Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 29870367.
- Also identified by DOI 10.1109/TMI.2018.2820382 and PMC identifier 7613684.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.
Medical subject headings
- Deep Learning
- Image Processing, Computer-Assisted
- Imaging, Three-Dimensional
- Photoacoustic Techniques