The ability to measure microcirculatory parameters such as hemoglobin oxygen saturation is important since it mirrors the microcirculatory state of the body. The microcirculation delivers oxygen and nutrients to the cells of the body and, if impaired, may be a sign of circulatory failure. Human skin microcirculation can be accessed non-invasively with bio-optical technologies, where skin acts as a diagnostic window. 

Diffuse reflectance spectroscopy (DRS) is a technique that access skin microcirculatory parameters, especially hemoglobin oxygen saturation. Basic systems are fiber optic probebased and measure in one point, often in firm contact with the skin. Multispectral diffuse reflectance imaging (MSI) enables spatially resolved DRS, imaging skin optical parameters from spectrally resolved backscattered intensities. Spectral information detected by MSI systems contain information on, e.g., hemoglobin oxygen saturation and optical properties of the tissue. Both spatial and temporal resolved information of hemoglobin oxygen saturation is beneficial for better diagnostics in most clinical applications, e.g., to monitor progression of wound healing processes, or other microcirculatory diseases reflected in hemoglobin spectral changes. 

Analysis of acquired MSI multispectral data cubes to access information on tissue parameters with high contrast to these variations can be performed in several ways using models and simulations. Time resolved continuous measurements that are spectrally and spatially resolved generate large amounts of data, requiring both storage space and fast analysis. Reducing the number of wavelengths is one way to limit the amount of data, if it does not reduce the quality of interpreted results. 

Therefore, in my work, I investigated theoretically how to reduce the number of wavelengths, and later implemented my findings using a snapshot MSI camera. Monte Carlo (MC) simulations were used to estimate hemoglobin oxygen saturation from captured MSI data. I also performed temporally resolved in vivo measurements on healthy test subjects during vascular occlusion provocations with a 16-channel snapshot MSI system. The acquired data were analyzed using two different methods: inverse MC and trained artificial neural networks (ANNs). For inverse MC, the acquired spectrum was iteratively compared to simulated spectra, where different optical properties were used for the simulation, trying to find the best fit. ANNs were trained to intensity data measured with the MSI system, using concurrently measured hemoglobin oxygen saturation values from a validated probe-based system as target data. 

The results and outcome of this thesis indicate good possibility to accurately estimate hemoglobin oxygen saturation with as few as four wavelengths. Estimated hemoglobin oxygen saturation values from analysis of in vivo measurements from the 16-channel snapshot MSI camera show high conformance to values measured by the validated probe-based system. Using the ANN-approach reduces time for analysis of a 512 × 270-pixel image to 0.056 s, compared to 1 h 58 min required by the inverse MC algorithm to analyze the same data. The method enables real-time analysis, and is, consequently, preferable in many clinical situations. 

Significance: Developing algorithms for estimating blood oxygenation from snapshot multispectral imaging (MSI) data is challenging due to the complexity of sensor characteristics and photon transport modeling in tissue. We circumvent this using a method where artificial neural networks (ANNs) are trained on in vivo MSI data with target values from a point-measuring reference method.

Aim: To develop and evaluate a methodology where a snapshot filter mosaic camera is utilized for imaging skin hemoglobin oxygen saturation (SO2), using ANNs.

Approach: MSI data were acquired during occlusion provocations. ANNs were trained to estimate SO2 with MSI data as input, targeting data from a validated probe-based reference system. Performance of ANNs with different properties and training data sets was compared.

Results: The method enables spatially resolved estimation of skin tissue SO2. Results are comparable to those acquired using a Monte-Carlo-based approach when relevant training data are used.

Conclusions: Training an ANN on in vivo MSI data covering a wide range of target values acquired during an occlusion protocol enable real-time estimation of SO2 maps. Data from the probe-based reference system can be used as target despite differences in sampling depth and measurement position.

Significance: Hemoglobin oxygen saturation and red blood cell (RBC) tissue fraction are important parameters when assessing microvascular status. Functional information can be attained using temporally resolved measurements performed during stimulus–response protocols. Pointwise assessments can currently be conducted with probe-based systems. However, snapshot multispectral imaging (MSI) can be used for spatial–temporal measurements.

