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Radiation-induced acoustic (RA) imaging is a promising technique forvisualizing radiation energy deposition in tissues, enabling new imagingmodalities and real-time therapy monitoring. However, it requires measuringhundreds or even thousands of averages to achieve satisfactory signal-to-noiseratios (SNRs). This repetitive measurement increases ionizing radiation doseand degrades the temporal resolution of RA imaging, limiting its clinicalutility. In this study, we developed a general deep inception convolutionalneural network (GDI-CNN) to denoise RA signals to substantially reduce thenumber of averages. The multi-dilation convolutions in the network allow forencoding 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-inducedacoustic, protoacoustic, and electroacoustic signals, qualitatively andquantitatively. Results demonstrated the effectiveness and generalizability ofGDI-CNN: for all the enrolled RA modalities, GDI-CNN achieved comparable SNRsto the fully-averaged signals using less than 2% of the averages, significantlyreducing imaging dose and improving temporal resolution. The proposed deeplearning framework is a general method for few-frame-averaged acoustic signaldenoising, which significantly improves RA imaging's clinical utilities forlow-dose imaging and real-time therapy monitoring.

Radiation-induced Acoustic Signal Denoising using a Supervised Deep Learning Framework for Imaging and Therapy Monitoring