  Radiation-induced acoustic (RA) imaging is a promising technique for
visualizing radiation energy deposition in tissues, enabling new imaging
modalities and real-time therapy monitoring. However, it requires measuring
hundreds or even thousands of averages to achieve satisfactory signal-to-noise
ratios (SNRs). This repetitive measurement increases ionizing radiation dose
and degrades the temporal resolution of RA imaging, limiting its clinical
utility. In this study, we developed a general deep inception convolutional
neural network (GDI-CNN) to denoise RA signals to substantially reduce the
number of averages. The multi-dilation convolutions in the network allow for
encoding and decoding signal features with varying temporal characteristics,
making the network generalizable to signals from different radiation sources.
The proposed method was evaluated using experimental data of X-ray-induced
acoustic, protoacoustic, and electroacoustic signals, qualitatively and
quantitatively. Results demonstrated the effectiveness and generalizability of
GDI-CNN: for all the enrolled RA modalities, GDI-CNN achieved comparable SNRs
to the fully-averaged signals using less than 2% of the averages, significantly
reducing imaging dose and improving temporal resolution. The proposed deep
learning framework is a general method for few-frame-averaged acoustic signal
denoising, which significantly improves RA imaging's clinical utilities for
low-dose imaging and real-time therapy monitoring.
