Patient‐specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy

Background Protoacoustic (PA) imaging has the potential to provide real‐time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of using deep learning to enhance PA images. As the model was trained using a limited number of patients' data, its efficacy was limited when applied to individual patients. Purpose In this study, we developed a patient‐specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individual patients. Methods Our method consists of two stages: in the first stage, a group model is trained from a diverse training set containing all patients, where a novel deep learning network is employed to directly reconstruct the initial pressure maps from the radiofrequency (RF) signals; in the second stage, we apply transfer learning on the pre‐trained group model using patient‐specific dataset derived from a novel data augmentation method to tune it into a patient‐specific model. Raw PA signals were simulated based on computed tomography (CT) images and the pressure map derived from the planned dose. The reconstructed PA images were evaluated against the ground truth by using the root mean squared errors (RMSE), structural similarity index measure (SSIM) and gamma index on 10 specific prostate cancer patients. The significance level was evaluated by t‐test with the p‐value threshold of 0.05 compared with the results from the group model. Results The patient‐specific model achieved an average RMSE of 0.014 (p