Biomedical optics express最新文献

The change in ocular wavefront aberrations with visual angle determines the isoplanatic patch, defined as the largest field of view over which diffraction-limited retinal imaging can be achieved. Here, we study how the isoplanatic patch at the foveal center varies across 32 schematic eyes, each individualized with optical biometry estimates of corneal and crystalline lens surface topography, assuming a homogeneous refractive index for the crystalline lens. The foveal isoplanatic patches were calculated using real ray tracing through 2, 4, 6 and 8 mm pupil diameters for wavelengths of 400-1200 nm, simulating five adaptive optics (AO) strategies. Three of these strategies, used in flood illumination, point-scanning, and line-scanning ophthalmoscopes, apply the same wavefront correction across the entire field of view, resulting in almost identical isoplanatic patches. Two time-division multiplexing (TDM) strategies are proposed to increase the isoplanatic patch of AO scanning ophthalmoscopes through field-varying wavefront correction. Results revealed substantial variation in isoplanatic patch size across eyes (40-500%), indicating that the field of view in AO ophthalmoscopes should be adjusted for each eye. The median isoplanatic patch size decreases with increasing pupil diameter, coarsely following a power law. No statistically significant correlations were found between isoplanatic patch size and axial length. The foveal isoplanatic patch increases linearly with wavelength, primarily due to its wavelength-dependent definition (wavefront root-mean-squared, RMS <λ/14), rather than aberration chromatism. Additionally, ray tracing reveals that in strongly ametropic eyes, induced aberrations can result in wavefront RMS errors as large as λ/3 for an 8-mm pupil, with implications for wavefront sensing, open-loop ophthalmic AO, spectacle prescription and refractive surgery.

The process of obtaining images of capillary vessels in the human eye's fundus using Doppler holography encounters difficulties due to ocular aberrations. To enhance the accuracy of these images, it is advantageous to apply an adaptive aberration correction technique. This study focuses on numerical Shack-Hartmann, which employs sub-pupil correlation as the wavefront sensing method. Application of this technique to Doppler holography encounters unique challenges due to the holographic detection properties. A detailed comparative analysis of the regularization technique against direct gradient integration in the estimation of aberrations is made. Two different reference images for the measurement of image shifts across subapertures are considered. The comparison reveals that direct gradient integration exhibits greater effectiveness in correcting asymmetrical aberrations.

Optical microscopy techniques have become essential tools for studying normal and pathological biological systems. However, in many situations, image quality deteriorates rapidly in the field of view due to optical aberrations and scattering induced by thick tissues. To compensate for these aberrations and restore the microscope's image quality, adaptive optics (AO) techniques have been proposed for the past 15 years. A key parameter for the AO implementation lies in the limited isoplanatic dimension over which the image quality remains uniform. Here, we propose a method for measuring this dimension and deducing the anisoplanatism and intensity transmission of the samples. We apply this approach to fixed slices of mouse cortices as a function of their thickness. We find a typical mid-maximum width of 20 µm for the isoplanatic spot, which is independent of sample thickness.

Functional near-infrared spectroscopy (fNIRS) technology has been steadily advancing since the first measurements of human brain activity over 30 years ago. Initially, efforts were focused on increasing the channel count of fNIRS systems and then to moving from sparse to high density arrays of sources and detectors, enhancing spatial resolution through overlapping measurements. Over the last ten years, there have been rapid developments in wearable fNIRS systems that place the light sources and detectors on the head as opposed to the original approach of using fiber optics to deliver the light between the hardware and the head. The miniaturization of the electronics and increased computational power continues to permit impressive advances in wearable fNIRS systems. Here we detail our design for a wearable fNIRS system that covers the whole head of an adult human with a high-density array of 56 sources and up to 192 detectors. We provide characterization of the system showing that its performance is among the best in published systems. Additionally, we provide demonstrative images of brain activation during a ball squeezing task. We have released the hardware design to the public, with the hope that the community will build upon our foundational work and drive further advancements.

Many clearing methods achieve high transparency by removing lipid components from tissues, which damages microstructure and limits their application in lipid research. As for methods which preserve lipid, it is difficult to balance transparency, fluorescence preservation and clearing speed. In this study, we propose a rapid water-based clearing method that is fluorescence-friendly and preserves lipid components. FLUID allows for preservation of endogenous fluorescence over 60 days. It shows negligible tissue distortion and is compatible with various types of fluorescent labeling and tissue staining methods. High quality imaging of human brain tissue and compatibility with pathological staining demonstrated the potential of our method for three-dimensional (3D) biopsy and clinical pathological diagnosis.

Skin microvasculature is essential for cardiovascular health and thermoregulation in humans, yet its imaging and analysis pose significant challenges. Established methods, such as speckle decorrelation applied to optical coherence tomography (OCT) B-scans for OCT-angiography (OCTA), often require a high number of B-scans, leading to long acquisition times that are prone to motion artifacts. In our study, we propose a novel approach integrating a deep learning algorithm within our OCTA processing. By integrating a convolutional neural network with a squeeze-and-excitation block, we address these challenges in microvascular imaging. Our method enhances accuracy and reduces measurement time by efficiently utilizing local information. The Squeeze-and-Excitation block further improves stability and accuracy by dynamically recalibrating features, highlighting the advantages of deep learning in this domain.