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

Cylindrical illumination with angular coupling for whole-prostate photoacoustic tomography

Current diagnosis of prostate cancer relies on histological analysis of tissue samples acquired by biopsy, which could benefit from real-time identification of suspicious lesions. Photoacoustic tomography has the potential to provide real-time targets for prostate biopsy guidance with chemical selectivity, but light delivered from the rectal cavity has been unable to penetrate to the anterior prostate. To overcome this barrier, a urethral device with cylindrical illumination is developed for whole-prostate imaging, and its performance as a function of angular light coupling is evaluated with a prostate-mimicking phantom

Diffuse optical spectroscopic imaging reveals distinct early breast tumor hemodynamic responses to metronomic and maximum tolerated dose regimens.

BACKGROUND:Breast cancer patients with early-stage disease are increasingly administered neoadjuvant chemotherapy (NAC) to downstage their tumors prior to surgery. In this setting, approximately 31% of patients fail to respond to therapy. This demonstrates the need for techniques capable of providing personalized feedback about treatment response at the earliest stages of therapy to identify patients likely to benefit from changing treatment. Diffuse optical spectroscopic imaging (DOSI) has emerged as a promising functional imaging technique for NAC monitoring. DOSI uses non-ionizing near-infrared light to provide non-invasive measures of absolute concentrations of tissue chromophores such as oxyhemoglobin. In 2011, we reported a new DOSI prognostic marker, oxyhemoglobin flare: a transient increase in oxyhemoglobin capable of discriminating NAC responders within the first day of treatment. In this follow-up study, DOSI was used to confirm the presence of the flare as well as to investigate whether DOSI markers of NAC response are regimen dependent. METHODS:This dual-center study examined 54 breast tumors receiving NAC measured with DOSI before therapy and the first week following chemotherapy administration. Patients were treated with either a standard of care maximum tolerated dose (MTD) regimen or an investigational metronomic (MET) regimen. Changes in tumor chromophores were tracked throughout the first week and compared to pathologic response and treatment regimen at specific days utilizing generalized estimating equations (GEE). RESULTS:Within patients receiving MTD therapy, the oxyhemoglobin flare was confirmed as a prognostic DOSI marker for response appearing as soon as day 1 with post hoc GEE analysis demonstrating a difference of 48.77% between responders and non-responders (p < 0.0001). Flare was not observed in patients receiving MET therapy. Within all responding patients, the specific treatment was a significant predictor of day 1 changes in oxyhemoglobin, showing a difference of 39.45% (p = 0.0010) between patients receiving MTD and MET regimens. CONCLUSIONS:DOSI optical biomarkers are differentially sensitive to MTD and MET regimens at early timepoints suggesting the specific treatment regimen should be considered in future DOSI studies. Additionally, DOSI may help to identify regimen-specific responses in a more personalized manner, potentially providing critical feedback necessary to implement adaptive changes to the treatment strategy

Frequency domain diffuse optical tomography with a single source and detector via high- speed hypocycloid scanning

Diffuse Optical Imaging (DOI) relies on the fact that near infrared light (600-1000 nm) is strongly scattered in biological tissue, and weakly absorbed by tissue chromophores such as blood, fat, water, and melanin. In frequency domain DOI, intensity modulated light is introduced into the tissue and the observed absorption and phase changes enable absolute concentrations of these chromophores to be calculated. These concentrations provide valuable insight into tissue metabolic activity that have proven useful for a variety of clinical outcomes from exercise physiology to predicting tumor response to treatment. Diffuse Optical Tomography (DOT) is an extension of DOI that allows three dimensional reconstruction of tissue chromophore concentrations. Typically, DOT requires a large number (~10-100) of light sources and detectors to collect the data necessary for 3D reconstruction. In these systems, each source and detector pair probes a specific volume of tissue and an algorithm is used to reconstruct tissue chromophore concentration within each voxel. However, the use of large numbers of fibers results in imaging systems that are large, expensive, unwieldy, and often anatomically specific (i.e. systems are constructed for breast measurements and cannot be easily used on other anatomical locations). In this poster I will present a new method for DOT that uses a single source and detector fiber in a potentially hand-held format that is able to probe a large volume of tissue using rapid scanning of each fiber in a hypocycloid pattern. Please click Additional Files below to see the full abstract

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)

A combined frequency domain near infrared spectroscopy and diffuse correlation spectroscopy system for monitoring the sternocleidomastoid muscle

Please click Additional Files below to see the full abstract

Structured light imaging mesoscopy: detection of embedded morphological changes in superficial tissues.

SignificanceCurrent paradigms for the optical characterization of layered tissues involve explicit consideration of an inverse problem which is often ill-posed and whose resolution may retain significant uncertainty. Here, we present an alternative approach, structured light imaging mesoscopy (SLIM), that leverages the inherent sensitivity of raw spatial frequency domain (SFD) reflectance measurements for the detection of embedded subsurface scattering changes in tissue.AimWe identify wavelength-spatial frequency ( λ-fx ) combinations that provide optimal sensitivity of SFD reflectance changes originating from scattering changes in an embedded tissue layer. We specifically consider the effects of scattering changes in the superficial dermis which is a key locus of pathology for diverse skin conditions such as cancer, aging, and scleroderma.ApproachWe used Monte Carlo simulations in a four-layer skin model to analyze the SFD reflectance changes resulting from changes in superficial dermal scattering across wavelength ( λ=471 to 851 nm) and spatial frequency ( fx=0 to 0.5/mm). Within this model, we consider different values for epidermal melanin concentration to simulate variations in skin tone.ResultsMonte Carlo simulations revealed that scattering changes within the superficial dermis produce SFD reflectance changes which are maximized at specific ( λ-fx ) pairs and vary with skin tone. For light skin tones, SFD reflectance changes due to scattering reductions in the superficial dermis are maximized at λ=621  nm and spatial frequency fx≈0.33/mm . By contrast, for darker skin tones, maximal SFD reflectance changes occur at wavelengths in the near-infrared ( λ≥811  nm ) at a spatial frequency of fx≈0.25/mm . Interestingly, the change in SFD reflectance produced by such scattering changes is most uniform across all skin tones when using the longest wavelength tested ( λ=851  nm ) and a spatial frequency of fx≈0.22/mm . Taken together, our computational model identifies specific ( λ-fx ) pairs to optimally detect embedded structural alterations in the superficial dermis.ConclusionsThe findings establish the SLIM methodology as a means to detect morphological changes in an embedded subsurface tissue layer by leveraging inherent sensitivities of spatial frequency domain reflectance. This approach promises to enable simplified clinical tracking of subsurface microstructural alterations without the explicit need to consider an inverse problem approach

Structured light imaging mesoscopy for detection of embedded morphological changes in superficial tissues

This study introduces Structured Light Imaging Mesoscopy (SLIM), a novel non-contact optical method for detecting subsurface morphological tissue alterations. By leveraging the inherent sensitivity of spatial frequency domain (SFD) reflectance measurements, SLIM identifies specific wavelength-spatial frequency combinations that optimize the detection of scattering changes in the superficial dermis, a key area for various skin conditions. Monte Carlo simulations across a range of skin tones revealed that these optimal combinations vary with melanin concentration. Specifically, in subjects with lighter skin tones optimal sensitivity is achieved using shorter wavelengths and higher spatial frequencies, while for darker skin tones longer wavelengths and lower spatial frequencies are preferred. This approach simplifies clinical tracking of subsurface microstructural changes by eliminating the need for complex inverse problem solving, enabling rapid data acquisition and minimal processing