Department of Radiation Oncology Physics, University of Maryland, Baltimore, Maryland, USA.
Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Med Phys. 2024 Oct;51(10):7425-7438. doi: 10.1002/mp.17294. Epub 2024 Jul 9.
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.
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.
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.
The patient-specific model achieved an average RMSE of 0.014 ( ), and an average SSIM of 0.981 ( ), out-performing the group model. Qualitative results also demonstrated that our patient-specific 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 and 1%/5 mm) 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 therapy.
Our method demonstrated the feasibility of achieving 3D high-precision PA-based dose verification using patient-specific deep-learning 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 technique.
原生声学（PA）成像是实时质子治疗剂量验证的潜在方法。然而，由于角度采集受限，PA 图像容易受到严重失真的影响。我们之前的研究表明，使用深度学习来增强 PA 图像是有潜力的。由于模型是使用有限数量的患者数据进行训练的，因此当应用于个体患者时，其效果有限。
本研究开发了一种用于原生声学成像的患者特异性深度学习方法，以提高原生声学成像的重建质量和个体患者的剂量验证准确性。
我们的方法包括两个阶段：第一阶段，从包含所有患者的多样化训练集中训练一个组模型，其中使用一种新的深度学习网络直接从射频（RF）信号重建初始压力图；第二阶段，使用源自新颖数据增强方法的患者特定数据集对预训练的组模型应用迁移学习，将其调整为患者特异性模型。基于 CT 图像和来自计划剂量的压力图模拟原始 PA 信号。使用均方根误差（RMSE）、结构相似性指数度量（SSIM）和伽马指数在 10 名特定前列腺癌患者中对重建的 PA 图像与真实图像进行评估。通过 t 检验评估显著性水平，p 值阈值为 0.05，与组模型的结果进行比较。
患者特异性模型的平均 RMSE 为 0.014（ ），平均 SSIM 为 0.981（ ），优于组模型。定性结果也表明，与组模型相比，我们的患者特异性方法可以获得更好的成像质量，并重建更多细节。剂量验证的平均 RMSE 为 0.011（ ），平均 SSIM 为 0.995（ ）。伽马指数评估表明，预测剂量图与真实剂量图之间具有高度一致性（1%/3 和 1%/5 毫米时分别为 97.4%[ ]和 97.9%[ ]）。我们的方法大约需要 6 秒即可为每个患者重建 PA 图像，表明其在线 3D 剂量验证前列腺质子治疗的可行性。
我们的方法证明了使用患者特异性深度学习方法实现 3D 高精度基于 PA 的剂量验证的可行性，这可能有助于指导治疗以减轻范围不确定性的影响并提高精度。需要进一步的研究来验证该技术的临床影响。
