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Proton FLASH therapy can deliver ultra-high dose rate radiation to tumor targets compared to conventional radiotherapy, and it has the potential to enable the paradigm shift of radiation oncology. Due to the inter- and intra-variation of respiratory motion, it is highly desired to accurately localize the target before the FLASH treatment delivery. This proof-of-concept study proposes a novel deep learning-based method to estimate tumor location from two orthogonal 2D kV on-board images. This approach potentially can enable real-time tumor localization and tracking to ensure the effectiveness of FLASH treatment. Our proposed method, central matching networks (CMN), can collect feature maps that represent the probability distribution of tumors, called center-ness maps, from 2D kV images and re-align them to their projections’ angle under the Cartesian coordinate system. Since the center-ness maps are inferred from two orthogonal projections, the depth information is thus obtained. CMN then re-transforms the 2D center-ness maps to the 3D center-ness map via depth inference. We conducted a simulation study with 180 orthogonal 2D kV projections from 4D computed tomography (CT) of 9 patients to test our method. For each 3D CT image set of a breathing phase, we simulated its 2D projections using two orthogonal angles. Then a leave-patient-out method was applied to train CMN to ensure the robustness of models. The model performance is quantified by Euclidean distances of the center-of-mass between the ground truth and predicted tumor locations in 3D space. CMN results in the Euclidean distance of 2.0±0.9 mm for all patients. The results demonstrate the feasibility and efficacy of CMN for 3D tumor localization, which has the potential to achieve in-treatment target localization on the fly.
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