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Phase-Resolved Optical Coherence Elastography: An Insight into Tissue Displacement Estimation.

Ana BatistaPedro SerranhoMário J SantosCarlos CorreiaJosé P DominguesCustódio LoureiroJoão CardosoSílvia BarbeiroAntónio Miguel MorgadoRui Bernardes
Published in: Sensors (Basel, Switzerland) (2023)
Robust methods to compute tissue displacements in optical coherence elastography (OCE) data are paramount, as they play a significant role in the accuracy of tissue elastic properties estimation. In this study, the accuracy of different phase estimators was evaluated on simulated OCE data, where the displacements can be accurately set, and on real data. Displacement (∆d) estimates were computed from (i) the original interferogram data (Δφori) and two phase-invariant mathematical manipulations of the interferogram: (ii) its first-order derivative (Δφd) and (iii) its integral (Δφint). We observed a dependence of the phase difference estimation accuracy on the initial depth location of the scatterer and the magnitude of the tissue displacement. However, by combining the three phase-difference estimates (Δdav), the error in phase difference estimation could be minimized. By using Δdav, the median root-mean-square error associated with displacement prediction in simulated OCE data was reduced by 85% and 70% in data with and without noise, respectively, in relation to the traditional estimate. Furthermore, a modest improvement in the minimum detectable displacement in real OCE data was also observed, particularly in data with low signal-to-noise ratios. The feasibility of using Δdav to estimate agarose phantoms' Young's modulus is illustrated.
Keyphrases
  • electronic health record
  • big data
  • computed tomography
  • high resolution
  • machine learning