This PDF file contains the front matter associated with SPIE Proceedings Volume 11974, including the Title Page, Copyright information, and Table of Contents.

In the context of optical biopsy for the diagnosis of skin carcinoma, spatially resolved diffuse reflectance (SR-DR) spectroscopy is widely used to discern healthy from lesional tissues. The estimation of diagnostically relevant optical properties by means of inverse problem solving is one way to exploit the acquired clinical spectra. This method requires the comparison between the latter spectra collected with a medical device (MD), and the ones generated by the photons transport numerical simulations. However, this comparison is typically limited to shape comparison (spectra are normalized before a term-by-term comparison) due to non-standardization of the experimental DR spectra, for which magnitude depends on the multifiber optics probe geometry and on a preliminary calibration measurement performed on a spectralon DR standard illuminated at a given distance. This study proposes to establish a corrective factor to overcome this dependence, and thus obtain clinical spectra whose intensity unit is identical to the simulated ones, i.e., the ratio between photons sent by the emitting fiber and captured by the collecting fibers. The photometric calculations leading to a theoretical value of this factor for various calibration measurement geometries are presented. Experimental validations performed on optical phantoms (with optical properties confirmed from double integrating sphere measurements) using an existing SR-DR MD reveal encouraging fitting between experimental and simulated calculation of such corrective factor. Those results highlight the interest of the method for the standardization of clinically acquired DR spectra i.e. their comparison in terms of absolute magnitudes.

A recent theoretical framework using power-law functions was proposed to model scattering from biological tissues in ultrasound and optical coherence tomography. Multi-scale scattering sites such as the fractal branching vasculature will then contribute to power-law based probability distributions of speckle statistics. These distributions are the Burr type XII distribution, the Lomax distribution, and the generalized logistic distribution for speckle amplitude, intensity, and log amplitude, respectively. Previous experiments with ultrasound and optical coherence tomography demonstrate that these distributions are better fits to image histogram data of various biological tissues when compared with classical models (e.g., Rayleigh, K, and gamma distributions). Of critical importance is that this framework provides novel parameters, most notably the power-law exponent parameter, for characterizing the physics of scattering from soft tissue. The typical range for the exponent parameter in other normal tissues is approximately 3 to 6. The aim is for this parameter to be used as a new biomarker for diagnostic imaging, sensitive to changes in tissue structures. Here, we demonstrate a specific application to mouse brain tissue, in which the exponent parameter is used to characterize mouse cortical brain under various conditions including ex vivo and in vivo using optical coherence tomography.

Line-field confocal optical coherence tomography (LC-OCT) is an imaging technique that combines the principles of time-domain OCT and reflectance confocal microscopy (RCM). LC-OCT was designed to generate threedimensional (3D) morphological images of the skin, in vivo, with a spatial resolution of ∼ 1 μm. As in OCT and RCM, LC-OCT image contrast originates from the backscattering of incident light by the sample microstructures, which is determined by the optical scattering properties of the sample, namely the scattering coefficient μs and the scattering anisotropy parameter g. When imaging biological tissues, these properties can provide insight into tissue organization and structure, and could be used for quantitative tissue characterization in vivo. We present a method for obtaining spatially-resolved measurements of optical scattering parameters from LC-OCT images. Our approach is based on a calibration using a test sample with known optical scattering properties and on the application of a theoretical model previously developed for focus-tracking mode OCT and RCM. Assuming a single-scattering regime, this model allows to derive the optical scattering parameters μs and g from the intensity depth profiles acquired by LC-OCT. Spatially-resolved measurements are achieved by dividing the 3D LC-OCT image into “macro-voxels” and analyzing the different sample layers separately, leading to 3D distributions of μs and g. This method was experimentally tested against integrating spheres and collimated transmission measurements and validated on a set of mono- and bi-layered scattering phantoms.

Oblique plane microscopy (OPM) is a powerful tool for monitoring biological processes due to its capability for highresolution, rapid, optically-sectioned imaging through a single objective lens. Recently, our group has demonstrated scattering-contrast OPM (sOPM) as a technique to image blood cells in situ and in vivo. In order to classify blood cells visualized with sOPM, scattering data could be further leveraged and better understood. We present here a visualization and analysis of the scattering signal by masking and imaging the final Fourier plane of the sOPM system. We demonstrate the angular distribution of the scattering signal and image with several aperture masks. Microsphere phantoms are imaged in the image plane and Fourier plane to demonstrate the change in scattering behavior for Mie scatterers with large (4 micron) diameters and small (190 nm), Rayleigh-like scatterers similar to subcellular features such as granules. Circular apertures were used to isolate the side scattering centered at 90 degrees compared to the angular extremes. A Michelson contrast of 0.20-0.25 was observed for 4 micron diameter spheres and 0.05-0.10 for 190 nm diameter spheres using a split aperture. Microsphere sizes are classified from images using split aperture contrast and confirmed by fluorescence. Leveraging differential scattering angle contrast will enable the visual classification of blood cells, particularly white blood cells where granules and other organelles present distinct side scattering signals. Finally, the quantitative nature of the differential scattering angle contrast may enable machine-learning based classification and cell counting.

The COVID-19 pandemic has caused a marked disruption in the delivery of medical care, resulting in significant negative consequences for patients. Considering Covid-19 spreads primarily through expelled respiratory droplets, the ability to detect and measure droplets is critical to the development of clinical protective practices. However, most available methods are either unsuitable for the clinical setting, or cannot distinguish solid particles from liquid droplets. We developed a robust and portable optical instrument capable of measuring the size and quantity of droplets generated during medical procedures. Here we outline the system design and describe our preclinical measurements, which showed that surgical masks significantly reduce the number of expelled speech droplets.

