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Deep inspiration breath-hold (DIBH) is a motion management technique that is commonly used in lung radiotherapy (RT) for patients with large tumor motion. Cone beam CT (CBCT) scan with DIBH is commonly used for treatment setup, and the acquisition of a CBCT scan (typically ~1 minute) requires multiple DIBHs. If a large shift of treatment couch is needed, a second CBCT scan is often acquired after the shift for verification purpose. However, the more DIBH occurred during treatment setup, the higher probability that patient may become tired and hence less able to reproduce consistent DIBH during treatment delivery, which would degrade the target coverage and deliver more dose to critical organs and normal tissues. To reduce the number of DIBHs required for treatment setup, we propose a deep learning method to locate tumor 3D location from two orthogonal and simultaneously acquired 2D projections (one is kV and the other is MV) for fast verification, which will eliminate the need for the second CBCT scan. Our proposed method, named center-ness matching network, is capable of collecting feature map that represent the probability distribution of tumor, called center-ness map, from 2D projections and re-aligning them to their projections’ angle in the Cartesian coordinate system. The tumor depth information is also collected. The center-ness matching network then re-transforms the 2D center-ness maps to the 3D center-ness map via depth learning. Finally, the 3D location of tumor is estimated from the 3D center-ness map. We conducted a retrospective study on 10 patient cases, who had undergone 4D-CT scans for motion evaluation at CT simulation and received lung RT treatments in our institution. For each 3D CT image set of a breathing phase, we simulated its 2D kV projection at gantry angle 0° and 2D MV projection at 90°. Patient specific 3D CT images of 9 phases and the corresponding 2D projections data were used for training, with the remaining phase used for testing. The target error (TE), which is of the difference in distance between the center-of-mass of ground truth tumor of 3D CT and the predicted tumor 3D location, achieved by our method was 2.6±0.7 mm. These results demonstrated the feasibility and efficacy of our proposed method, which provides a potential solution for fast verification of treatment setup of DIBH lung RT.
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