Biomedical optics express最新文献

Raman spectroscopy, with its unique "molecular fingerprint" characteristics, is an essential tool for label-free, non-invasive biochemical analysis of cells. It provides precise information on cellular biochemical components, such as proteins, lipids, and nucleic acids by analyzing molecular vibrational modes. However, overlapping Raman spectral signals make spectral unmixing crucial for accurate quantification. Traditional unmixing methods face challenges: unsupervised algorithms yield poorly interpretable results, while supervised methods like BCA rely heavily on accurate reference spectra and are sensitive to environmental changes (e.g., pH, temperature, excitation wavelength), causing spectral distortion and reducing quantitative reliability. This study addresses these challenges by introducing a parameterized Voigt function into the linear spectral mixing model for element spectrum compensation, using iterative least-squares optimization for adaptive unmixing and quantitative analysis. Simulations show that the Voigt-compensated unmixing algorithm improves spectral fitting accuracy and robustness. Applied to Raman spectra from Hela cell apoptosis and iPSCs differentiation, the algorithm accurately tracks biochemical molecular changes, proving its applicability in cellular Raman spectral analysis and a precise, reliable, and versatile tool for quantitative biochemical analysis.

Confocal microscopy is an important bio-imaging technique that increases the resolution using a spatial pinhole to block out-of-focus light. In theory, the maximum resolution and optical sectioning are obtained when the detection pinhole is fully closed, but this is prevented by the dramatic decrease in the signal reaching the detector. In image scanning microscopy (ISM) this limitation is overcome by the use of an array of point detectors rather than a single detector. This, combined with pixel reassignment, increases the resolution of $\sqrt{2}$ over widefield imaging, with relatively little modification to the existing hardware of a laser-scanning microscope. Separation of photons by lifetime tuning (SPLIT) is a super-resolution technique, based on the phasor analysis of the fluorescent signal into an additional channel of the microscope. Here, we use SPLIT to analyze the information encoded within the array detectors distance for improving the resolution of ISM (SPLIT-ISM). We find that the lateral resolution can be increased of an additional 1.3 × with respect to the pixel-reassigned image with a concomitant increase in optical sectioning. We applied the SPLIT-ISM technique on biological images acquired by two currently available ISM systems: the Genoa Instruments PRISM and the Zeiss Airyscan. We evaluate the improvement provided by SPLIT-ISM through the QuICS algorithm, a quantitative tool based on image correlation spectroscopy. QuICS allows extracting three parameters related to the resolution, and contrast SNR of the image. We find that SPLIT-ISM provides an increase in spatial resolution for both the Genoa Instrument PRISM and the Zeiss Airyscan microscopes.

High-quality swept-source optical coherence tomography (SS-OCT) imaging systems require precise synchronization between the OCT signal and the k-clock signal. However, in practical applications, an uncertain time delay between these signals can cause inaccurate k-space sampling, leading to degraded imaging resolution. This study first simulates the axial resolution degradation curve caused by varying time delays and experimentally validates the results. Additionally, the effects of different time delays on both OCT structural and blood flow images are systematically investigated through experiments. To address this issue, a numerical calibration method is implemented to compensate for the nonlinear phase component. This approach involves acquiring two reflection signals at different depths, unwrapping the phase, performing high-order polynomial fitting, and removing nonlinear phase components induced by time delay, which effectively corrects the resolution degradation. Experiments conducted on semi-transparent white tape, blood flow phantom, and human nailfold demonstrate that the proposed correction algorithm significantly improves the axial resolution of both structural and blood flow images. The findings indicate that our investigation and the developed calibration method are instrumental in reconstructing high-resolution SS-OCT images, which are essential for accurate diagnosis and effective treatment monitoring in clinical applications.

Optical coherence tomography (OCT) is promising to become an essential imaging tool for non-invasive oral mucosal tissue assessment, but it faces challenges like speckle noise and motion artifacts. In addition, it is difficult to distinguish different layers of oral mucosal tissues from gray level OCT images due to the similarity of optical properties between different layers. We introduce the Efficient Segmentation-Denoising Model (ESDM), a multi-task deep learning framework designed to enhance OCT imaging by reducing scan time from ∼8s to ∼2s and improving oral epithelium layer segmentation. ESDM integrates the local feature extraction capabilities of the convolution layer and the long-term information processing advantages of the transformer, achieving better denoising and segmentation performance compared to existing models. Our evaluation shows that ESDM outperforms state-of-the-art models with a PSNR of 26.272, SSIM of 0.737, mDice of 0.972, and mIoU of 0.948. Ablation studies confirm the effectiveness of our design, such as the feature fusion methods, which enhance performance with minimal model complexity increase. ESDM also presents high accuracy in quantifying oral epithelium thickness, achieving mean absolute errors as low as 5 µm compared to manual measurements. This research shows that ESDM can notably improve OCT imaging and reduce the cost of accurate oral epithermal segmentation, improving diagnostic capabilities in clinical settings.

