Real-time photoacoustic projection imaging using deep learning

Abstract

Photoacoustic tomography (PAT) is an emerging and non-invasive hybrid imaging modality for visualizing light absorbing structures in biological tissue. The recently invented PAT systems using arrays of 64 parallel integrating line detectors allow capturing photoacoustic projection images in fractions of a second. Standard image formation algorithms for this type of setup suffer from under-sampling due to the sparse detector array, blurring due to the finite impulse response of the detection system, and artifacts due to the limited detection view. To address these issues, in this paper we develop a new direct and non-iterative image reconstruction framework using deep learning. The proposed DALnet combines the universal backprojection (UBP) using dynamic aperture length (DAL) correction with a deep convolutional neural network (CNN). Both subnetworks contain free parameters that are adjusted in the training phase. As demonstrated by simulation and experiment, the DALnet is capable of producing high-resolution projection images of 3D structures at a frame rate of over 50 images per second on a standard PC with NVIDIA TITAN Xp GPU. The proposed network is shown to outperform state-of-the-art iterative total variation reconstruction algorithms in terms of reconstruction speed as well as in terms of various evaluation metrics.

References in corpus (5)

• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
• Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
• Deep Learning-Guided Image Reconstruction from Incomplete Data
• Deep Learning Interior Tomography for Region-of-Interest Reconstruction

Cited by in corpus (8)

• Deep Learning in Photoacoustic Tomography: Current approaches and future directions
• Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties
• Deep learning for photoacoustic imaging: a survey
• Deep learning versus ${\ell }^1$-minimization for compressed sensing photoacoustic tomography
• NETT Regularization for Compressed Sensing Photoacoustic Tomography
• Learning the geometry of wave-based imaging
• A Jointed Feature Fusion Framework for Photoacoustic Reconstruction
• Learned backprojection for sparse and limited view photoacoustic tomography