Holistic Monte-Carlo optical modelling of biological imaging

The invention and advancement of biological microscopy depends critically on an ability to accurately simulate imaging of complex biological structures embedded within complex scattering media. Unfortunately no technique exists for rigorous simulation of the complete imaging process, including the source, instrument, sample and detector. Monte-Carlo modelling is the gold standard for the modelling of light propagation in tissue, but is somewhat laborious to implement and does not incorporate the rejection of scattered light by the microscope. On the other hand microscopes may be rigorously and rapidly modelled using commercial ray-tracing software, but excluding the interaction with the biological sample. We report a hybrid Monte-Carlo optical ray-tracing technique for modelling of complete imaging systems of arbitrary complexity. We make the software available to enable user-friendly and rigorous virtual prototyping of biological microscopy of arbitrary complexity involving light scattering, fluorescence, polarised light propagation, diffraction and coherence. Examples are presented for the modelling and optimisation of representative imaging of neural cells using light-sheet and micro-endoscopic fluorescence microscopy and imaging of retinal vasculature using confocal and non-confocal scanning-laser ophthalmoscopes

Design, Implementation, and Evaluation of a Fluorescence Laminar Optical Tomography Scanner for Brain Imaging

Implementation of new instrumentation and techniques for neuroscience research in recent years has opened new avenues in the study of the dynamics of large-scale neural networks such as the brain. In many of these techniques, including fluorescence recordings and optogenetic stimulation, a combination of photonics and molecular genetic methods are exploited to manipulate and monitor neural activities. Such techniques have been proven to be highly efficient in unraveling the mysteries of data processing in the micro circuits of the brain and as a result these techniques are widely used nowadays in most neuroscience labs. In optogenetics, cell-types of interest are genetically modified by expressing light-sensitive proteins adapted from microbial opsin. Once these proteins are expressed, we are able to use light of appropriate wavelengths to manipulate, increase or suppress neural activity of specific neurons on command. With a high temporal resolution (in the order of milliseconds) and cell-type-specific precision, optogenetics is able to probe how the nervous system functions in real-time, even in freely-moving animals. Currently, whenever genetic modifications are employed in the study of nervous systems, fluorescence proteins are also co-expressed in the same cells as biological markers to visualize the induced changes in the targeted cells. Despite its importance to trace the signal of such markers in-vivo, capabilities of the developed fluorescence tomography instrumentation are still limited and researchers mostly document the fluorescence distribution and expression of proteins of interest after euthanizing the animal and dissection of the tissue. In this project, we present our effort in implementing a fluorescence laminar optical tomography (FLOT) system which is specifically designed for non-invasive three dimensional imaging of fluorescence proteins within the brain of rodents. The application of the developed technology is not limited to optogenetics, but it can be used as a powerful tool to help improving the precision and accuracy of neuroscience and optogenetic experiments. In this design, a set of galvanometer mirrors are employed for realization of a fast and flexible scanner while a highly sensitive camera records the produced fluorescence signals. Fluorescence laminar optical tomography (FLOT) scanner has shown promising results in imaging superficial areas up to 2mm deep from the surface, with the resolution of ~200µm. Details of the design of the hardware and reconstruction algorithms are discussed and samples of experimental results are presented

Computational Framework For Neuro-Optics Simulation And Deep Learning Denoising

The application of machine learning techniques in microscopic image restoration has shown superior performance. However, the development of such techniques has been hindered by the demand for large datasets and the lack of ground truth. To address these challenges, this study introduces a computer simulation model that accurately captures the neural anatomic volume, fluorescence light transportation within the tissue volume, and the photon collection process of microscopic imaging sensors. The primary goal of this simulation is to generate realistic image data for training and validating machine learning models. One notable aspect of this study is the incorporation of a machine learning denoiser into the simulation, which accelerates the computational efficiency of the entire process. By reducing noise levels in the generated images, the denoiser significantly enhances the simulation\u27s performance, allowing for faster and more accurate modeling and analysis of microscopy images. This approach addresses the limitations of data availability and ground truth annotation, offering a practical and efficient solution for microscopic image restoration. The integration of a machine learning denoiser within the simulation significantly accelerates the overall simulation process, while improving the quality of the generated images. This advancement opens new possibilities for training and validating machine learning models in microscopic image restoration, overcoming the challenges of large datasets and the lack of ground truth

Deep Tissue Light Delivery and Fluorescence Tomography with Applications in Optogenetic Neurostimulation

