Improving the intra-fraction update efficiency of a correlation model used for internal motion estimation during real-time tumor tracking for SBRT patients: fast update or no update?

Background And Purpose: For tumor tracking, a correlation model is used to estimate internal tumor position based on external surrogate motion. When patients experience an internal/external surrogate drift, an update of the correlation model is required to continue tumor tracking. In this study, the accuracy of the internal tumor position estimation for both the clinical available update at discrete points in time (rebuild) and an in-house developed non-clinical online update approach was investigated.

Methods: A dynamic phantom with superimposed baseline drifts and 14 SBRT patients, treated with real-time tumor tracking (RTTT) on the Vero system, were retrospectively simulated for three update scenarios, respectively no update, clinical rebuild and 0.5 Hz automated online update of the correlation model. By comparing the target positions based on 0.5 Hz verification X-ray images with the estimated internal tumor positions regarding all three update scenarios, 95th percentile modeling errors (ME95), incidences of full geometrical coverage of the CTV by a 5 mm extended PTV (P₅mm) and population-based PTV margins were calculated. Further, the treatment time reduction was estimated when switching from the clinical rebuild approach to the online correlation model update.

Results: For dynamic phantom motion with baseline drifts up to 0.4 mm/min, a 0.5 Hz intra-fraction update showed a similar accuracy in terms of ME95 and P5 mm compared to clinical rebuild. For SBRT patients treated on Vero with RTTT, accuracy was improved by 0.5 Hz online update compared to the clinical rebuild protocol, yielding smaller PTV margins (from 3.2 mm to 2.7 mm), reduced ME95,3D (from 4.1 mm to 3.4 mm) and an increased 5th percentile P5 mm (from 90.7% to 96.1%) for the entire patient group. Further, 80% of treatment sessions were reduced in time with on average 5.5 ± 4.1(1 SD)min.

Conclusion: With a fast (0.5 Hz) automated online update of the correlation model, an efficient RTTT workflow with improved geometrical accuracy was obtained.