Exploiting diffuse reflectance measurement uncertainty estimates in spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is a wide-field, noncontact diffuse optical imaging technique that has garnered significant interest for a variety of applications, including the monitoring of skin and breast lesions in clinical settings, and the progression of Alzheimer’s disease and drug delivery to the brain in mouse models. In most applications, diffuse reflectance measurements are used to quantify the optical absorption and reduced scattering coefficients of the turbid medium, and with these, chromophore concentrations of interest are extracted (e.g., hemoglobin in tissue). However, uncertainties in estimated absorption and reduced scattering values are rarely reported, and we know of no method capable of providing such uncertainties when look-up table-based inversion algorithms are used to recover the optical properties. Quantifying these uncertainties would have several important benefits. For example, they could be propagated forward to yield uncertainties in estimated chromophore concentrations, which could have profound implications for the interpretation of experimental results. They could also be employed to help guide the selection of spatial frequencies used for SFDI measurements, given the requirements of the specific application. In this work, we make two novel contributions. First, we show how knowledge of the accuracy of diffuse reflectance measurements from an SFDI instrument (i.e., diffuse reflectance uncertainty estimates) can be transformed to yield quantitative predictions of uncertainties for recovered absorption and reduced scattering values. Second, we use diffuse reflectance uncertainty estimates directly in a new algorithm for the recovery of optical properties. This algorithm performs equivalently to a standard look-up table-based approach but is up to ~200X faster (per pixel). To transform diffuse reflectance uncertainty estimates into uncertainty estimates for the absorption and reduced scattering coefficients, we employ the Cramer-Rao bound (CRB). The CRB is a lower bound that defines the best achievable precision (i.e., lowest variance) of any unbiased estimator for a given data model. It is often used in the statistical signal processing community, especially in the sonar and radar signal processing communities, to perform feasibility studies and system design. We calculate the CRBs for the absorption and reduced scattering coefficients and use them as our estimates of uncertainties for these parameters. We show that these estimates agree with results from Monte Carlo simulations to better than 0.1% for the common scenario where optical properties are computed with a look-up table using two spatial frequencies. We validate our simulations with tissue-mimicking phantom experiments and in vivo measurements on a human volunteer. This method of generating uncertainty estimates opens the door to several exciting possibilities. For example, the analytical form of the CRB calculation can be exploited to quickly generate “maps” of uncertainty estimates as a function of optical properties and spatial frequencies, thereby providing a tool that can be used to efficiently explore this trade space. The CRB-derived uncertainty estimates can also be propagated into chromophore uncertainty estimates. With knowledge of the spatial frequencies and wavelengths used for a given application, it is possible to pre-compute look-up tables of optical property and/or chromophore uncertainty estimates, which would be a significant advantage for applications requiring real-time performance. Diffuse reflectance uncertainty estimates can also be used to speed up optical property recovery with no performance penalty. We have developed a new algorithm to do this that in simulation performs equivalently to a standard look-up table-based approach employing linear interpolation but is up to ~200X faster (per pixel)

Optical sampling depth in the spatial frequency domain.

We present a Monte Carlo (MC) method to determine depth-dependent probability distributions of photon visitation and detection for optical reflectance measurements performed in the spatial frequency domain (SFD). These distributions are formed using an MC simulation for radiative transport that utilizes a photon packet weighting procedure consistent with the two-dimensional spatial Fourier transform of the radiative transport equation. This method enables the development of quantitative metrics for SFD optical sampling depth in layered tissue and its dependence on both tissue optical properties and spatial frequency. We validate the computed depth-dependent probability distributions using SFD measurements in a layered phantom system with a highly scattering top layer of variable thickness supported by a highly absorbing base layer. We utilize our method to establish the spatial frequency-dependent optical sampling depth for a number of tissue types and also provide a general tool to determine such depths for tissues of arbitrary optical properties

Diffuse and nonlinear imaging for in vivo monitoring of structure and function in preclinical tumors

Diffuse Optical Imaging (DOI) technologies provide metabolic and hemodynamic information in tissue in a label-free manner using non-harmful near infrared light. Recently, DOI techniques have received significant interest as a non-invasive functional imaging tool for monitoring patient response to cancer therapies in the clinic. A number of reports have demonstrated that DOI can determine response within hours to weeks from the start of treatment. Despite these promising results, the potential impact, and ultimately adoption of DOI for cancer therapy monitoring in the clinic is limited in part by the lack of knowledge of the cellular, molecular, and biological origins of these clinical observations. Knowledge of the biological underpinnings of DOI response markers is likely to provide clinically relevant insights that can be used to manage and personalize cancer treatment strategies. To this end, the work presented in this dissertation was focused on developing methodology and instrumentation for a novel preclinical imaging technique called Diffuse and Nonlinear Imaging (DNI). DNI combines functional measurements of tumors obtained by wide-field DOI with the underlying tumor biology captured with intravital Multiphoton Microscopy (MPM). Specifically, DNI combines MPM with the DOI technique Spatial Frequency Domain Imaging (SFDI) to provide multiscale datasets of tumor microvascular architecture coregistered within wide-field hemodynamic maps. A procedure was developed to image small animal tumor models with high x-y spatial coregistration accuracy and precision between SFDI and MPM, along with a novel method to match the imaging depths of both modalities by utilizing informed SFDI spatial frequency selection. A preliminary in vivo DNI study of murine mammary tumors revealed multiscale relationships between tumor oxygen saturation and microvessel diameter, and tumor oxygen saturation and microvessel length. Based on these encouraging results, an integrated DNI instrument was then designed and fabricated to acquire tumor vascular structure and function datasets in an inherently spatially coregistered manner from a single system, while simultaneously increasing the sampling resolution of functional spatial heterogeneity. Finally, a small longitudinal study was conducted with the DNI system to explore multiscale relationships between tumor vascular structure and function over space and time in different tumor models and treatment regimens. In summary, the work described in this dissertation resulted in a new method to investigate the relationships between clinically translational DOI hemodynamic markers and MPM metrics of vascular architecture. Ultimately, this work will help to pave a path towards DOI for personalized and precision medicine to significantly impact and inform adaptive therapy strategies tailored to the in vivo state of each patient’s tumor.2021-12-24T00:00:00

