Correlation Evaluation of a Tumor Tracking System Using Multiple External Markers

1. Correlation Evaluation of a Tumor Tracking System Using Multiple External Markers Hui Yan, Fang-Fang Yin, et al (Duke University Med. Ctr.)

2. Overview • Patient set-up & tumor localization difficult sites with frequent organ movement, like lungs • CTV > PTV by considerable margin to account for target displacement • Actual dose differ from intended dose distribution to tumor • Causes of internal target displacement: • Position-related target shift, • Interfractional organ motion; • Intrafractional organ motion (esp. respiration related motion);

3. Overview • Several breath-holding techniques developed to minimize respiratory-related organ motion • Reduce CTV margins, reduce motion, BUT cannot eliminate • Direct tumor tracking system have been employed using implanted metal seeds and markers with x-ray imaging • Continuous imaging causes radiation to be significant • Indirect tumor tracking systems: • Spirometer & strain gauge ; • External markers/sensors (Infrared LEDs)

4. Investigation • This study: multiple external marker tracking system was investigated. • Infrared cameras and a clinical simulator were used to acquire the motion of an internal and multiple external markers simultaneously. • Correlation between internal and external motion signals were analyzed using a cross-covariance method. • Composite signals for each comparison were generated with multiple external signals using linear regression.

5. Experiment/Data Acquisition • 7 patients undergoing radiotherapy for lung cancer (all with Karnofsky >/= 70) • 3 to 5 IR reflective external markers were placed on patients’ chest wall

6. Experiment/Data Acquisition • With each patient: • 6 sessions with 3 identical sessions in each imaging direction • S1: Free breathing for 40s (FFB) • S2: Free breathing for 10s, hold for 5s, resume free breathing 10s (BH) • S3: Free breathing for 40s (SFB) • 2 IR cameras collect data, 10Hz • 3D marker location time index saved • Fluoroscopic images, 15Hz

7. Experiment/Data Acquisition • The mean displacement and σof the tumor center for each patient: Table II • Mean deviation ~2 pixels, avg. peak-to-peak displacement was up to 30 pixels • Mean deviation to relatively small • All the data was normalized to the range of [0,1] for ease of analyzing and comparison

8. Correlation Analysis Method • The cross-covariance (XCOV in Matlab) function was used • Same as traditional correlation coefficients, but also provided additional information about the phase shift • XCOV func. φxy(m) is the cross-correlation of 2 mean-removed time series xn and yn: • Finite-length time series, XCOV becomes:

9. Correlation Analysis Method • After index conversion from [-N,N] to [1,2N-1] and normalization: • The phase shift between 2 input series can found from XCOV sequence. If no shift, max XCOV sequence value will occur at index N. • where δ is the phase shift;

10. Correlation Analysis Method

11. Correlation Analysis between external and internal signals • XCOV function used between all pairs of external & internal signals to gather mean, min, and max of the phase shifts (Table III). • Max phase shift = 0.81s • Mean varies from 0.12s - 0.52s • Correlation coeff. [0,0.98] • After the correction for phase shift, the average correlation coeffcient value increased significantly and the corresponding deviation decreased. • Correlation coefficients grouped by breathing patterns (Table IV).

12. Correlation Analysis between composite and internal signals • Different composite signals were generated using different combinations of external signals. • To see the effect of the number of external markers, the combination formula was used: Cmn= n!/[(n-m)!m!] • # different cmbinations of m external markers from n markers. • Correlation errors of the 3 composites for a combination were averaged; mean, max & min were tabulated

13. Effect of the number of external markers • Most cases, a decrease in mean correlation error was observed when more external signals were taken into account • But minimum values of correlation error do not decrease as the number of external markers increased.

14. Effect of dimensional components of internal and external signal • Composite signals generated from external signals in a specified dimension or directions (grouped by breathing pattern): • Lateral, Longitudinal, Vertical, Lateral-Longitudinal, Longitudinal-Vertical, Lateral-Vertical, and Lateral-Longitudinal-Vertical;

15. Effect of dimensional components of internal and external signal • Minimal correlation error was achieved by the composite signal consisting of external markers in ALL three dimensions;

16. Effect of dimensional components of internal and external signal • With external marker dimensional components fixed, composite signals were generated and compared to the internal signal in the same dimension.

17. Effect of dimensional components of internal and external signal • Correlation errors were lower when more components external signals were included in the composite signal. • Relatively, the largest correlation errors were found in internal signals in the lateral direction of AP imaging.

18. Effect of the breathing pattern • The two free breathing sessions (FFB & SFB) exhibited a similar level of correlation errors (mean, min & max) in all patients. • Patients 1,2,4,5,7 had similar correlation errors for all 3 breathing patterns. • Patients 3 & 6: Visible differences in the correlation errors of BH and free-breathing sessions.

19. Effect of the breathing pattern • The bars represent the min & max values of the correlation errors • Most of the points follow an approximately linear relationship • This linear relationship indicates that the correlation error between the composite and internal signals is affected inversely by the quality of correlation coefficient between external and internal signals.

20. Effect of the phase shift • Table IX tabulates correlation errors caused by the external composite signals before and after the correction for the phase shift • Significant decrease in correlation error • Patients 1, 2, 4, 6, 7 similar levels of correlation errors before & after correction • Patients 3 & 5 mean and max values were decreased by up to 20%

21. Effect of the phase shift • In addition to the decrease in mean value of correlation errors, consistent decreases of the maximum and minimum values of correlation errors were also observed in most of patients.

22. Questions? GOGATORS!