High-dose laser exposure to tissue causes thermal damage and significant changes in tissue optical properties. Samples of porcine dermis and subcutaneous fat were immersed in a temperature-controlled water bath to induce a range of thermal damage. Temperature history was recorded to quantify the damage with the Arrhenius integral. Samples were then measured in a double integrating sphere setup and optical coefficients computed using the inverse adding doubling method. The tissues demonstrate non-monotonic changes in optical properties with respect to induced thermal damage. These results will inform medical scenarios and computational models where optical interaction with damaged tissues is expected.
Nanosecond electric pulses (nsEP) are effective in biomedical applications like cancer treatment, gene therapy, and drug delivery due to their ability to influence cellular membranes and intracellular processes without thermal damage. However, the high electric fields required for these bioeffects present challenges. Recent studies suggest that MHz compression of nsEP could lead to similar effects at lower field strengths, enhancing safety and efficacy. This study leverages streak camera and optical streaking microscopy to examine membrane charging dynamics from nsEP bursts. Results will broaden understanding of membrane responses to nsEP, potentially improving their effectiveness and safety in biomedical applications.
Accurate values of the optical properties of skin and subcutaneous fat are important for a variety of applications, such as optical imaging techniques and computational modeling of possible hazardous laser exposure. Several studies are available in the published literature that report skin optical properties, but the method of tissue preparation and storage in these experiments can be variable. These methods include the application of some form of cold storage, such as refrigeration or freezing, which may in turn affect the optical properties of the tissues compared to the in vivo or freshly excised case. We measured the absorption and scattering coefficients of skin and subcutaneous fat samples prior to and following various methods of cold storage, particularly refrigeration, slow freezing, and flash freezing. Tissues were collected from two subjects in order to capture biological variability. We employed a double integrating sphere setup and the inverse adding-doubling method to determine optical properties. The results of this investigation will help contextualize existing studies on tissue optical properties and enable informed procedural design for future measurements.
Nitrogen vacancy (NV) magnetometers can provide sensitive field measurements via optically detected magnetic resonance (ODMR). Built-in photo-isolation and biocompatibility of diamond have enabled many applications for biological systems. Since small changes in field strength yield commensurate intensity fluctuations, dynamic measurements require fast sensitive detectors such as photomultiplier tubes which inhibits spatial resolution. EMCCD and sCMOS cameras can make sensitive measurements, however, temporal resolution is limited by low frame rates. Recently compressed photography enabled intermediate time steps spatially encoded in a single acquisition. We propose compressed microscopy for capturing dynamic magnetic field fluctuations using sCMOS, moving towards label-free neural sensing.
The optical activity of Raman scattering provides insight into the absolute configuration and conformation of chiral molecules. Applications of Raman optical activity (ROA) are limited by long integration times due to a relatively low sensitivity of the scattered light to chirality (typically 10-3 to 10-5). We apply ROA techniques to hyper-Raman scattering using incident circularly polarized light and a right-angle scattering geometry. We explore the sensitivity of hyper- Raman scattering to chirality as compared to spontaneous Raman optical activity. Using the excitation wavelength at around 532 nm, the photobleaching is minimized, while the hyper-Raman scattering benefits from the electronic resonant enhancement. For S/R-2-butanol and L/D-tartaric acid, we were unable to detect the hyper-Raman optical activity at the sensitivity level of 1%. We also explored parasitic thermal effects which can be mitigating by varying the repetition rate of the laser source used for excitation of hyper-Raman scattering.
Raman imaging continues to grow in popularity as a label-free technique for characterizing the underlying chemical structure of biological materials, both in-vitro and in-vivo. While Raman spectra demonstrate high chemical specificity, spontaneous Raman scattering is an inherently weak process and requires prohibitively long acquisition times. When Raman is utilized to image highly scattering cellular environments, integration times can be on the order of several minutes to hours. Recently developed compressed sensing techniques can greatly improve hyperspectral Raman acquisition times by randomly under-sampling the spatial dimensions. A digital micromirror device (DMD) is used to spatially encode the image plane. The encoded image is then propagated to a spectrometer where the spectral components are produced by shearing one spatial dimension. Several reconstruction algorithms have been developed that can then be used to return the original. Here, we will present single-shot, 2D Raman imaging of CHO cells using compressed hyperspectral Raman microscope. This system provides an order of magnitude improvement on traditional hyperspectral acquisition rates. Single-shot compressed hyperspectral Raman images can reveal biochemical changes due to short lifetime dynamic processes. These improvements will allow imaging of samples that metabolize quickly, rapidly oxidize, or are physically altered under experimental conditions.
