Computed optical coherence microscopy of mouse brain ex vivo

Optical coherence microscopy with a split-spectrum image reconstruction method for temporal-dynamics contrast-based imaging of intracellular motility

Biomedical researchers use optical coherence microscopy (OCM) for its high resolution in real-time label-free tomographic imaging. However, OCM lacks bioactivity-related functional contrast. We developed an OCM system that can measure changes in intracellular motility (indicating cellular process states) via pixel-wise calculations of intensity fluctuations from metabolic activity of intracellular components. To reduce image noise, the source spectrum is split into five using Gaussian windows with 50% of the full bandwidth. The technique verified that F-actin fiber inhibition by Y-27632 reduces intracellular motility. This finding could be used to search for other intracellular-motility-associated therapeutic strategies for cardiovascular diseases.

Investigation of multiple scattering in space and spatial-frequency domains: with application to the analysis of aberration-diverse optical coherence tomography

Optical microscopy suffers from multiple scattering (MS), which limits the optical imaging depth into scattering media. We previously demonstrated aberration-diverse optical coherence tomography (AD-OCT) for MS suppression, based on the principle that for datasets acquired with different aberration states of the imaging beam, MS backgrounds become decorrelated while single scattering (SS) signals remain correlated, so that a simple coherent average can be used to enhance the SS signal over the MS background. Here, we propose a space/spatial-frequency domain analysis framework for the investigation of MS in OCT, and apply the framework to compare AD-OCT (using astigmatic beams) to standard Gaussian-beam OCT via experiments in scattering tissue phantoms. Utilizing this framework, we found that increasing the astigmatic magnitude produced a large drop in both MS background and SS signal, but the decay experienced by the MS background was larger than the SS signal. Accounting for the decay in both SS signal and MS background, the overall signal-to-background ratio (SBR) of AD-OCT was similar to the Gaussian control after about 10 coherent averages, when deeper line foci was positioned at the plane-of-interest and the line foci spacing was smaller than or equal to 80 µm. For an even larger line foci spacing of 160 µm, AD-OCT resulted in a lower SBR than the Gaussian-beam control. This work provides an analysis framework to gain deeper levels of understanding and insights for the future study of MS and MS suppression in both the space and spatial-frequency domains.

Closed-loop wavefront sensing and correction in the mouse brain with computed optical coherence microscopy

Optical coherence microscopy (OCM) uses interferometric detection to capture the complex optical field with high sensitivity, which enables computational wavefront retrieval using back-scattered light from the sample. Compared to a conventional wavefront sensor, aberration sensing with OCM via computational adaptive optics (CAO) leverages coherence and confocal gating to obtain signals from the focus with less cross-talk from other depths or transverse locations within the field-of-view. Here, we present an investigation of the performance of CAO-based aberration sensing in simulation, bead phantoms, and ex vivo mouse brain tissue. We demonstrate that, due to the influence of the double-pass confocal OCM imaging geometry on the shape of computed pupil functions, computational sensing of high-order aberrations can suffer from signal attenuation in certain spatial-frequency bands and shape similarity with lower order counterparts. However, by sensing and correcting only low-order aberrations (astigmatism, coma, and trefoil), we still successfully corrected tissue-induced aberrations, leading to 3× increase in OCM signal intensity at a depth of ∼0.9 mm in a freshly dissected ex vivo mouse brain.

Spectral fusing Gabor domain optical coherence microscopy based on FPGA processing

High-resolution imaging using high numerical aperture imaging optics is commonly known to cause a narrow depth of focus, which limits the depth of field in optical coherence tomography (OCT). To achieve semi-invariant high resolution in all directions, Gabor domain optical coherence microscopy (GD-OCM) combines the in-focus regions of multiple cross-sectional images that are acquired while shifting the focal plane of the objective lens. As a result, GD-OCM requires additional processes for in-focus extraction and fusion, leading to longer processing times, as compared with conventional frequency domain OCT (FD-OCT). We previously proposed a method of spectral domain Gabor fusion that has been proven to improve the processing speed of GD-OCM. To investigate the full potential of the spectral domain Gabor fusion technique, we present the implementation of the spectral domain Gabor fusion algorithm using field programmable gate arrays (FPGAs) in a spectral acquisition hardware device. All filtering processes are now performed in an acquisition device as opposed to the post-processing of the original GD-OCM, which reduces the amount of data transfer between the image acquisition device and the processing host. To clearly demonstrate the imaging performance of the implemented system, we performed GD-OCM imaging of a stack of polymeric tapes. GD-OCM imaging was performed over seven focus zones. The results showed that the processing time for linear wavenumber calibration and spectral Gabor filtering can be improved with FPGA implementation. The total processing time was improved by about 35%.

Bulk-phase-error correction for phase-sensitive signal processing of optical coherence tomography

We present a numerical phase stabilization method for phase-sensitive signal processing of optical coherence tomography (OCT). This method removes the bulk phase error caused by the axial bulk motion of the sample and the environmental perturbation during volumetric acquisition. In this method, the partial derivatives of the phase error are computed along both fast and slow scanning directions, so that the vectorial gradient field of the phase error is given. Then, the phase error is estimated from the vectorial gradient field by a newly developed line integration method; a smart integration path method. The performance of this method was evaluated by analyzing the spatial frequency spectra of en face OCT images, and it objectively shows the significant phase-error-correction ability of the method. The performance was also evaluated by observing computationally refocused en face images of ex vivo tissue samples, and it was found that the image quality was improved by the phase-error correction.