HIGHLIGHTS
SUMMARY
Recent research has attempted to address some of the challenges posed due to the diffuse nature of photons and ill-posed inverse problems by employing deep learning (DL) and shallow machine_learning (ML) techniques. For instance, Murad et_al experimentally demonstrated the simultaneous reconstruction of the absorption and scattering coefficient of tissue mimicking using a 1D convolution neural_network (1D-CNN). Zou et_al used a machine_learning model with physical constraints to reconstruct DOT images. The significant observations and the results obtained from this work clearly show the potential of machine_learning algorithms where DOT reconstruction problems are concerned . . .
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