To Fine Multiscale Approach For Linear Least Squares Optical Flow Estimation , 2000 .

[12]  Shang Wang,et al.  Optical coherence elastography for tissue characterization: a review , 2015, Journal of biophotonics.

[13]  T. Brox,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik a Survey on Variational Optic Flow Methods for Small Displacements a Survey on Variational Optic Flow Methods for Small Displacements , 2022 .

[14]  Ying Wu,et al.  Large Displacement Optical Flow from Nearest Neighbor Fields , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  D. Sampson,et al.  Optical elastography on the microscale , 2020 .

[16]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[17]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[18]  J. F. Greenleaf,et al.  Magnetic resonance elastography: Non-invasive mapping of tissue elasticity , 2001, Medical Image Anal..

[19]  Gjlles Aubert,et al.  Mathematical problems in image processing , 2001 .

[20]  Christoph Schnörr,et al.  Determining optical flow for irregular domains by minimizing quadratic functionals of a certain class , 1991, International Journal of Computer Vision.

[21]  J. Schmitt,et al.  Differential absorption imaging with optical coherence tomography , 1998 .

[22]  Otmar Scherzer,et al.  Texture Generation for Photoacoustic Elastography , 2014, Journal of Mathematical Imaging and Vision.