Study of the brain microcircuits using optogenetics is an active area of research. This method has a few advantages over the conventional electrical stimulation including the bi-directional control of neural activity, and more importantly, specificity in neuromodulation. The first step in all optogenetic experiments is to express certain light sensitive ion channels/pumps in the target cell population and then confirm the proper expression of these proteins before running any experiment. Fluorescent bio-markers, such as green fluorescent protein (GFP), have been used for this purpose and co-expressed in the same cell population. The fluorescent signal from such proteins provides a monitory mechanism to evaluate the expression of optogenetic opsins over time. The conventional method to confirm the success in gene delivery is to sacrifice the animal, retract and slice the brain tissue, and image the corresponding slices using a fluorescent microscope. Obviously, determining the level of expression over time without sacrificing the animal is highly desirable. Also, optogenetics can be combined with cell-type specific optical recording of neural activity for example by imaging the fluorescent signal of genetically encoded calcium indicators. One challenging step in any optogenetic experiment is delivering adequate amount of light to target areas for proper stimulation of light sensitive proteins. Delivering sufficient light density to a target area while minimizing the off-target stimulation requires a precise estimation of the light distribution in the tissue. Having a good estimation of the tissue optical properties is necessary for predicting the distribution of light in any turbid medium. The first objective of this project was the design and development of a high resolution optoelectronic device to extract optical properties of rats\u27 brain tissue (including the absorption coefficient, scattering coefficient, and anisotropy factor) for three different wavelengths: 405nm, 532nm and 635nm and three different cuts: transverse, sagittal, and coronal. The database of the extracted optical properties was linked to a 3D Monte Carlo simulation software to predict the light distribution for different light source configurations. This database was then used in the next phase of the project and in the development of a fluorescent tomography scanner. Based on the importance of the fluorescent imaging in optogenetics, another objective of this project was to design a fluorescence tomography system to confirm the expression of the light sensitive proteins and optically recording neural activity using calcium indicators none or minimally invasively. The method of fluorescence laminar optical tomography (FLOT) has been used successfully in imaging superficial areas up to 2mm deep inside a scattering medium with the spatial resolution of ~200µm. In this project, we developed a FLOT system which was specifically customized for in-vivo brain imaging experiments. While FLOT offers a relatively simple and non-expensive design for imaging superficial areas in the brain, still it has imaging depth limited to 2mm and its resolution drops as the imaging depth increases. To address this shortcoming, we worked on a complementary system based on the digital optical phase conjugation (DOPC) method which was shown previously that is capable of performing fluorescent tomography up to 4mm deep inside a biological tissue with lateral resolution of ~50 µm. This system also provides a non-invasive method to deliver light deep inside the brain tissue for neurostimulation applications which are not feasible using conventional techniques because of the high level of scattering in most tissue samples. In the developed DOPC system, the performance of the system in focusing light through and inside scattering mediums was quantified. We also showed how misalignments and imperfections of the optical components can immensely reduce the capability of a DOPC setup. Then, a systematic calibration algorithm was proposed and experimentally applied to our DOPC system to compensate main aberrations such as reference beam aberrations and also the backplane curvature of the spatial light modulator. In a highly scattering sample, the calibration algorithm achieved up to 8 fold increase in the PBR

Optical trapping: optical interferometric metrology and nanophotonics

The two main themes in this thesis are the implementation of interference methods with optically trapped particles for measurements of position and optical phase (optical interferometric metrology) and the optical manipulation of nanoparticles for studies in the assembly of nanostructures, nanoscale heating and nonlinear optics (nanophotonics). The first part of the thesis (chapter 1, 2) provides an introductory overview to optical trapping and describes the basic experimental instrument used in the thesis respectively. The second part of the thesis (chapters 3 to 5) investigates the use of optical interferometric patterns of the diffracting light fields from optically trapped microparticles for three types of measurements: calibrating particle positions in an optical trap, determining the stiffness of an optical trap and measuring the change in phase or coherence of a given light field. The third part of the thesis (chapters 6 to 8) studies the interactions between optical traps and nanoparticles in three separate experiments: the optical manipulation of dielectric enhanced semiconductor nanoparticles, heating of optically trapped gold nanoparticles and collective optical response from an ensemble of optically trapped dielectric nanoparticles

Three-dimensional full wave model of image formation in optical coherence tomography

We demonstrate, what we believe to be, the first mathematical model of image formation in optical coherence tomography, based on Maxwell’s equations, applicable to general three-dimensional samples. It is highly realistic and represents a significant advance on a previously developed model, which was applicable to two-dimensional samples only. The model employs an electromagnetic description of light, made possible by using the pseudospectral time-domain method for calculating the light scattered by the sample which is represented by a general refractive index distribution. We derive the key theoretical and computational advances required to develop this model. Two examples are given of image formation for which analytic comparisons may be calculated: point scatterers and finite sized spheres. We also provide a more realistic example of C-scan formation when imaging turbid media. We anticipate that this model will be important for various applications in OCT, such as image interpretation and the development of quantitative techniques