Biomedical optics express最新文献

Super-solution fluorescence microscopy, such as single-molecule localization microscopy (SMLM), is effective in observing subcellular structures and achieving excellent enhancement in spatial resolution in contrast to traditional fluorescence microscopy. Recently, deep learning has demonstrated excellent performance in SMLM in solving the trade-offs between spatiotemporal resolution, phototoxicity, and signal intensity. However, most of these researches rely on sufficient and high-quality datasets. Here, we propose a physical priors-based convolutional super-resolution network (PCSR), which incorporates a physical-based loss term and an initial optimization process based on the Wiener filter to create excellent super-resolution images directly using low-resolution images. The experimental results demonstrate that PCSR enables the achievement of a fast reconstruction time of 100 ms and a high spatial resolution of 10 nm by training on a limited dataset, allowing subcellular research with high spatiotemporal resolution, low cell phototoxic illumination, and high accessibility. In addition, the generalizability of PCSR to different live cell structures makes it a practical instrument for diverse cell research.

In this paper, we introduce a physics-guided deep learning approach for high-quality, real-time Fourier-domain optical coherence tomography (FD-OCT) image reconstruction. Unlike traditional supervised deep learning methods, the proposed method employs unsupervised learning. It leverages the underlying OCT imaging physics to guide the neural networks, which could thus generate high-quality images and provide a physically sound solution to the original problem. Evaluations on synthetic and experimental datasets demonstrate the superior performance of our proposed physics-guided deep learning approach. The method achieves the highest image quality metrics compared to the inverse discrete Fourier transform (IDFT), the optimization-based methods, and several state-of-the-art methods based on deep learning. Our method enables real-time frame rates of 232 fps for synthetic images and 87 fps for experimental images, which represents significant improvements over existing techniques. Our physics-guided deep learning-based approach could offer a promising solution for FD-OCT image reconstruction, which potentially paves the way for leveraging the power of deep learning in real-world OCT imaging applications.

NIMO-TEMPO is a metrology device that measures all types of intraocular lenses available in the ophthalmic market. Its technology, based on interferometry, captures the wavefront of the lens to compute optical results essential to evaluate its quality and understand its characteristics. This study aims to demonstrate the reliability of this device and its associated software, TEMPO-MENTOR, in measuring intraocular lenses. The analysis is based on comparing the theoretical results of the optical design and data computed by the device algorithm. Results provided with NIMO-TEMPO are also validated using another metrology device.

Compressed sensing (CS) is an approach that enables comprehensive imaging by reducing both imaging time and data density, and is a theory that enables undersampling far below the Nyquist sampling rate and guarantees high-accuracy image recovery. Prior efforts in the literature have focused on demonstrations of synthetic undersampling and reconstructions enabled by compressed sensing. In this paper, we demonstrate the first physical, hardware-based sub-Nyquist sampling with a galvanometer-based OCT system with subsequent reconstruction enabled by compressed sensing. Acquired images of a variety of samples, with volume scanning time reduced by 89% (12.5% compression rate), were successfully reconstructed with relative error (RE) of less than 20% and mean square error (MSE) of around 1%.

Optical coherence tomography (OCT) is a unique imaging modality capable of axial sectioning with a resolution of only a few microns. Its ability to image with high resolution deep within tissue makes it ideal for material inspection, dentistry, and, in particular, ophthalmology. Widefield retinal imaging has garnered increasing clinical interest for the detection of numerous retinal diseases. However, real-time applications in clinical practice demand the contrast of swept-source OCT at scan speeds that limit their depth range. The curvature of typical samples, such as teeth, corneas, or retinas, thus restricts the field-of-view of fast OCT systems. Novel high-speed swept sources are expected to further improve the scan rate; however, not without exacerbating the already severe trade-off in depth range. Here, we show how, without the need for mechanical repositioning, harmonic images can be rapidly synthesized at any depth. This is achieved by opto-electronic modulation of a single-frequency swept source laser in tandem with tailored numerical dispersion compensation. We demonstrate experimentally how real-time imaging of highly-curved samples is enabled by extending the effective depth-range 8-fold. Even at the scan speed of a 400 kHz swept source, harmonic OCT enables widefield retinal imaging.

