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Photonics
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Adaptive Optics and Its Applications
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Adaptive Optics and Its Applications

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Biophotonics and Biomedical Optics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14956

Special Issue Editor

Dr. Ratheesh Kumar Meleppat

E-Mail Website
Guest Editor
Department of Ophthalmology & Vision Science, UC Davis Health System, University of California Davis, Davis, CA 95616, USA
Interests: biophotonics; biomedical optical imaging; optical coherence tomography; confocal fluorescence imaging; retinal imaging; biomedical instrumentation; optical engineering; optoelectronics; adaptive optics; digital image processing; machine learning; deep learning; artificial intelligence; hyperspectral imaging; spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive optics (AO) is a technology used to enhance the performance of an optical system by manipulating the optical wavefront. A wavefront of interest could be corrupted by several means, such as optical elements, misalignment, presence of medium, etc. Employing an active means of manipulating a wavefront with adaptive optical elements such as a deformable mirror provides precise control of the shape of the wavefront. This precision and programmable control, unobtainable by non-adaptive elements, leads to dramatic improvements in the performance of many optical systems (e.g., resolution in imaging systems). Thus, AO is employed in a broad range of imaging and non-imaging applications to reduce aberrations, improve image quality, or shape laser beams.

AO has become a powerful branch in photonics and emerged as an inevitable tool in a wide range of key applications, including astronomy and space science, atmospheric science, ophthalmology, vision science, microscopy, optical communications, beam control, etc. Notably, AO has achieved significant progress in biomedical optical imaging applications such as microscopy, where generation of high-resolution images of cellular and subcellular structures with high contrast is now possible with AO. Similarly, AO-enabled wavefront aberration measurement and correction in human eye imaging have allowed the in vivo imaging of retinal cells (such as photoreceptors) with unprecedented resolution.

AO is an area of optical research and application that has grown significantly in the last fifteen years as the cost and complexity of suitable beam shaping optics have been reduced at the same time as huge increases in computing power have occurred, making control possible for more complicated systems. The challenge now is the exchange of knowledge between different fields. With the development of increasing performance of components, adaptive optics has entered a wide variety of applications, including a significant growth in commercial application.

This Special Issue opens a window to current developments, with papers covering the breadth of research being undertaken in AO and demonstrating the way that the methodology is now entering mainstream optics and photonics, and different potential applications. In this Special Issue, original research articles and reviews are welcomed.

Research areas may include (but are not limited to) the following:

  • Novel design of AO systems for (but not limited to) the following applications:
    • Ophthalmology and vision science;
    • Astronomy and space science;
    • Optical microscopy;
    • optical communications;
    • Remote sensing;
    • Metrology;
  • Progress in (but not limited to) AO-enabled biomedical imaging systems: super-resolution microscopy, OCT, laser scanning microscope (confocal and multiphoton microscope), photoacoustic tomography/microscope, optogenetics, and optical tweezers;
  • Progress in (but not limited to) biomedical imaging applications: ophthalmology, tissue imaging, microbial (bacteria/biofilm) imaging, brain imaging, cancer imaging, cell imaging, neuroscience, etc.;
  • Multimodal optical imaging with AO
  • Image processing techniques for AO and non-AO imaging systems;
  • Machine learning (ML)/deep learning, and artificial intelligence (AI)-based AO systems;
  • Innovations in hardware and software controls for AO systems;
  • AO-enabled application-oriented novelties;
  • Simulation and theoretical modeling of optical wave propagation in turbulent media;
  • Novel optical simulation models of AO systems (such as Zemax) for various imaging and non-imaging applications.

I look forward to receiving your contributions.

Dr. Ratheesh Kumar Meleppat
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adaptive optics
  • wavefront sensing
  • biomedical imaging
  • retina imaging
  • optical imaging
  • ophthalmology
  • optical coherence tomography (OCT)

Published Papers (7 papers)