Scanning confocal Raman spectroscopy was applied for detecting and identifying topically applied ocular pharmaceuticals on rabbit corneal tissue. Raman spectra for Cyclosporin A, Difluprednate, and Dorzolamide were acquired together with Raman spectra from rabbit corneas with an unknown amount of applied drug. Kernel principle component analysis (KPCA) was then used to explore a transform that can describe the acquired set of Raman spectra. Using this transform, we observe some spectral similarity between cornea spectra and Cyclosporin A, with little similarity to Dorzolamide and Difluprednate. Further investigation is needed to identify why these differences occur.
Nitrogen vacancy (NV) centers have amassed considerable interest as biologically compatible magnetometers. NV centers are point defects consisting of a substituted nitrogen adjacent to a vacancy in diamond’s lattice. These defects exhibit an optically addressable magnetic field response at room temperature, a process known as optically detected magnetic resonance (ODMR). We take advantage of the imbalanced probability of the excited magnetic spin ±1 state to transition to the ground magnetic spin 0 state through an intermediate secondary singlet pathway in NV color centers. This alternative intersystem relaxation response can provide a source of contrast for live-cell imaging with potential nano-scale resolution, as well as for measuring low concentrations of paramagnetic ions. Paramagnetic molecules generate random magnetic field fluctuations which result in a non-zero RMS field. These fluctuations can induce spin relaxation in NVs in the near field of such paramagnetic molecules. This technique is applied at room temperature without microwave control frequencies or induced magnetic fields. The relaxation time for a bulk NV sensor doped with phosphorus was measured, which compared well with referenced values. Phosphorus doping in NV diamond allows the excitation wavelength to be red shifted for a less cytotoxic effect. ODMR spectra were acquired with a helium neon (HeNe) laser.
Identification and analysis of laser-induced lesions on the retina can be challenging in both the research and clinical settings depending on the age of a lesion and the imaging modality used for detection. Previous research exploring retinal damage thresholds utilized the consensus of an expert panel to confirm energies required for minimal visible lesions, a method that includes some subjectivity. Because of this, there is a desire to develop an image processing architecture to accurately locate retinal laser lesions in images generated from clinically relevant modalities. Issues such as imaging aberrations inducing circular artifacts, perceived stretch in lesions, and differences in the appearance of lesions across the dataset preclude use of traditional image processing tools. A database containing images of laser lesions has been developed in order to provide a reference for researchers and clinicians. In this work, we explored using various Convolutional Neural Network (CNN) architectures and preprocessing techniques to more objectively identify and analyze retinal laser lesions. Specifically, we developed frequency domain filtering techniques in order to emphasize lesion qualities. We consider this task to be one of image segmentation to make the networks somewhat size invariant. Since the lesions account for a small amount of the image pixels, we implemented an intersection-based loss function. We evaluated the performance of our trained networks against more complicated architecture variants. Additionally, we trained a network to segment and classify lesions as the result of photochemical, photomechanical or photothermal damage.
Identification and analysis of laser-induced lesions on the retina can be challenging in both the research and clinical settings depending on the age of a lesion and the imaging modality used for detection. Previous research exploring retinal damage thresholds utilized the consensus of an expert panel to confirm energies required for minimal visible lesions, a method that includes some subjectivity. Because of this, there is a desire to develop an image processing architecture to accurately locate retinal laser lesions in images generated from clinically relevant modalities. Issues such as imaging aberrations inducing circular artifacts, perceived stretch in lesions, and differences in the appearance of lesions across the dataset preclude use of traditional image processing tools. A database containing images of laser lesions has been developed in order to provide a reference for researchers and clinicians. In this work, we explored using various Convolutional Neural Network (CNN) architectures and preprocessing techniques to more objectively identify and analyze retinal laser lesions. Specifically, we developed frequency domain filtering techniques in order to emphasize lesion qualities. We consider this task to be one of image segmentation to make the networks somewhat size invariant. Since the lesions account for a small amount of the image pixels, we implemented an intersection-based loss function. We evaluated the performance of our trained networks against more complicated architecture variants. Additionally, we trained a network to segment and classify lesions as the result of photochemical, photomechanical or photothermal damage.
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