Post on 05-Jan-2016
description
Correlation Evaluation of a Tumor Tracking System Using Multiple External
Markers
Hui Yan, Fang-Fang Yin, et al
(Duke University Med. Ctr.)
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);
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)
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.
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
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
Experiment/Data Acquisition
The mean displacement and σ of 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
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:
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;
Correlation Analysis Method
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).
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: Cm
n= 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
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.
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;
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;
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.
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.
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.
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.
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%
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.
Questions?
GO GATORS!