Deep learning for photoacoustic image reconstruction from incomplete data (Conference Presentation)

Abstract

There are occasions, perhaps due to hardware constraints, or to speed-up data acquisition, when it is helpful to be able to reconstruct a photoacoustic image from an under-sampled or incomplete data set. Here, we will show how Deep Learning can be used to improve image reconstruction in such cases. Deep Learning is a type of machine learning in which a multi-layered neural network is trained from a set of examples to perform a task. Convolutional Neural Networks (CNNs), a type of deep neural network in which one or more layers perform convolutions, have seen spectacular success in recent years in tasks as diverse as image classification, language processing and game playing. In this work, a series of CNNs were trained to perform the steps of an iterative, gradient-based, image reconstruction algorithm from under-sampled data. This has two advantages: first, the iterative reconstruction is accelerated by learning more efficient updates for each iterate; second, the CNNs effectively learn a prior from the training data set, meaning that it is not necessary to make potentially unrealistic regularising assumptions about the image sparsity or smoothness, for instance. In addition, we show an example in which the CNNs learn to remove artifacts that arise when a slow but accurate acoustic model is replaced by a fast but approximate model. Reconstructions from simulated as well as in vivo data will be shown.