Open AccessEnergy-guided learning approach to compressive FD-OCT
Author(s) -
Shimon Schwartz,
Chenyi Liu,
Alexander Wong,
David A. Clausi,
Paul Fieguth,
Kostadinka Bizheva
Publication year - 2013
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.21.000329
Subject(s) - compressed sensing , optical coherence tomography , computer science , sampling (signal processing) , energy (signal processing) , iterative reconstruction , image quality , data acquisition , artificial intelligence , computer vision , optics , filter (signal processing) , physics , mathematics , image (mathematics) , statistics , operating system
High quality, large size volumetric imaging of biological tissue with optical coherence tomography (OCT) requires large number and high density of scans, which results in large data acquisition volume. This may lead to corruption of the data with motion artifacts related to natural motion of biological tissue, and could potentially cause conflicts with the maximum permissible exposure of biological tissue to optical radiation. Therefore, OCT can benefit greatly from different approaches to sparse or compressive sampling of the data where the signal is recovered from its sub-Nyquist measurements. In this paper, a new energy-guided compressive sensing approach is proposed for improving the quality of images acquired with Fourier domain OCT (FD-OCT) and reconstructed from sparse data sets. The proposed algorithm learns an optimized sampling probability density function based on the energy distribution of the training data set, which is then used for sparse sampling instead of the commonly used uniformly random sampling. It was demonstrated that the proposed energy-guided learning approach to compressive FD-OCT of retina images requires 45% fewer samples in comparison with the conventional uniform compressive sensing (CS) approach while achieving similar reconstruction performance. This novel approach to sparse sampling has the potential to significantly reduce data acquisition while maintaining image quality.