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Research

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15 pages, 7164 KiB  
Article
Effect of HIFU-Induced Thermal Ablation in Numerical Breast Phantom
by Sumit Kumar Yadav, Souradip Paul and Mayanglambam Suheshkumar Singh
Photonics 2023, 10(4), 425; https://doi.org/10.3390/photonics10040425 - 09 Apr 2023
Viewed by 1683
Abstract
Breast cancer is a leading cause of cancer-related deaths in women, and treatment involved invasive surgery such as lumpectomy. In the last decade, a non-invasive, non-contact high-intensity focused ultrasound (HIFU) therapy was developed for treatment with promising results. However, its success rate depends [...] Read more.
Breast cancer is a leading cause of cancer-related deaths in women, and treatment involved invasive surgery such as lumpectomy. In the last decade, a non-invasive, non-contact high-intensity focused ultrasound (HIFU) therapy was developed for treatment with promising results. However, its success rate depends on patient selection, tissue heterogeneities, HIFU operational parameters, and even imaging techniques. In this emerging field, computer simulations can provide us with a much-needed platform to learn, test, and deduce results virtually before conducting experiments. In this study, we used three different classes of anatomically realistic numerical breast phantoms from clinical contrast-enhanced magnetic resonance imaging (MRI) data, including scattered-, heterogeneous-, and extremely dense-type breasts. Upon assigning the appropriate acoustic and optical parameters to the tissues within, we simulated HIFU propagation by using the k-Wave toolbox in MATLAB and compared the changes introduced in the three types of breasts. It was found that scattered-type breast was best-suited for HIFU therapy. Furthermore, we simulated light-beam propagation with the ValoMC toolbox in MATLAB after introducing the lesion to compare the distribution of the initial pressure generated via the photoacoustic effect. This simulation study will be of significant clinical impact, especially in the study and management of HIFU-based treatments, which are individual/tissue-selective in nature. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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13 pages, 3196 KiB  
Communication
A.I. Pipeline for Accurate Retinal Layer Segmentation Using OCT 3D Images
by Mayank Goswami
Photonics 2023, 10(3), 275; https://doi.org/10.3390/photonics10030275 - 06 Mar 2023
Viewed by 1414
Abstract
An image data set from a multi-spectral animal imaging system was used to address two issues: (a) registering the oscillation in optical coherence tomography (OCT) images due to mouse eye movement and (b) suppressing the shadow region under the thick vessels/structures. Several classical [...] Read more.
An image data set from a multi-spectral animal imaging system was used to address two issues: (a) registering the oscillation in optical coherence tomography (OCT) images due to mouse eye movement and (b) suppressing the shadow region under the thick vessels/structures. Several classical and A.I.-based algorithms, separately and in combination, were tested for each task to determine their compatibility with data from the combined animal imaging system. The hybridization of A.I. with optical flow followed by homography transformation was shown to be effective (correlation value > 0.7) for registration. Resnet50 backbone was shown to be more effective than the famous U-net model for shadow region detection with a loss value of 0.9. A simple-to-implement analytical equation was shown to be effective for brightness manipulation with a 1% increment in mean pixel values and a 77% decrease in the number of zeros. The proposed equation allows the formulation of a constraint optimization problem using a controlling factor α for the minimization of the number of zeros, the standard deviation of the pixel values, and maximizing the mean pixel value. For layer segmentation, the standard U-net model was used. The A.I.-Pipeline consists of CNN, optical flow, RCNN, a pixel manipulation model, and U-net models in sequence. The thickness estimation process had a 6% error compared with manually annotated standard data. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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12 pages, 5612 KiB  
Article
Spiral Shaped Photonic Crystal Fiber-Based Surface Plasmon Resonance Biosensor for Cancer Cell Detection
by Shweta Mittal, Ankur Saharia, Yaseera Ismail, Francesco Petruccione, Anton V. Bourdine, Oleg G. Morozov, Vladimir V. Demidov, Juan Yin, Ghanshyam Singh and Manish Tiwari
Photonics 2023, 10(3), 230; https://doi.org/10.3390/photonics10030230 - 21 Feb 2023
Cited by 12 | Viewed by 3553
Abstract
This work presents the design and simulation of an all-optical sensor for detection of cancer cells. The proposed device is based on the surface plasmon resonance effect on a spiral shaped photonic crystal fiber structure. The finite element method (FEM) based simulations are [...] Read more.
This work presents the design and simulation of an all-optical sensor for detection of cancer cells. The proposed device is based on the surface plasmon resonance effect on a spiral shaped photonic crystal fiber structure. The finite element method (FEM) based simulations are carried out for the different cancer cells, such as HELA, Basal, Jurkat, and MDA-MB-231, MCF7, and PC12 detection. The sensor has shown the maximum sensitivity of −289 RIU−1 for the refractive index of the detection of breast cancer cell with the resolution of 2.33 × 10−4. The sensor is effective for the refractive index range of 1.36 to 1.401.The structure is based on spiral shaped photonic crystal fiber, and has shown promising linear sensing response to support the practical feasibility of the device. The proposed sensor design is effective in detecting cervical cancer, skin cancer, blood cancer, breast cancer type 1, breast cancer type 2, and adrenal gland cancer. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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14 pages, 5091 KiB  
Article
Wavefront Reconstruction of Shack-Hartmann with Under-Sampling of Sub-Apertures
by Jian Huang, Lianqun Yao, Shuyun Wu and Gongchang Wang
Photonics 2023, 10(1), 65; https://doi.org/10.3390/photonics10010065 - 06 Jan 2023
Cited by 4 | Viewed by 1668
Abstract
Shack-Hartmann wavefront sensor plays a key role in adaptive optics (AO) systems, which detect the aberrant wavefront by an array of micro-lenslets across the aperture pupil. However, some sub-apertures would be a lack of light induced by the imperfectness of micro-lenslets or pupil [...] Read more.
Shack-Hartmann wavefront sensor plays a key role in adaptive optics (AO) systems, which detect the aberrant wavefront by an array of micro-lenslets across the aperture pupil. However, some sub-apertures would be a lack of light induced by the imperfectness of micro-lenslets or pupil shift away from the optical path. Thus, the wavefront detection would be under-sampled and the performance of wavefront reconstruction would be severely degraded. It is therefore important to evaluate the influence of under-sampling on the wavefront reconstruction. In this paper, an AO system was established by the OOMAO simulation platform. For dynamical turbulence aberrations or statistic defocus aberrations, three cases including a single sub-aperture, a row of sub-apertures, and a quadrant sub-apertures lack of light were simulated. Compared with the uncorrected aberrant wavefront, our results showed that the RMS of the residual wavefront for a typical atmospheric condition (Fried parameter (r0) ranges from 5 cm to 15 cm) can be reduced by a factor of 5~8, 4~6, and 2~3 with these three cases of under-sampling, respectively. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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13 pages, 2566 KiB  
Article
Unveiling the Role of the Beam Shape in Photothermal Beam Deflection Measurements: A 1D and 2D Complex Geometrical Optics Model Approach
by Mohanachandran Nair Sindhu Swapna, Dorota Korte and Sankaranarayana Iyer Sankararaman
Photonics 2022, 9(12), 991; https://doi.org/10.3390/photonics9120991 - 16 Dec 2022
Cited by 2 | Viewed by 1191
Abstract
The preponderance of laser beam shapes cannot be ruled out during the implementation of an optical experiment nor during the formulation of its theoretical background. The present work elucidates the role of Gaussian and top-hat beam shapes in generating and analysing the photothermal [...] Read more.
The preponderance of laser beam shapes cannot be ruled out during the implementation of an optical experiment nor during the formulation of its theoretical background. The present work elucidates the role of Gaussian and top-hat beam shapes in generating and analysing the photothermal beam deflection (PBD) signals. The complex geometrical optics models encompassing the perturbations in the phase and amplitude of the probe beam with one-dimensional (1D) and two-dimensional (2D) approaches is employed to curve fit the PBD signal and are compared. From the fitted curve, the thermal diffusivity and conductivity of the sample are calculated with the 1D and 2D models. A uniform intensity distribution over the sample, like a top-hat beam, is achieved using an optical lens system and verified using a beam profiler. When the phase and amplitude of the PBD signal are fitted at different positions of the lens, i.e., in focussed and defocussed conditions, it is observed that difference in the measured thermal characteristics is about 30% for the Gaussian pump beam profile, whereas it is only <4% for top-hat beam. Even though the fitting accuracy and sum of residues estimated for the 2D model are better than 1D, the ease of computation with the 1D model employing top-hat excitation suggests the application of the top-hat profile in photothermal experiments. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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14 pages, 32320 KiB  
Communication
G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation
by Shahzaib Iqbal, Syed S. Naqvi, Haroon A. Khan, Ahsan Saadat and Tariq M. Khan
Photonics 2022, 9(12), 923; https://doi.org/10.3390/photonics9120923 - 30 Nov 2022
Cited by 15 | Viewed by 2106
Abstract
In recent years, convolutional neural network architectures have become increasingly complex to achieve improved performance on well-known benchmark datasets. In this research, we have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps, hence reducing [...] Read more.
In recent years, convolutional neural network architectures have become increasingly complex to achieve improved performance on well-known benchmark datasets. In this research, we have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps, hence reducing the complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The suggested architecture is analysed using three publicly available datasets for retinal vessel segmentation, namely DRIVE, CHASE and STARE datasets. The proposed G-Net light achieves an average accuracy of 0.9686, 0.9726, 0.9730 and F1-score of 0.8202, 0.8048, 0.8178 on DRIVE, CHASE, and STARE datasets, respectively. The proposed G-Net light achieves state-of-the-art performance and outperforms other lightweight vessel segmentation architectures with fewer trainable number of parameters. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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Review

