Probabilistic speckle parametric imaging in optical coherence tomography

Bhaskara Rao Chintada; Sebastian Ruiz Lopera; Brett Bouma; Nestor Uribe-Patarroyo

doi:10.1117/12.3005661

4 April 2024 Probabilistic speckle parametric imaging in optical coherence tomography

Bhaskara Rao Chintada, Sebastian Ruiz Lopera, Brett Bouma, Nestor Uribe-Patarroyo

Proceedings Volume PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; PC128300N (2024) https://doi.org/10.1117/12.3005661
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

OCT speckle carries information on sub-cellular tissue structures, and speckle statistics have been shown to be potential biomarkers in tissue characterization for disease detection and monitoring. Current methods for estimating speckle parameters use simple methods in which speckle statistics are determined inside a fixed kernel, which makes them unsuitable in heterogeneous tissue and have a clear trade-off between accuracy and spatial resolution. These limitations make them unsuitable for automatically detecting spatially-resolved differences in cellular microstructure that occurs in a diseased tissue. To address this unmet need, we have developed an algorithm based on a probabilistic approach to automatically select kernels consisting of pixels that have a high probability of sharing the same speckle probability density function and use them to estimate spatially-resolved speckle parameters using likelihood-based estimation. Our proposed method enables new capabilities in producing speckle parametric images, providing information on spatial variability of speckle distribution throughout OCT volumes and additional information to structural OCT imaging.

Conference Presentation

Citation Download Citation

Bhaskara Rao Chintada, Sebastian Ruiz Lopera, Brett Bouma, and Nestor Uribe-Patarroyo "Probabilistic speckle parametric imaging in optical coherence tomography", Proc. SPIE PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, PC128300N (4 April 2024); https://doi.org/10.1117/12.3005661

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