Feasibility study of using next-generation reservoir computing (NG-RC) model to estimate liver tumor motion from external breathing signals

Background

Respiratory motion is a challenge for accurate radiotherapy that may be mitigated by real-time tracking. Commercial tracking systems utilize a hybrid external-internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X-ray imaging of the internal tumor position.

Purpose

This study investigates the feasibility of using the next generation reservoir computing (NG-RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions.

Methods

The NG-RC model utilizes the nonlinear vector autoregressive (NVAR) machine to account for the hysteresis or phase differences between external and internal motions. The datasets used to evaluate the efficacy of the NG-RC model include 57 motion traces from the CyberKnife system. The datasets were divided into three regions (central, lower, and upper livers) and three motion patterns. These patterns include linear and nonlinear motion patterns (Group A), hysteresis motion patterns (Group B), and all motion patterns (Group C). Moreover, various updating techniques were examined, such as continuously updating the NG-RC model using the first-in-first-out (FIFO) approach and sampling the internal tumor position every 0 s (strategy A), 60 s (strategy B), 30 s (strategy C), and 50 s (strategy D).

Results

The NG-RC model combined with strategy C resulted in better estimation accuracy than the reported CyberKnife cases (Wilcoxon signed rank p < 0.05). For linear and nonlinear motion patterns, the 3D radial estimation accuracy (mean ± SD) using the NG-RC model combined with strategy C and the CyberKnife system was 1.20 ± 0.78 and 1.1 ± 0.20 mm in the central liver, 0.66 ± 0.25 and 1.49 ± 0.50 mm in the lower liver, and 1.73 ± 0.86 and 1.61 ± 0.42 mm in the upper liver. For hysteresis motion patterns, the corresponding values were 1.13 ± 0.37 and 1.45 ± 0.33 mm, 1.43 ± 1.30 and 1.67 ± 0.42 mm, and 1.20 ± 0.68 and 1.46 ± 0.54 mm in the central, lower, and upper livers, respectively.

Conclusion

This study proposed a new hybrid correlation model for real-time tumor tracking, which can be used to account for both linear and nonlinear motion patterns, as well as hysteresis motion patterns. Additionally, the NG-RC model required shorter training data sets (15 s) during pre-treatment and short internal motion sampling (every 30 s) during treatment compared to other ECMs.