This work describes the development of silicone-based evaluation phantoms for biomedical optics in the wavelength range from 400 to 1550 nm. The absorption coefficient μ _a and the reduced scattering coefficient ${\mu }_{\text{s}}^{\text{'}}$ were determined using an integrating sphere setup. Zirconium dioxide pigments were used as scatterers and carbon black as absorbers. We developed an in-house manufacturing process using a Hauschild SpeedMixer to ensure reproducibility. A set of nine cubic phantoms with three different reduced scattering and absorption coefficients was produced. Prediction of the μ _a and ${\mu }_{\text{s}}^{\text{'}}$ was done by using the weighted mass concentrations of the used materials. The average prediction accuracy over all wavelengths and phantoms is 1.0% for the reduced scattering coefficient and 3.5% for the absorption coefficient.

Polygon scanners allow for some of the fastest available line rates for raster scanning imaging. Due to the optical invariant, however, there is a trade-off between the line rate and the number of resolvable points per line. Here, we describe a device that can increase the number of resolvable points per line of mirror-based scanners without sacrificing speed. We first theoretically model the effect of the device on the number of resolvable points per line of a polygon scanner, and then experimentally test this device with both a simplified facet system and a transmission microscope using a polygon scanner. We demonstrate an improvement in the field of view by 1.7 times without a reduction in spatial resolution.

This study presents a systematic method to simulate various intraocular lenses (IOLs) available in the market. Five IOLs (two trifocals, one bifocal, one enhanced monofocal, and one extended depth of focus (EDOF)) were evaluated in terms of through focus visual Strehl (TFVS) utilizing the OptiSpheric IOL PRO2 device (Trioptics GmbH). Then, the estimated TFVS (ETFVS) and the temporal coefficients necessary for temporal multiplexing were computed, and through an iterative process, the SimVis TFVS was obtained. Finally, a high-speed focimeter was used to measure the opto-tunable lens responses to the temporal profile, and the experimental SimVis TFVS was acquired. Therefore, results are analyzed in terms of ETFVS (computed from the VSR-OTF), SimVis TFVS (computed from the TCs through temporal multiplexing), and experimental SimVis TFVS (acquired from the high-speed focimeter setup). The ETFVS and the SimVis TFVS curves demonstrated excellent alignment across all IOLs with cross-correlation coefficients > 0.94. Similarly, the experimental SimVis TFVS and the SimVis TFVS curves showed high correlation with cross-correlation coefficients > 0.97 and root mean square error (RMSE) < 0.05 for each lens. We demonstrated that different IOL designs can be visually simulated using its TFVS to obtain the corresponding temporal coefficients for simulations through temporal multiplexing using the SimVis technology.

Shack-Hartmann-based wavefront sensing combined with deep learning, due to its fast, accurate, and large dynamic range, has been widely studied in many fields including ocular aberration measurement. Problems such as noise and corneal reflection affect the accuracy of detection in practical measuring ocular aberration systems. This paper establishes a framework comprising of a noise-added model, Hartmannograms with corneal reflections and the corneal reflection elimination algorithm. Therefore, a more realistic data set is obtained, enabling the convolutional neural network to learn more comprehensive features and carry out real machine verification. The results show that the proposed method has excellent measurement accuracy. The root mean square error (RMSE) of the residual wavefront is 0.00924 ± 0.0207λ (mean ± standard deviation) in simulation and 0.0496 ± 0.0156λ in a real machine. Compared with other methods, this network combined with the proposed corneal reflection elimination algorithm is more accurate, speedier, and more widely applicable in the noise and corneal reflection situations, making it a promising tool for ocular aberration measurement.

In this study, we present a pump-free SERS microfluidic chip capable of detecting liver cancer-related miR-21 and miR-155 concurrently with ultra-sensitivity and high efficiency. We employed a Fe₃O₄@cDNA-AuNPs@Raman reporter@H composite structure and a recognition competition strategy. When the target miRNAs (miR-21 and miR-155) are present in the test liquid, they specifically compete with the nucleic acid complementary strand(H) of Fe₃O₄@cDNA-AuNPs@Raman reporter@H, causing AuNPs to competitively detach from the surface of Fe₃O₄, resulting in a decrease in the SERS signal. Consequently, this pump-free SERS microfluidic chip enables the detection of the target miRNAs more rapidly and accurately in complex environments. This method offers an approach for the simultaneous and efficient detection of miRNAs and holds promising applications in the early diagnosis of liver cancer.