Aim: To validate if hemoglobin oxygen saturation and RBC tissue fraction can be quantified using a snapshot MSI system and an inverse Monte Carlo algorithm.

Approach: Skin tissue measurements from the MSI system were compared to those from a validated probe-based system during arterial and venous occlusion provocation on 24 subjects in the wavelength interval 450 to 650 nm, to evaluate a wide range of hemoglobin oxygen saturation and RBC tissue fraction levels.

Results: Arterial occlusion results show a mean linear regression R2 = 0.958 for hemoglobin oxygen saturation. Comparing relative RBC tissue fraction during venous occlusion results in R2 = 0.925. The MSI system shows larger dynamic changes than the reference system, which might be explained by a deeper sampling including more capacitance vessels.

Conclusions: The snapshot MSI system estimates hemoglobin oxygen saturation and RBC tissue fraction in skin microcirculation showing a high correlation (R2 > 0.9 in most subjects) with those measured by the reference method.

Hyperspectral imaging (HSI) can estimate the spatial distribution of skin blood oxygenation, using visible to near-infrared light. HSI oximeters often use a liquid-crystal tunable filter, an acousto-optic tunable filter or mechanically adjustable filter wheels, which has too long response/switching times to monitor tissue hemodynamics. This work aims to evaluate a multispectral snapshot imaging system to estimate skin blood volume and oxygen saturation with high temporal and spatial resolution. We use a snapshot imager, the xiSpec camera (MQ022HG-IM-SM4X4-VIS, XIMEA (R)), having 16 wavelength-specific Fabry-Perot filters overlaid on the custom CMOS-chip. The spectral distribution of the bands is however substantially overlapping, which needs to be taken into account for an accurate analysis. An inverse Monte Carlo analysis is performed using a two-layered skin tissue model, defined by epidermal thickness, haemoglobin concentration and oxygen saturation, melanin concentration and spectrally dependent reduced-scattering coefficient, all parameters relevant for human skin. The analysis takes into account the spectral detector response of the xiSpec camera. At each spatial location in the field-of-view, we compare the simulated output to the detected diffusively backscattered spectra to find the best fit. The imager is evaluated for spatial and temporal variations during arterial and venous occlusion protocols applied to the forearm. Estimated blood volume changes and oxygenation maps at 512x272 pixels show values that are comparable to reference measurements performed in contact with the skin tissue. We conclude that the snapshot xiSpec camera, paired with an inverse Monte Carlo algorithm, permits us to use this sensor for spatial and temporal measurement of varying physiological parameters, such as skin tissue blood volume and oxygenation.

Skin blood haemoglobin saturation (𝑠_b) can be estimated with hyperspectral imaging using the wavelength (λ) range of 450-700 nm where haemoglobin absorption displays distinct spectral characteristics. Depending on the image size and photon transport algorithm, computations may be demanding. Therefore, this work aims to evaluate subsets with a reduced number of wavelengths for 𝑠_b estimation. White Monte Carlo simulations are performed using a two-layered tissue model with discrete values for epidermal thickness (𝑇_epi) and the reduced scattering coefficient (μ'_s ), mimicking an imaging setup. A detected intensity look-up table is calculated for a range of model parameter values relevant to human skin, adding absorption effects in the post-processing. Skin model parameters, including absorbers, are; μ'_s (λ), 𝑇_epi, haemoglobin saturation (𝑠_b), tissue fraction blood (𝑐_b) and tissue fraction melanin (𝑐_mel). The skin model paired with the look-up table allow spectra to be calculated swiftly. Three inverse models with varying number of free parameters are evaluated: A(𝑠_b, 𝑐_b), B(𝑠_b, 𝑐_b, 𝑐_mel) and C(all parameters free). Fourteen wavelength candidates are selected by analysing the maximal spectral sensitivity to 𝑠_b and minimizing the sensitivity to 𝑐_b. All possible combinations of these candidates with three, four and 14 wavelengths, as well as the full spectral range, are evaluated for estimating 𝑠_b for 1000 randomly generated evaluation spectra. The results show that the simplified models A and B estimated 𝑠_b accurately using four wavelengths (mean error 2.2% for model B). If the number of wavelengths increased, the model complexity needed to be increased to avoid poor estimations.