Model based learning for accelerated, limited-view 3D photoacoustic tomography
This work addresses the challenge of accelerated and artifact-free 3D imaging in photoacoustic tomography, which is important for medical diagnostics, though it appears incremental as it builds on existing deep learning methods for tomographic reconstructions.
The authors tackled the problem of reconstructing high-resolution 3D images from limited-view photoacoustic measurements by designing a deep neural network that incorporates gradient information and learns a prior for image structures, resulting in accurate images tested on lung vessel data and applied to in-vivo measurements.
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 work we present a deep neural network that is specifically designed to provide high resolution 3D 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 artefacts. 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 CT scans and then applied to in-vivo photoacoustic measurement data.