Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns

Improving flat fluorescence microscopy in scattering tissue through deep learning strategies

Intravital microscopy in small animals growingly contributes to the visualization of short- and long-term mammalian biological processes. Miniaturized fluorescence microscopy has revolutionized the observation of live animals' neural circuits. The technology's ability to further miniaturize to improve freely moving experimental settings is limited by its standard lens-based layout. Typical miniature microscope designs contain a stack of heavy and bulky optical components adjusted at relatively long distances. Computational lensless microscopy can overcome this limitation by replacing the lenses with a simple thin mask. Among other critical applications, Flat Fluorescence Microscope (FFM) holds promise to allow for real-time brain circuits imaging in freely moving animals, but recent research reports show that the quality needs to be improved, compared with imaging in clear tissue, for instance. Although promising results were reported with mask-based fluorescence microscopes in clear tissues, the impact of light scattering in biological tissue remains a major challenge. The outstanding performance of deep learning (DL) networks in computational flat cameras and imaging through scattering media studies motivates the development of deep learning models for FFMs. Our holistic ray-tracing and Monte Carlo FFM computational model assisted us in evaluating deep scattering medium imaging with DL techniques. We demonstrate that physics-based DL models combined with the classical reconstruction technique of the alternating direction method of multipliers (ADMM) perform a fast and robust image reconstruction, particularly in the scattering medium. The structural similarity indexes of the reconstructed images in scattering media recordings were increased by up to 20% compared with the prevalent iterative models. We also introduce and discuss the challenges of DL approaches for FFMs under physics-informed supervised and unsupervised learning.

Photothermally-Heated Superparamagnetic Polymeric Nanocomposite Implants for Interstitial Thermotherapy

Photothermally-heated polymer-based superparamagnetic nanocomposite (SNC) implants have the potential to overcome limitations of the conventional inductively-heated ferromagnetic metallic alloy implants for interstitial thermotherapy (IT). This paper presents an assessment of a model SNC—poly-dimethylsiloxane (PDMS) and Fe3O4 nanoparticles (MNP)—implant for IT. First, we performed structural and optical characterization of the commercially purchased MNPs, which were added to the PDMS to prepare the SNCs (MNP weight fraction =10 wt.%) that were used to fabricate cubic implants. We studied the structural properties of SNC and characterized the photothermal heating capabilities of the implants in three different media: aqueous solution, cell (in-vitro) suspensions and agarose gel. Our results showed that the spherical MNPs, whose optical absorbance increased with concentration, were uniformly distributed within the SNC with no new bond formed with the PDMS matrix and the SNC implants generated photothermal heat that increased the temperature of deionized water to different levels at different rates, decreased the viability of MDA-MB-231 cells and regulated the lesion size in agarose gel as a function of laser power only, laser power or exposure time and the number of implants, respectively. We discussed the opportunities it offers for the development of a smart and efficient strategy that can enhance the efficacy of conventional interstitial thermotherapy. Collectively, this proof-of-concept study shows the feasibility of a photothermally-heated polymer-based SNC implant technique.

Spectral characterization of liquid hemoglobin phantoms with varying oxygenation states

SIGNIFICANCE

For optical methods to accurately assess hemoglobin oxygen saturation in vivo, an independently verifiable tissue-like standard is required for validation. For this purpose, we propose three hemoglobin preparations and evaluate methods to characterize them.

AIM

To spectrally characterize three different hemoglobin preparations using multiple spectroscopic methods and to compare their absorption spectra to commonly used reference spectra.

APPROACH

Absorption spectra of three hemoglobin preparations in solution were characterized using spectroscopic collimated transmission: whole blood, lysed blood, and ferrous-stabilized hemoglobin. Tissue-mimicking phantoms composed of Intralipid, and the hemoglobin solutions were characterized using spatial frequency-domain spectroscopy (SFDS) and enhanced perfusion and oxygen saturation (EPOS) techniques while using yeast to deplete oxygen.

RESULTS

All hemoglobin preparations exhibited similar absorption spectra when accounting for methemoglobin and scattering in their oxyhemoglobin and deoxyhemoglobin forms, respectively. However, systematic differences were observed in the fitting depending on the reference spectra used. For the tissue-mimicking phantoms, SFDS measurements at the surface of the phantom were affected by oxygen diffusion at the interface with air, associated with higher values than for the EPOS system.

CONCLUSIONS

We show the validity of different blood phantoms and what considerations need to be addressed in each case to utilize them equivalently.

Machine learning estimation of tissue optical properties

Dynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.

Temperature induced changes in the optical properties of skin in vivo

Knowledge of temperature-induced changes of skin optical properties is required for accurate dosimetry of photothermal treatments. We determined and compared in vivo optical properties of mouse ear skin at different temperatures. The diffuse reflectance, total and diffuse transmittance were measured in the spectral range from 400 to 1650 nm using an integrating sphere spectrometer at the temperatures of 25 °C, 36 °C and 60 °C. Target temperatures were attained and maintained using an automated heater equipped with a sensor for feed-back and control. Temperature and temperature induced morphological changes of skin were monitored using an infrared thermal camera and reflectance confocal microscopy, respectively. An inverse Monte Carlo technique was utilized to determine absorption, scattering, and anisotropy factors from the measured quantities. Our results indicate significant differences between the optical properties of skin at different temperatures. Absorption and scattering coefficients increased, whereas anisotropy factors decreased with increasing temperature. Changes in absorption coefficients indicate deoxygenation of hemoglobin, and a blue shift of water absorption bands. Confocal imaging confirmed that our observations can be explained by temperature induced protein denaturation and blood coagulation. Monitoring spectral responses of treated tissue may become a valuable tool for accurate dosimetry of light treatments.

Evaluation of the Returned Electromagnetic Signal from Retro-reflectors in Turbid Media

We provide first-principle theoretical and numerical simulations using the coherent Transfer Matrix Approach (TMA) to describe the behavior of the three main class of the optical beacons namely phase conjugators, reflectors, and retroreflectors within a turbid medium. Our theory describes the extraordinary enhancement (about 5 dB) offered by retroreflectors compared to reflectors in our detailed experiments and shows that they effectively act as local optical phase conjugators. Moreover, the performance of retroreflectors shows little degradation for increased light incident angles in turbid media, while the performance of reflectors degrades drastically. These results may find applications for detection of the echoes of electromagnetic radiation in turbid media.

Design and models of helical needle geometries for core biopsies

Biopsy needles are standard medical devices for extracting biological tissue with the purpose of diagnosing a specific anomaly such as cancerous masses, or lumps. The outcome of these procedures greatly relies on the quality of the samples which, in turn, depends on the forces acting on the needle during its insertion. In this scenario, the design of the needle tip plays a fundamental role in determining the cutting forces. Yet, since the dawn of modern medicine, only a few studies have proposed novel needle tip configurations. In this study, the geometry of biopsy needles is investigated, and helical cutting edges characterized by a three-dimensional (3D) profile are conceived. Mathematical models were formulated to compute the cutting angles and the tissue fracture forces. The proposed methodology is general and can be applied to any 3D needle cutting edge geometry. The utility of the helical geometry was demonstrated on a 14-gauge hollow needle which is generally used during breast biopsies. Experimental insertions were performed at different cutting speeds on phantom tissue. The results show that helical needles generate lower cutting forces than commercial needles and recommendations are formulated for the selection of their cutting parameters. The outcome of this investigation is applicable to biopsy examinations in which a hollow needle is adopted to acquire soft tissue samples.