Oxygenation measurements are widely used in patient care. However, most clinically available instruments currently consist of contact probes that only provide global monitoring of the patient (e.g., pulse oximetry probes) or local monitoring of small areas (e.g., spectroscopy-based probes). Visualization of oxygenation over large areas of tissue, without a priori knowledge of the location of defects, has the potential to improve patient management in many surgical and critical care applications. In this study, we present a clinically compatible multispectral spatial frequency domain imaging (SFDI) system optimized for surgical oxygenation imaging. This system was used to image tissue oxygenation over a large area (16×12 cm) and was validated during preclinical studies by comparing results obtained with an FDA-approved clinical oxygenation probe. Skin flap, bowel, and liver vascular occlusion experiments were performed on Yorkshire pigs and demonstrated that over the course of the experiment, relative changes in oxygen saturation measured using SFDI had an accuracy within 10% of those made using the FDA-approved device. Finally, the new SFDI system was translated to the clinic in a first-in-human pilot study that imaged skin flap oxygenation during reconstructive breast surgery. Overall, this study lays the foundation for clinical translation of endogenous contrast imaging using SFDI.

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-16/issue-8/086015/First-in-human-pilot-study-of-a-spatial-frequency-domain/10.1117/1.3614566.pdf

First-in-human pilot study of a spatial frequency domain oxygenation imaging system.

The sensitivity to surface profile of non-contact optical imaging, such as spatial frequency domain imaging, may lead to incorrect measurements of optical properties and consequently erroneous extrapolation of physiological parameters of interest. Previous correction methods have focused on calibration-based, model-based, and computation-based approached. We propose an experimental method to correct the effect of surface profile on spectral images. Three-dimensional (3D) phantoms were built with acrylonitrile butadiene styrene (ABS) plastic using an accurate 3D imaging and an emergent 3D printing technique. In this study, our method was utilized for the correction of optical properties (absorption coefficient μa and reduced scattering coefficient μs′) of objects obtained with a spatial frequency domain imaging system. The correction method was verified on three objects with simple to complex shapes. Incorrect optical properties due to surface with minimum 4 mm variation in height and 80 degree in slope were detected and improved, particularly for the absorption coefficients. The 3D phantom-based correction method is applicable for a wide range of purposes. The advantages and drawbacks of the 3D phantom-based correction methods are discussed in details.

Three-dimensional phantoms for curvature correction in spatial frequency domain imaging

A spatial frequency domain imaging (SFDI) system was developed for characterizing ex vivo human ovarian tissue using wide-field absorption and scattering properties and their spatial heterogeneities. Based on the observed differences between absorption and scattering images of different ovarian tissue groups, six parameters were quantitatively extracted. These are the mean absorption and scattering, spatial heterogeneities of both absorption and scattering maps measured by a standard deviation, and a fitting error of a Gaussian model fitted to normalized mean Radon transform of the absorption and scattering maps. A logistic regression model was used for classification of malignant and normal ovarian tissues. A sensitivity of 95%, specificity of 100%, and area under the curve of 0.98 were obtained using six parameters extracted from the SFDI images. The preliminary results demonstrate the diagnostic potential of the SFDI method for quantitative characterization of wide-field optical properties and the spatial distribution heterogeneity of human ovarian tissue. SFDI could be an extremely robust and valuable tool for evaluation of the ovary and detection of neoplastic changes of ovarian cancer.

https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-21/issue-10/101402/Characterizing-optical-properties-and-spatial-heterogeneity-of-human-ovarian-tissue/10.1117/1.JBO.21.10.101402.pdf

Characterizing optical properties and spatial heterogeneity of human ovarian tissue using spatial frequency domain imaging

Multispectral images of skin contain information on the spatial distribution of biological chromophores, such as blood and melanin. From this, parameters such as blood volume and blood oxygenation can be retrieved using reconstruction algorithms. Most such approaches use some form of pixelwise or volumetric reconstruction code. We explore the use of principal component analysis (PCA) of multispectral images to access blood volume and blood oxygenation in near real time. We present data from healthy volunteers under arterial occlusion of the forearm, experiencing ischemia and reactive hyperemia. Using a two-layered analytical skin model, we show reconstruction results of blood volume and oxygenation and compare it to the results obtained from our new spectral analysis based on PCA. We demonstrate that PCA applied to multispectral images gives near equivalent results for skin chromophore mapping and quantification with the advantage of being three orders of magnitude faster than the reconstruction algorithm.

Principal component model of multispectral data for near real-time skin chromophore mapping

Diffusion geometry techniques are useful to classify patterns and visualize high-dimensional datasets. Building upon ideas from diffusion geometry, we outline our mathematical foundations for learning a function on high-dimension biomedical data in a local fashion from training data. Our approach is based on a localized summation kernel, and we verify the computational performance by means of exact approximation rates. After these theoretical results, we apply our scheme to learn early disease stages in standard and new biomedical datasets.

/pdf/locally-learning-biomedical-data-using-diffusion-frames-1sjh1pbsny.pdf

Locally Learning Biomedical Data Using Diffusion Frames

Direct Curvature Correction for Non-Contact Imaging Modalities-Applied to Multi-Spectral Imaging

In this paper we discuss results based on using instrumental motion as a signal rather than treating it as noise in Near Infra-Red (NIR) imaging. As a practical application to demonstrate this approach we show the design of a novel NIR hematoma detection device. The proposed device is based on a simplified single source configuration with a dual separation detector array and uses motion as a signal for detecting changes in blood volume in the dural regions of the head. The rapid triage of hematomas in the emergency room will lead to improved use of more sophisticated/expensive imaging facilities such as CT/MRI units. We present simulation results demonstrating the viability of such a device and initial phantom results from a proof of principle device. The results demonstrate excellent localization of inclusions as well as good quantitative comparisons.

A hematoma detector—a practical application of instrumental motion as signal in near infra-red imaging

This research describes a non-invasive, non-contact method used to quantitatively analyze the functional characteristics of tissue. Multi-spectral images collected at several near-infrared wavelengths are input into a mathematical optical skin model that considers the contributions from different analytes in the epidermis and dermis skin layers. Through a reconstruction algorithm, we can quantify the percent of blood in a given area of tissue and the fraction of that blood that is oxygenated. Imaging normal tissue confirms previously reported values for the percent of blood in tissue and the percent of blood that is oxygenated in tissue and surrounding vasculature, for the normal state and when ischemia is induced. This methodology has been applied to assess vascular Kaposi's sarcoma lesions and the surrounding tissue before and during experimental therapies. The multi-spectral imaging technique has been combined with laser Doppler imaging to gain additional information. Results indicate that these techniques are able to provide quantitative and functional information about tissue changes during experimental drug therapy and investigate progression of disease before changes are visibly apparent, suggesting a potential for them to be used as complementary imaging techniques to clinical assessment.

/pdf/using-non-invasive-multi-spectral-imaging-to-quantitatively-1ejps6bqgx.pdf

Chernomordik

Papers

Direct Curvature Correction for Non-Contact Imaging Modalities-Applied to Multi-Spectral Imaging

A hematoma detector—a practical application of instrumental motion as signal in near infra-red imaging

Using Non-Invasive Multi-Spectral Imaging to Quantitatively Assess Tissue Vasculature