Manipulating ocular longitudinal chromatic aberration (LCA) can enhance visual outcomes in presbyopia-correcting multifocal intraocular lenses and serve as a powerful tool for investigating eye growth mechanisms. This study introduces a spatial light modulator-based visual simulator (SLMVS) that utilizes the unique properties of diffractive optics with a negative Abbe number to precisely control ocular LCA, allowing for its correction or reversal. The system was validated through optical bench tests using a model eye and human subjects. Bench test results under polychromatic light showed improved image quality close to monochromatic performance when LCA was corrected at the far focus of both monofocal and trifocal lenses. In human tests, the SLMVS achieved a mean LCA correction of 0.01 ± 0.13 D and a mean LCA reversal of -1.62 ± 0.40 D, compared to an average ocular LCA of 1.41 ± 0.25 D.

This study introduces a fast and accurate online training method for blood flow index (BFI) and relative BFI (rBFI) reconstruction in diffuse correlation spectroscopy (DCS). We implement rigorous mathematical models to simulate the auto-correlation functions (g ₂) for semi-infinite homogeneous and three-layer human brain models. We implemented a fast online training algorithm known as random vector functional link (RVFL) to reconstruct BFI from noisy g ₂. We extensively evaluated RVFL regarding both speed and accuracy for training and inference. Moreover, we compared RVFL with extreme learning machine (ELM) architecture, a conventional convolutional neural network (CNN), and three fitting algorithms. Results from semi-infinite and three-layer models indicate that RVFL achieves higher accuracy than the other algorithms, as evidenced by comprehensive metrics. While RVFL offers comparable accuracy to CNNs, it boosts training speeds that are 3900-fold faster and inference speeds that are 19.8-fold faster, enhancing its generalizability across different experimental settings. We also used g ₂ from one- and three-layer Monte Carlo (MC)-based in-silico simulations, as well as from analytical models, to compare the accuracy and consistency of the results obtained from RVFL and ELM. Furthermore, we discuss how RVFL is more suitable for embedded hardware due to its lower computational complexity than ELM and CNN for training and inference.

Light-sheet fluorescence microscopy (LSFM) has demonstrated great potential in the life sciences owing to its efficient volumetric imaging capabilities. For long-term imaging, the light-sheet typically needs to be stabilized to the detection focal plane for the best imaging results. Current light-sheet stabilization methods rely on fluorescence emission from the sample, which may interrupt scientific imaging and add to sample photobleaching. Here, we show that for oblique plane microscopes (OPM), a subset of LSFM where a single primary objective is used for illumination and detection, light-sheet stabilization can be achieved without expending sample fluorescence. Our method achieves ∼21 nm axial precision and maintains the light-sheet well within the depth of focus of the detection system for hour-long acquisition runs in a lab environment that would otherwise detune the system. We demonstrate subcellular imaging of the actin skeleton in melanoma cancer cells with a stabilized OPM.

Peripheral piston modulation in diffractive multifocal lenses suggests potential improvements in distance vision quality. Five lens designs-bifocal (BF), bifocal with piston (BFP), trifocal (TF), trifocal with piston (TFP), and a commercial refractive (RCN)-were compared using an adaptive optics visual simulator. Optical simulations revealed enhanced optical quality for distant objects with peripheral pistons, without compromising near vision. Visual performance and quality were assessed in eight cycloplegic young subjects. The peripheral piston was associated with trends toward improved high- and low-contrast visual acuity and visual preference scores at distance, suggesting functional and perceptual benefits over non-piston designs.

Broadband absorption spectroscopy (BAS) has the potential to fill an unmet need for a non-invasive monitor of cerebral edema. In this work, we demonstrated the feasibility of BAS to measure water content in the adult human head by quantifying measurement reliability and the influence of ambient light on BAS-measured parameters. We observed strong inter-operator reliability of BAS-measurement of water (R² = 0.69, Lin's CCC = 0.78, p < 0.01). Similar significant reliability was observed for lipid (R² = 0.54), oxygen saturation (R² = 0.81), and total hemoglobin (R² = 0.44). Ambient light increased water content by approximately 2-3% compared to dark conditions.

Lensless imaging is a popular research field because of its small size, wide field-of-view, and low aberration in recent years. However, some traditional lensless imaging methods suffer from slow convergence, mechanical errors, and conjugate solution interference, which limit their further application and development. In this work, we proposed a lensless imaging method based on a spatial light modulator (SLM) with unknown phase modulation values. In our imaging system, the SLM is utilized to modulate the wavefront of the object. When the phase modulation values of the SLM are inaccurate or unknown, conventional algorithms such as amplitude-phase retrieval (APR) or the extended ptychographic iterative engine (ePIE) fail to reconstruct the complex amplitude information of the object. To address this challenge, we introduce a novel approach that combines ptychographic scanning along a spiral path with the ePIE algorithm, enabling accurate reconstruction of the original image. We further analyze the effect of modulation function and the characteristics of the coherent light source on the quality of the reconstructed image. The experiments show that the proposed method is superior to traditional methods in terms of recovering speed and accuracy, with the recovering resolution up to 14 μm in the reconstruction of the USAF phase plate image.