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?

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