AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data
This addresses a bottleneck in biomedical imaging for researchers and clinicians by improving reconstruction quality from limited data, though it appears incremental as it builds on neural network methods.
The paper tackles the problem of severe artifacts in photoacoustic image reconstruction from sparse sensor data by proposing AS-Net with multi-feature fusion, achieving superior reconstructions and faster speed validated on simulated and in vivo datasets.
Photoacoustic (PA) imaging is a biomedical imaging modality capable of acquiring high-contrast images of optical absorption at depths much greater than traditional optical imaging techniques. However, practical instrumentation and geometry limit the number of available acoustic sensors surrounding the imaging target, which results in the sparsity of sensor data. Conventional PA image reconstruction methods give severe artifacts when they are applied directly to the sparse PA data. In this paper, we firstly propose to employ a novel signal processing method to make sparse PA raw data more suitable for the neural network, concurrently speeding up image reconstruction. Then we propose Attention Steered Network (AS-Net) for PA reconstruction with multi-feature fusion. AS-Net is validated on different datasets, including simulated photoacoustic data from fundus vasculature phantoms and experimental data from in vivo fish and mice. Notably, the method is also able to eliminate some artifacts present in the ground truth for in vivo data. Results demonstrated that our method provides superior reconstructions at a faster speed.