Surgical site infection is a significant threat to the patient after surgery and is associated with over a third of postoperative deaths. In this work we evaluate spatial frequency domain imaging (SFDI) for clinical use in monitoring wound structural changes and early signs of infection. We use both Monte Carlo and experimental techniques to image embedded vertical wound models. The simulation aids in analyzing heterogeneity between tissues with different optical parameters to detect changes due to infection. The exact resolution of SFDI measurement for scattering and wound width will be discussed.

Spatially resolved diffuse reflectance spectroscopy (SR-DRS) is a widely studied optical biopsy technique to investigate and to diagnose skin tissue modifications due to pathologies such as cancers. One way to exploit clinical spectra acquired with a SR-DRS medical device consists in estimating diagnostically relevant skin optical properties that is, by solving an inverse problem based on numerical simulations to generate spectra in accordance with the technical and geometrical features of the latter device. For realistic multi-layer skin media, the simultaneous estimation of layer-wise optical properties of interest is quite challenging (difficulty of convergence or non-unicity of the solution) and time consuming, especially for one or several parameters to be estimated in more than three layers of a skin model. To tackle this problem, the work presented here proposes an improved inverse problem solving scheme, which (i) sequentially determines the parameters of interest, layer by layer, in a 5-layer skin model using (ii) a custom cost-function adapted to the layered structure of the skin, i.e. considering wavelength and source-detector distance sensitivity to each layer. In-silico validation of the proposed approach was performed through convergence analysis towards ground truth simulated spectra. Using this sequential approach, the values of a 4-parameters vector were estimated with a relative errors of a few percent only and three times faster compared to current optimization method. Moreover, it brings morphological and physiological dimension to the inverse problem solving.

Dynamic fluorescence diffuse optical tomography (FDOT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. Based on a two-compartmental model, the adaptive extended Kalman filtering (EKF)for dynamic FDOT is proposed in our previous work to obtain better estimation than the conventional EKF. However, the set of the measurement error covariance matrix still affect the performance of the adaptive-EKF that is always manually adjusted. In order to quickly and accurately obtain the measurement covariance matrix, this paper uses generalized regression neural network (GRNN) to predict the value in different measurements as the initial covariance value of the adaptive-EKF. The simulation results have proved the validity of the measurement covariance matrix predicted by GRNN, and the images of fluorescence pharmacokinetic parameters can be achieved excellently using the adaptive-EKF and GRNN-learning.

Time domain diffuse optics (TD-DO) relies on the injection of ps laser pulses and on the collection of the arrival times of scattered photons. To reach the ultimate limits of the technique (allowing to investigate even structures at depth <5 cm), a large area detector is needed. To this extent, we realized and present a new silicon photomultiplier featuring a 1 cm2 area. To the best of our knowledge, it represents the largest detector ever proposed for TD-DO and shows a light harvesting capability which is more than 1 decade larger than the state-of-the-art technology system. To assess its suitability for TDDO measurements, we tested the detector with several procedures from shared protocols (BIP, nEUROPt and MEDPHOT). However, the light harvesting capability of a detector with large area can be proficiently exploited only if coupled to timing electronics working in sustained count-rate CR (i.e., well above the single photon statistics). For this reason, we study the possibility to work in a regime where (even more than) one photon per laser pulse is detected (i.e., more than 100% laser repetition rate) exploiting in-silico technology. The results show that the possibility to use sustained count-rate represents a dramatic improvement in the number of photons detected with respect to current approaches (where count-rate of 1-5% of the laser repetition rate are used) without significant losses in the measurement accuracy. This represents a new horizon for TD-DO measurements, opening the way to new applications (e.g., optical investigation of the lung or monitoring of fast dynamics never studied before).

Diffuse reflectance spectroscopy (DRS) is a well-established technology for quantitative, non-invasive, and rapid measurement of tissue hemodynamics. We used DRS scans collected between 450-700 nm using a single-channel fiber optical probe in hydrocephalus pediatric patients with implanted shunts to investigate whether idiopathic pain experienced at the shunt site was identifiable in hemodynamic signatures from measurements. The relationship between the presence of shunt-related pain by were examined using localized DRS measurements using an optical probe with short source-detector separation. DRS scans were collected at seven different sites around the implanted shunt and from a matched contralateral site, from subjects during routine clinical visits. Reflectance data were processed using an inverse Monte Carlo model to translate each DRS spectrum into the wavelength averaged scattering coefficient, the total hemoglobin concentration, and vascular blood oxygen saturation. DRS variables from acquired measurements were examined across patient groups using statistical t-tests. Preliminary results indicate a reduced hemoglobin saturation for subjects presenting with shuntodynia and need to be examined in greater detail to identify whether these optical signals indicate early-development of pressure injuries in tissues.

Time-resolved diffuse optical spectroscopy (TRDOS) provides a method for directly and independently determining the optical transport coefficients of turbid media. A multispectral, gated TRDOS system was built using a supercontinuum laser source and a fast single photon avalanche photodiode (SPAD) for detection. Electronic time-gating of the SPAD allowed for detection of time-gated photon distributions, and showed an increase of nearly 80x in dynamic range relative to ungated detection. TRDOS measurements using both ungated and gated detection schemes on two-layer tissue mimicking phantoms were acquired. The distribution of time-of-flight (DTOF) of photons was measured from a two-layered tissue simulating phantom at multiple source-detector separations and wavelengths. Measured DTOFs were matched to predictions from diffusion-theory (DT) for two-layered media after numerical convolution with measured instrument response functions. We show a dependence of the two-layer DT model on the input values of the upper layer thickness and refractive indices. It was found that both the upper layer thickness and refractive index parameters must well-determined for DT predictions to match measurements in two-layer media.