  Protoacoustic imaging showed great promise in providing real-time 3D dose
verification of proton therapy. However, the limited acquisition angle in
protoacoustic imaging induces severe artifacts, which significantly impairs its
accuracy for dose verification. In this study, we developed a deep learning
method with a Recon- Enhance two-stage strategy for protoacoustic imaging to
address the limited view issue. Specifically, in the Recon-stage, a
transformer-based network was developed to reconstruct initial pressure maps
from radiofrequency signals. The network is trained in a hybrid-supervised
approach, where it is first trained using supervision by the iteratively
reconstructed pressure map and then fine-tuned using transfer learning and
self-supervision based on the data fidelity constraint. In the Enhance-stage, a
3D U-net is applied to further enhance the image quality with supervision from
the ground truth pressure map. The final protoacoustic images are then
converted to dose for proton verification. The results evaluated on a dataset
of 126 prostate cancer patients achieved an average RMSE of 0.0292, and an
average SSIM of 0.9618, significantly out-performing related start-of-the-art
methods. Qualitative results also demonstrated that our approach addressed the
limit-view issue with more details reconstructed. Dose verification achieved an
average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation
demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between
the predicted and the ground truth dose maps. Notably, the processing time was
reduced to 6 seconds, demonstrating its feasibility for online 3D dose
verification for prostate proton therapy.
