Multispectral snapshot imaging of skin microcirculatory hemoglobin oxygen saturation using artificial neural networks trained on in vivo data.

Abstract

.SignificanceDeveloping 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.AimTo develop and evaluate a methodology where a snapshot filter mosaic camera is utilized for imaging skin hemoglobin oxygen saturation (), using ANNs.ApproachMSI data were acquired during occlusion provocations. ANNs were trained to estimate 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.ResultsThe method enables spatially resolved estimation of skin tissue . Results are comparable to those acquired using a Monte-Carlo-based approach when relevant training data are used.ConclusionsTraining an ANN on in vivo MSI data covering a wide range of target values acquired during an occlusion protocol enable real-time estimation of maps. Data from the probe-based reference system can be used as target despite differences in sampling depth and measurement position.