Comparison of modeling accuracy between Radixact^® and CyberKnife^® Synchrony^® respiratory tracking system

Abstract

Synchrony Respiratory Tracking system adapted from CyberKnife has been introduced in Radixact to compensate the tumor motion caused by respiration. This study aims to compare the modeling accuracy of the Synchrony system between Radixact and CyberKnife. Two Synchrony plans based on fiducial phantoms were created for CyberKnife and Radixact, respectively. Different respiratory motion traces were used to drive a motion platform to move along the superoinferior and left-right direction. The cycle time and the amplitude of target/surrogate motion of one selected motion trace were scaled to investigate the dependence of modeling accuracy on the motion characteristic. The predicted target position, the correlation error, potential difference (Radixact only) and standard error (CyberKnife only) were extracted from raw data or log files of the two systems. The modeling accuracy was evaluated by calculating the root-mean-square (RMS) error between the predicted target positions and the input motion trace. A threshold T95 within which 95% of the potential difference or the standard error lay was defined and evaluated. Except for the motion trace with a small amplitude and a good (linear) correlation between target and surrogate motion, Radixact showed smaller RMS errors than CyberKnife. The RMS error of both systems increased with the motion amplitude and showed a decreasing trend with the increasing cycle time. No correlation was found between the RMS error and the amplitude of surrogate motion. T95 could be a good estimator of modeling accuracy for CyberKnife rather than Radixact. The correlation error defined in Radixact were largely affected by the number of fiducial markers and the setup error. In general, the modeling accuracy of the Radixact Synchrony system is better than that of the CyberKnife Synchrony system under unfavorable conditions.

Target margin design through analyzing a large cohort of clinical log data in the cyberknife system

“…Additionally, under unfavorable conditions, the Radixact synchrony system's modeling accuracy is better than the CyberKnife synchrony systems. 31 …”

“…Additionally, under unfavorable conditions, the Radixact synchrony system's modeling accuracy is better than the CyberKnife synchrony systems. 31 The results of the analysis of the PTV margins based on the source of errors and uncertainties are presented in Table 6. Based on these data, the PTV margins were estimated using two validated methods: the Van Herk formula and the uncertainty estimation method.…”

Technical note: Tracking target/chest relationship changes during motion‐synchronized tomotherapy treatments

“…The ability of Radixact Synchrony to track phantom motion has been demonstrated in the literature 8–12 . Radixact Synchrony has also been used to treat patients at several institutions 10,11 .…”

“…Yang et al. investigated tracking accuracies of Radixact Synchrony and CyberKnife Synchrony for various motions derived from patient treatments, including cases that had a “change in correlation between the target and surrogate motion.” 12 However, the target location was derived from the chest motion pattern using a CyberKnife Synchrony correlation model, 18 which inherently makes them correlated. Other experiments have placed the LEDs directly on the phantom, creating a rigid correlation between the LED and target motion 8,9,10,11 …”

“…The ability of Radixact Synchrony to track phantom motion has been demonstrated in the literature. [8][9][10][11][12] Radixact Synchrony has also been used to treat patients at several institutions. 10,11 For phantom experiments, Synchrony has been shown to track phantom motion within a root-mean square (RMS) error of 1.5 mm, and often less than 1.0 mm.…”