Optical sampling depth in the spatial frequency domain.

We present a Monte Carlo (MC) method to determine depth-dependent probability distributions of photon visitation and detection for optical reflectance measurements performed in the spatial frequency domain (SFD). These distributions are formed using an MC simulation for radiative transport that utilizes a photon packet weighting procedure consistent with the two-dimensional spatial Fourier transform of the radiative transport equation. This method enables the development of quantitative metrics for SFD optical sampling depth in layered tissue and its dependence on both tissue optical properties and spatial frequency. We validate the computed depth-dependent probability distributions using SFD measurements in a layered phantom system with a highly scattering top layer of variable thickness supported by a highly absorbing base layer. We utilize our method to establish the spatial frequency-dependent optical sampling depth for a number of tissue types and also provide a general tool to determine such depths for tissues of arbitrary optical properties

Demographic reporting and phenotypic exclusion in fNIRS

Functional near-infrared spectroscopy (fNIRS) promises to be a leading non-invasive neuroimaging method due to its portability and low cost. However, concerns are rising over its inclusivity of all skin tones and hair types (Parker and Ricard, 2022, Webb et al., 2022). Functional NIRS relies on direct contact of light-emitting optodes to the scalp, which can be blocked more by longer, darker, and especially curlier hair. Additionally, NIR light can be attenuated by melanin, which is accounted for in neither fNIRS hardware nor analysis methods. Recent work has shown that overlooking these considerations in other modalities like EEG leads to the disproportionate exclusion of individuals with these phenotypes—especially Black people—in both clinical and research literature (Choy, 2020; Bradford et al., 2022; Louis et al., 2023). In this article, we sought to determine if (Jöbsis, 1977) biomedical optics developers and researchers report fNIRS performance variability between skin tones and hair textures, (2a) fNIRS neuroscience practitioners report phenotypic and demographic details in their articles, and thus, (2b) is a similar pattern of participant exclusion found in EEG also present in the fNIRS literature. We present a literature review of top Biomedical Optics and Human Neuroscience journals, showing that demographic and phenotypic reporting is unpopular in both fNIRS development and neuroscience applications. We conclude with a list of recommendations to the fNIRS community including examples of Black researchers addressing these issues head-on, inclusive best practices for fNIRS researchers, and recommendations to funding and regulatory bodies to achieve an inclusive neuroscience enterprise in fNIRS and beyond

OpenSFDI: an open-source guide for constructing a spatial frequency domain imaging system.

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μa) and reduced scattering (μs') on a pixel-by-pixel basis. Measurements of μa at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, three-wavelength SFDI system capable of quantifying μa and μs' as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system. Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website. Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μa and μs' with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h. Results: The openSFDI systems had an error of 0  ±  6  %   in μa and -2  ±  3  %   in μs', compared to a commercial SFDI system. Bland-Altman analysis revealed the limits of agreement between the two systems to be   ±  0.004  mm  -  1 for μa and -0.06 to 0.1  mm  -  1 for μs'. The openSFDI system had low drift with an average standard deviation of 0.0007  mm  -  1 and 0.05  mm  -  1 in μa and μs', respectively., Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging

OpenSFDI : an open-source guide for constructing a spatial frequency domain imaging system

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μa) and reduced scattering (μ0 s) on a pixelby-pixel basis. Measurements of μa at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, threewavelength SFDI system capable of quantifying μa and μ0 s as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system. Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website. Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μa and μ0 s with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h. Results: The openSFDI systems had an error of 0 6% in μa and −2 3% in μ0 s, compared to a commercial SFDI system. Bland–Altman analysis revealed the limits of agreement between the two systems to be 0.004 mm−1 for μa and −0.06 to 0.1 mm−1 for μ0 s. The openSFDI system had low drift with an average standard deviation of 0.0007 mm−1 and 0.05 mm−1 in μa and μ0 s, respectively. Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging.Funding agencies: U.S. Department of Defense (DoD)United States Department of Defense [W81XWH-15-1-0070]; Knut and Alice Wallenberg FoundationKnut &amp; Alice Wallenberg Foundation; European Research Council under the European UnionEuropean Research Council (ERC) [715737]; Fre</p