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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.

In breast cancer diagnosis and therapy monitoring, there is a need for frequent, noninvasive disease progression evaluation. Breast tumors differ from healthy tissue in mechanical stiffness as well as optical properties, which allows optical methods to detect and monitor breast lesions noninvasively. Spatial frequency-domain imaging (SFDI) is a reflectance-based diffuse optical method that can yield two-dimensional images of absolute optical properties of tissue with an inexpensive and portable system, although depth penetration is limited. Since the absorption coefficient of breast tissue is relatively low and the tissue is quite flexible, there is an opportunity for compression of tissue to bring stiff, palpable breast lesions within the detection range of SFDI. Sixteen breast tissue-mimicking phantoms were fabricated containing stiffer, more highly absorbing tumor-mimicking inclusions of varying absorption contrast and depth. These phantoms were imaged with an SFDI system at five levels of compression. An increase in absorption contrast was observed with compression, and reliable detection of each inclusion was achieved when compression was sufficient to bring the inclusion center within ∼12 mm of the phantom surface. At highest compression level, contrasts achieved with this system were comparable to those measured with single source–detector near-infrared spectroscopy. [ABSTRACT FROM AUTHOR]

Significance: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. Aim: We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images. Approach: OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by comparing it with SSOP and a two-step hybrid technique that uses a previously developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation. Results: When tested on human feet, cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Finally, we optimize OxyGAN inference so that oxygenation maps are computed ∼10 times faster than previous work, enabling video-rate, 25-Hz imaging. Conclusions: Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications. [ABSTRACT FROM AUTHOR]

We present the effects of using a single-pixel camera approach to extract optical properties with the single-snapshot spatial frequency-domain imaging method. We acquired images of a human hand for spatial frequencies ranging from 0.1 to 0.4 mm − 1 with increasing compression ratios using adaptive basis scan wavelet prediction strategy. In summary, our findings indicate that the extracted optical properties remained usable up to 99% of compression rate at a spatial frequency of 0.2 mm − 1 with errors of 5% in reduced scattering and 10% in absorption. [ABSTRACT FROM AUTHOR]

Mapping the optical absorption and scattering properties of tissues using spatial frequency-domain imaging (SFDI) enhances quantitative fluorescence imaging of protoporphyrin IX (PpIX) in gliomas in the preclinical setting. The feasibility of using SFDI in the operating room was investigated here. A benchtop SFDI system was modified to mount directly to a commercial operating microscope. A digital light processing module imposed a selectable spatial light pattern from a broad-band xenon arc lamp to illuminate the surgical field. White light excitation and a liquid crystal-tunable filter allowed the diffuse reflectance images to be recorded at discrete wavelengths from 450 to 720 nm on a sCMOS camera. The performance was first tested in tissue-simulating phantoms, and data were then acquired intraoperatively during brain tumor resection surgery. The optical absorption and transport scattering coefficients could be estimated with average errors of 3.2% and 4.5% for the benchtop and clinical systems, respectively, with spatial resolution of better than 0.7 mm. These findings suggest that SFDI can be implemented in a clinically relevant configuration to achieve accurate mapping of the optical properties in the surgical field that can then be applied to achieve quantitative imaging of the fluorophore. [ABSTRACT FROM AUTHOR]

Mapping the optical absorption and scattering properties of tissues using spatial frequency-domain imaging (SFDI) enhances quantitative fluorescence imaging of protoporphyrin IX (PpIX) in gliomas in the preclinical setting. The feasibility of using SFDI in the operating room was investigated here. A benchtop SFDI system was modified to mount directly to a commercial operating microscope. A digital light processing module imposed a selectable spatial light pattern from a broad-band xenon arc lamp to illuminate the surgical field. White light excitation and a liquid crystal-tunable filter allowed the diffuse reflectance images to be recorded at discrete wavelengths from 450 to 720 nm on a sCMOS camera. The performance was first tested in tissue-simulating phantoms, and data were then acquired intraoperatively during brain tumor resection surgery. The optical absorption and transport scattering coefficients could be estimated with average errors of 3.2% and 4.5% for the benchtop and clinical systems, respectively, with spatial resolution of better than 0.7 mm. These findings suggest that SFDI can be implemented in a clinically relevant configuration to achieve accurate mapping of the optical properties in the surgical field that can then be applied to achieve quantitative imaging of the fluorophore., ((2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)