Multi-focus averaging for multiple scattering suppression in optical coherence tomography

Multiple scattering suppression for in vivo optical coherence tomography measurement using the B-scan-wise multi-focus averaging method

We demonstrate a method that reduces the noise caused by multi-scattering (MS) photons in an in vivo optical coherence tomography image. This method combines a specially designed image acquisition (i.e., optical coherence tomography scan) scheme and subsequent complex signal processing. For the acquisition, multiple cross-sectional images (frames) are sequentially acquired while the depth position of the focus is altered for each frame by an electrically tunable lens. In the signal processing, the frames are numerically defocus-corrected, and complex averaged. Because of the inconsistency in the MS-photon trajectories among the different electrically tunable lens-induced defocus, this averaging reduces the MS signal. Unlike the previously demonstrated volume-wise multi-focus averaging method, our approach requires the sample to remain stable for only a brief period, approximately 70 ms, thus making it compatible with in vivo imaging. This method was validated using a scattering phantom and in vivo unanesthetized small fish samples, and was found to reduce MS noise even for unanesthetized in vivo measurement.

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

Polarization-artifact reduction and accuracy improvement of Jones-matrix polarization-sensitive optical coherence tomography by multi-focus-averaging based multiple scattering reduction

Polarization-sensitive optical coherence tomography (PS-OCT) is a promising biomedical imaging tool for the differentiation of various tissue properties. However, the presence of multiple-scattering (MS) signals can degrade the quantitative polarization measurement accuracy. We demonstrate a method to reduce MS signals and increase the measurement accuracy of Jones matrix PS-OCT. This method suppresses MS signals by averaging multiple Jones matrix volumes measured using different focal positions. The MS signals are decorrelated among the volumes by focus position modulation and are thus reduced by averaging. However, the single scattering signals are kept consistent among the focus-modulated volumes by computational refocusing. We validated the proposed method using a scattering phantom and a postmortem medaka fish. The results showed reduced artifacts in birefringence and degree-of-polarization uniformity measurements, particularly in deeper regions in the samples. This method offers a practical solution to mitigate MS-induced artifacts in PS-OCT imaging and improves quantitative polarization measurement accuracy.

30 Years of Optical Coherence Tomography: introduction to the feature issue

The guest editors introduce a feature issue commemorating the 30th anniversary of Optical Coherence Tomography.