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Open Access Publications from the University of California

Scholarly Works (12 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2018)
    Cover page: Method using in vivo quantitative spectroscopy to guide design and optimization of low-cost, compact clinical imaging devices: emulation and evaluation of multispectral imaging systemsCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2016)
    We present a method for low-cost fabrication of polydimethylsiloxane (PDMS) tissue simulating phantoms with tunable scattering spectra, spanning visible, and near-infrared regimes. These phantoms use optical polishing agents (aluminum oxide powders) at various grit sizes to approximate in vivo tissue scattering particles across multiple size distributions (range: 17 to 3  μm). This class of tunable scattering phantoms is used to mimic distinct changes in wavelength-dependent scattering properties observed in tissue pathologies such as partial thickness burns. Described by a power-law dependence on wavelength, the scattering magnitude of these phantoms scale linearly with particle concentration over a physiologic range [μs′=(0.5 to 2.0  mm−1)] whereas the scattering spectra, specific to each particle size distribution, correlate to distinct exponential coefficients (range: 0.007 to 0.32). Aluminum oxide powders used in this investigation did not detectably contribute to the absorption properties of these phantoms. The optical properties of these phantoms are verified through inverse adding-doubling methods and the tolerances of this fabrication method are discussed.
      Cover page: Low-cost tissue simulating phantoms with adjustable wavelength-dependent scattering properties in the visible and infrared rangesCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2018)
      With recent proliferation in compact and/or low-cost clinical multispectral imaging approaches and commercially available components, questions remain whether they adequately capture the requisite spectral content of their applications. We present a method to emulate the spectral range and resolution of a variety of multispectral imagers, based on in-vivo data acquired from spatial frequency domain spectroscopy (SFDS). This approach simulates spectral responses over 400 to 1100 nm. Comparing emulated data with full SFDS spectra of in-vivo tissue affords the opportunity to evaluate whether the sparse spectral content of these imagers can (1) account for all sources of optical contrast present (completeness) and (2) robustly separate and quantify sources of optical contrast (crosstalk). We validate the approach over a range of tissue-simulating phantoms, comparing the SFDS-based emulated spectra against measurements from an independently characterized multispectral imager. Emulated results match the imager across all phantoms (<3  %   absorption, <1  %   reduced scattering). In-vivo test cases (burn wounds and photoaging) illustrate how SFDS can be used to evaluate different multispectral imagers. This approach provides an in-vivo measurement method to evaluate the performance of multispectral imagers specific to their targeted clinical applications and can assist in the design and optimization of new spectral imaging devices.
        Cover page: Method using in vivo quantitative spectroscopy to guide design and optimization of low-cost, compact clinical imaging devices: emulation and evaluation of multispectral imaging systems
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2019)
        While clinical examination is needed for burn severity diagnosis, several emerging technologies aim to quantify this process for added objectivity. Accurate assessments become easier after burn progression, but earlier assessments of partial thickness burn depth could lead to earlier excision and grafting and subsequent improved healing times, reduced rates of scarring/infection, and shorter hospital stays. Spatial Frequency Domain Imaging (SFDI), Laser Speckle Imaging (LSI) and thermal imaging are three non-invasive imaging modalities that have some diagnostic ability for noninvasive assessment of burn severity, but have not been compared in a controlled experiment. Here we tested the ability of these imaging techniques to assess the severity of histologically confirmed graded burns in a swine model. Controlled, graded burn wounds, 3cm in diameter were created on the dorsum of Yorkshire pigs (n=3, 45-55kg) using a custom-made burn tool that ensures consistent pressure has been employed by various burn research groups. For each pig, a total of 16 burn wounds were created on the dorsal side. Biopsies were taken for histological analysis to verify the severity of the burn. Clinical analysis, SFDI, LSI and thermal imaging were performed at 24 and 72h after burn to assess the accuracy of each imaging technique. In terms of diagnostic accuracy, using histology as a reference, SFDI (85%) and clinical analysis (83%) performed significantly better that LSI (75%) and thermography (73%) 24h after the burn. There was no statistically significant improvement from 24 to 72h across the different imaging modalities. These data indicate that these imaging modalities, and specifically SFDI, can be added to the burn clinicians' toolbox to aid in early assessment of burn severity.
          Cover page: Evaluating clinical observation versus Spatial Frequency Domain Imaging (SFDI), Laser Speckle Imaging (LSI) and thermal imaging for the assessment of burn depth
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2019)
          Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have been used to examine tissue properties to infer burn severity. Spatial frequency-domain imaging (SFDI) has also been used to characterize burns based on the relationships between histologic observations and changes in tissue properties. Recently, machine learning has been used to classify burns by combining optical features from multispectral or hyperspectral imaging. Rather than employ models of light propagation to deduce tissue optical properties, we investigated the feasibility of using SFDI reflectance data at multiple spatial frequencies, with a support vector machine (SVM) classifier, to predict severity in a porcine model of graded burns. Calibrated reflectance images were collected using SFDI at eight wavelengths (471 to 851 nm) and five spatial frequencies (0 to 0.2  mm  -  1). Three models were built from subsets of this initial dataset. The first subset included data taken at all wavelengths with the planar (0  mm  -  1) spatial frequency, the second comprised data at all wavelengths and spatial frequencies, and the third used all collected data at values relative to unburned tissue. These data subsets were used to train and test cubic SVM models, and compared against burn status 28 days after injury. Model accuracy was established through leave-one-out cross-validation testing. The model based on images obtained at all wavelengths and spatial frequencies predicted burn severity at 24 h with 92.5% accuracy. The model composed of all values relative to unburned skin was 94.4% accurate. By comparison, the model that employed only planar illumination was 88.8% accurate. This investigation suggests that the combination of SFDI with machine learning has potential for accurately predicting burn severity.
            Cover page: Burn wound classification model using spatial frequency-domain imaging and machine learning
            • Article
            • Peer Reviewed
            ICTS Publications (2017)

