Deep learning

Photoacoustic tomography

Physics

Algorithm

Optics

Iterative reconstruction

Artificial intelligence

Tomography

Set (abstract data type)

Programming language

Computer science

Computation

Photoacoustic imaging in biomedicine

Data set

Computer vision

Artificial neural network

Tomographic reconstruction

IEEE Transactions on Medical Imaging

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.

Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography