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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\u00a0patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>In this study, we developed a patient\u2010specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individual\u00a0patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2010trained group model using patient\u2010specific dataset derived from a novel data augmentation method to tune it into a patient\u2010specific 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 <jats:italic>t<\/jats:italic>\u2010test with the <jats:italic>p<\/jats:italic>\u2010value threshold of 0.05 compared with the results from the group\u00a0model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The patient\u2010specific model achieved an average RMSE of 0.014 (), and an average SSIM of 0.981 (), out\u2010performing the group model. Qualitative results also demonstrated that our patient\u2010specific approach acquired better imaging quality with more details reconstructed when comparing with the group model. Dose verification achieved an average RMSE of 0.011 (), and an average SSIM of 0.995 (). Gamma index evaluation demonstrated a high agreement (97.4% [] and 97.9% [] for 1%\/3\u00a0 and 1%\/5\u00a0mm) between the predicted and the ground truth dose maps. Our approach approximately took 6 s to reconstruct PA images for each patient, demonstrating its feasibility for online 3D dose verification for prostate proton\u00a0therapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our method demonstrated the feasibility of achieving 3D high\u2010precision PA\u2010based dose verification using patient\u2010specific deep\u2010learning approaches, which can potentially be used to guide the treatment to mitigate the impact of range uncertainty and improve the precision. Further studies are needed to validate the clinical impact of the\u00a0technique.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17294","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T09:31:24Z","timestamp":1720517484000},"page":"7425-7438","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Patient\u2010specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Yankun","family":"Lang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Physics University of Maryland Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoran","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University Durham North Carolina 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