            The current standard for diagnosis of burn severity and subsequent wound healing is through clinical examination, which is highly subjective. Several new technologies are shifting focus to burn care in an attempt to help quantify not only burn depth but also the progress of healing. While accurate early assessment of partial thickness burns is critical for dictating the course of treatment, the ability to quantitatively monitor wound status over time is critical for understanding treatment efficacy. SFDI and LSI are both non-invasive imaging modalities that have been shown to have great diagnostic value for burn severity, but have yet to be tested over the course of wound healing.

            In this study, a hairless rat model (n = 6, 300-450 g) was used with a four pronged comb to create four identical partial thickness burns (superficial n = 3 and deep n = 3) that were used to monitor wound healing over a 28 days period. Weekly biopsies were taken for histological analysis to verify wound progression. Both SFDI and LSI were performed weekly to track the evolution of hemodynamic (blood flow and oxygen saturation) and structural (reduced scattering coefficient) properties for the burns.

            LSI showed significant changes in blood flow from baseline to 220% in superficial and 165% in deep burns by day 7. In superficial burns, blood flow returned to baseline levels by day 28, but not for deep burns where blood flow remained elevated. Smaller increases in blood flow were also observed in the surrounding tissue over the same time period. Oxygen saturation values measured with SFDI showed a progressive increase from baseline values of 66-74% in superficial burns and 72% in deep burns by day 28. Additionally, SFDI showed significant decreases in the reduced scattering coefficient shortly after the burns were created. The scattering coefficient progressively decreased in the wound area, but returned towards baseline conditions at the end of the 28 days period. Scattering changes in the surrounding tissue remained constant despite the presence of hemodynamic changes.

            Here, we show that LSI and SFDI are capable of monitoring changes in hemodynamic and scattering properties in burn wounds over a 28 days period. These results highlight the potential insights that can be gained by using non-invasive imaging technologies to study wound healing. Further development of these technologies could be revolutionary for wound monitoring and studying the efficacy of different treatments. Lasers Surg. Med. 49:293-304, 2017. © 2017 Wiley Periodicals, Inc.

