LAUSR

Abstract. 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.