In Journal of biomedical optics
SIGNIFICANCE : In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning (with high repetition rate light sources) and using multiple-USTs. However, artifacts arising from the sparse signal acquisition and low signal-to-noise ratio at higher scanning speeds limit the imaging speed. Thus, there is a need to improve the imaging speed of the PAT systems without hampering the quality of the PAT image.
AIM : To improve the frame rate (or imaging speed) of the PAT system by using deep learning (DL).
APPROACH : For improving the frame rate (or imaging speed) of the PAT system, we propose a novel U-Net-based DL framework to reconstruct PAT images from fast scanning data.
RESULTS : The efficiency of the network was evaluated on both single- and multiple-UST-based PAT systems. Both phantom and in vivo imaging demonstrate that the network can improve the imaging frame rate by approximately sixfold in single-UST-based PAT systems and by approximately twofold in multi-UST-based PAT systems.
CONCLUSIONS : We proposed an innovative method to improve the frame rate (or imaging speed) by using DL and with this method, the fastest frame rate of ∼ 3 Hz imaging is achieved without hampering the quality of the reconstructed image.
Rajendran Praveenbalaji, Pramanik Manojit
2022-Jun
circular photoacoustic tomography, deep learning, high framerate imaging, photoacoustic tomography
