Nearest Neighbor Method to Estimate Internal Target for Real-Time Tumor Tracking

Purpose: This work proposed a nearest neighbor estimation method to track the respiration-induced tumor motion. Methods: Based on the simultaneously collected motion traces of external surrogate and internal target during the modeling phase prior to treatment, we first obtain the nearest neighbors of the current surrogate in external space. Subsequently, the concurrent targets in internal space are determined and used to estimate the current target position. The method was validated on 71 cases that were from 3 open access databases. In addition, to evaluate the method's estimation and prediction accuracy, we compared the method with other works. Results: Except for 2 cases, the nearest neighbor estimation achieved the root-mean-square error of <3 mm. The comparison indicated that the method had better estimation accuracy than polynomial model and good prediction performance. Discussion: The 2 exceptive cases were further analyzed for failure causes. We inferred that one was because of the lack of estimating new target in our method, and the other one was because of the mistake during data collection. Accordingly, the potential solutions were suggested. Besides, the method's estimation for surrogate outliers, effects of modeling length, calibration, and extension were discussed. Conclusion: The results demonstrated nearest neighbor estimation's effectiveness. Except for this, the method imposes no restrictions on the modality of the pretreatment target images and does not assume a specific correspondence function between the surrogate and the target. With only 1 critical parameter, this nearest neighbor estimation method is easy to implement in clinical setting and thus has potential for broad applications.