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21 pages, 11557 KiB  
Review
Digging Deeper through Biological Specimens Using Adaptive Optics-Based Optical Microscopy
by Gagan Raju and Nirmal Mazumder
Photonics 2023, 10(2), 178; https://doi.org/10.3390/photonics10020178 - 08 Feb 2023
Viewed by 2068
Abstract
Optical microscopy is a vital tool for visualizing the cellular and sub-cellular structures of biological specimens. However, due to its limited penetration depth, its biological applicability has been hindered. The scattering and absorption of light by a wide array of biomolecules causes signal [...] Read more.
Optical microscopy is a vital tool for visualizing the cellular and sub-cellular structures of biological specimens. However, due to its limited penetration depth, its biological applicability has been hindered. The scattering and absorption of light by a wide array of biomolecules causes signal attenuation and restricted imaging depth in tissues. Researchers have put forth various approaches to address this, including designing novel probes for imaging applications and introducing adaptive optics (AO) technology. Various techniques, such as direct wavefront sensing to quickly detect and fix wavefront deformation and indirect wavefront sensing using modal and zonal methods to rectify complex aberrations, have been developed through AO paradigms. In addition, algorithmic post-processing without mechanical feedback has been utilized to correct the optical patterns using the matrix-based method. Hence, reliable optical imaging through thick biological tissue is made possible by sensorless AO. This review highlights the latest advancements in various AO-based optical microscopy techniques for depth-resolved imaging and briefly discusses their potential in various biomedical applications. Full article
(This article belongs to the Special Issue Adaptive Optics and Its Applications)
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