              Cover page: Quantitative long-term measurements of burns in a rat model using Spatial Frequency Domain Imaging (SFDI) and Laser Speckle Imaging (LSI).Creative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2019)
              Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have been used to examine tissue properties to infer burn severity. Spatial frequency-domain imaging (SFDI) has also been used to characterize burns based on the relationships between histologic observations and changes in tissue properties. Recently, machine learning has been used to classify burns by combining optical features from multispectral or hyperspectral imaging. Rather than employ models of light propagation to deduce tissue optical properties, we investigated the feasibility of using SFDI reflectance data at multiple spatial frequencies, with a support vector machine (SVM) classifier, to predict severity in a porcine model of graded burns. Calibrated reflectance images were collected using SFDI at eight wavelengths (471 to 851 nm) and five spatial frequencies (0 to 0.2  mm  -  1). Three models were built from subsets of this initial dataset. The first subset included data taken at all wavelengths with the planar (0  mm  -  1) spatial frequency, the second comprised data at all wavelengths and spatial frequencies, and the third used all collected data at values relative to unburned tissue. These data subsets were used to train and test cubic SVM models, and compared against burn status 28 days after injury. Model accuracy was established through leave-one-out cross-validation testing. The model based on images obtained at all wavelengths and spatial frequencies predicted burn severity at 24 h with 92.5% accuracy. The model composed of all values relative to unburned skin was 94.4% accurate. By comparison, the model that employed only planar illumination was 88.8% accurate. This investigation suggests that the combination of SFDI with machine learning has potential for accurately predicting burn severity.
                Cover page: Burn wound classification model using spatial frequency-domain imaging and machine learning.Creative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2019)
                There is a need for noninvasive, quantitative methods to characterize wound healing in the context of longitudinal investigations related to regenerative medicine. Such tools have the potential to inform the assessment of wound status and healing progression and aid the development of new treatments. We employed spatial frequency domain imaging (SFDI) to characterize the changes in optical properties of tissue during wound healing progression in a porcine model of split-thickness skin grafts and also in a model of burn wound healing with no graft intervention. Changes in the reduced scattering coefficient measured using SFDI correlated with structural changes reported by histology of biopsies taken concurrently. SFDI was able to measure spatial inhomogeneity in the wounds and predicted heterogeneous healing. In addition, we were able to visualize differences in healing rate, depending on whether a wound was debrided and grafted, versus not debrided and left to heal without intervention apart from topical burn wound care. Changes in the concentration of oxy- and deoxyhemoglobin were also quantified, giving insight into hemodynamic changes during healing.
                  Cover page: Spatial frequency domain imaging: a quantitative, noninvasive tool for in vivo monitoring of burn wound and skin graft healing.Creative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2019)
                  There is a need for noninvasive, quantitative methods to characterize wound healing in the context of longitudinal investigations related to regenerative medicine. Such tools have the potential to inform the assessment of wound status and healing progression and aid the development of new treatments. We employed spatial frequency domain imaging (SFDI) to characterize the changes in optical properties of tissue during wound healing progression in a porcine model of split-thickness skin grafts and also in a model of burn wound healing with no graft intervention. Changes in the reduced scattering coefficient measured using SFDI correlated with structural changes reported by histology of biopsies taken concurrently. SFDI was able to measure spatial inhomogeneity in the wounds and predicted heterogeneous healing. In addition, we were able to visualize differences in healing rate, depending on whether a wound was debrided and grafted, versus not debrided and left to heal without intervention apart from topical burn wound care. Changes in the concentration of oxy- and deoxyhemoglobin were also quantified, giving insight into hemodynamic changes during healing.
                    Cover page: Spatial frequency domain imaging: a quantitative, noninvasive tool for in vivo monitoring of burn wound and skin graft healing
                    • Article
                    • Peer Reviewed
                    UC Irvine Previously Published Works (2020)
                    While visual assessment by a clinician is the standard of care for burn severity evaluations, new technologies at various stages of development are attempting to add objectivity to this practice by quantifying burn severity. Assessment accuracy generally improves after the burn injury has progressed, but early assessments that correctly identify superficial partial and deep partial burns have the potential to lead to more prompt treatments and shorter recovery times. To date, Spatial Frequency Domain Imaging (SFDI) has only been used in animal models of burns, but has shown the potential to categorize burns accurately at earlier time points. Here we examine the potential for SFDI to assess burn severity in clinical patients. We also utilize Laser Speckle Imaging (LSI), an FDA cleared non-invasive imaging technology that typically measures blood perfusion in order to evaluate burns in clinical patients. We present a case series of two patients, both with partial thickness burns of varying severity. Partial thickness burns are often difficult for clinicians to categorize based on visual appearance alone. SFDI and LSI were both performed on each patient at approximately 24 and 72 h after their respective burn incidents. Each technique was able to render spatially resolved information that enabled improved assessment accuracy for each burn. This represents the first publication of SFDI applied to clinical burn patients after being successfully utilized in animal models, and highlights the potential for SFDI as a feasible tool for the timely categorization of burn severity.
                      Cover page: Spatial Frequency Domain Imaging (SFDI) of clinical burns: A case report
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