Wavefront Shaping Concepts for Application in Optical Coherence Tomography-A Review

Design and Simulation of a 19-Electrode MEMS Piezoelectric Thin-Film Micro-Deformable Mirror for Ophthalmology

This study presents a numerical simulation-based investigation of a MEMS (micro-electromechanical systems)technology-based deformable mirror employing a piezoelectric film for fundus examination in adaptive optics. Compared to the classical equal-area electrode arrangement model, we optimize the electrode array for higher-order aberrations. The optimized model centralizes electrodes around the mirror center, which realizes low-voltage driving with high-accuracy correction. The optimized models exhibited commendable correction abilities, achieving a unidirectional displacement of 5.74 μm with a driven voltage of 15 V. The voltage-displacement relationship demonstrated high linearity at 0.99. Furthermore, the deformable mirror's influence matrix was computed, aligning with the Zernike standard surface shape of the order 1-3. To quantify aberration correction capabilities, fitting residuals for both models were calculated. The results indicate an average removal of 96.8% of aberrations to the human eye. This underscores that the optimized model outperforms the classical model in correcting high-order aberrations.

Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography

Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). Conclusions: The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening.

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

In situ laser manipulation of root tissues in transparent soil

AIMS

Laser micromanipulation such as dissection or optical trapping enables remote physical modification of the activity of tissues, cells and organelles. To date, applications of laser manipulation to plant roots grown in soil have been limited. Here, we show laser manipulation can be applied in situ when plant roots are grown in transparent soil.

METHODS

We have developed a Q-switched laser manipulation and imaging instrument to perform controlled dissection of roots and to study light-induced root growth responses. We performed a detailed characterisation of the properties of the cutting beams through the soil, studying dissection and optical ablation. Furthermore, we also studied the use of low light doses to control the root elongation rate of lettuce seedlings (Lactuca sativa) in air, agar, gel and transparent soil.

RESULTS

We show that whilst soil inhomogeneities affect the thickness and circularity of the beam, those distortions are not inherently limiting. The ability to induce changes in root elongation or complete dissection of microscopic regions of the root is robust to substrate heterogeneity and microscopy set up and is maintained following the limited distortions induced by the transparent soil environment.

CONCLUSIONS

Our findings show that controlled in situ laser dissection of root tissues is possible with a simple and low-cost optical set-up. We also show that, in the absence of dissection, a reduced laser light power density can provide reversible control of root growth, achieving a precise "point and shoot" method for root manipulation.