A Physics-Informed Neural Network for In Vivo Dosimetry Using Quantitative Radiacoustic Imaging

Accurate dosimetry is critical for safe and effective radiotherapy, yet no clinical method currently measures dose directly within the patient in vivo. Radiacoustic imaging (RAI), which detects acoustic waves generated by thermoelastic expansion during radiation delivery, offers a promising solution but has been limited to qualitative output. We present a quantitative RAI (qRAI) framework powered by a physics-informed neural network (PINN) that reconstructs quantitative dose maps in vivo. The PINN incorporates the physics of acoustic wave generation and propagation, along with a digital twin of the radiation delivery and radiacoustic detection systems, enabling accurate reconstruction from limited-view data. Reconstructed pressure maps are calibrated against experimental and simulated dose references. We validate the method across diverse clinical scenarios, including water tank dosimetry, human torso phantoms, and FLASH electron therapy. Compared to purely data-driven models, our PINN approach offers superior robustness and generalizability, especially in clinical settings lacking experimental ground truth. These results establish PINN-based qRAI as a powerful tool for real-time, adaptive, and quantitative in vivo dosimetry.