Correlation Evaluation of a Tumor Tracking System Using Multiple External Markers

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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 gt 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)

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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)

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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.

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Experiment/Data Acquisition

• 7 patients undergoing radiotherapy for lung
  cancer (all with Karnofsky gt/ 70)
• 3 to 5 IR reflective external markers were placed
  on patients chest wall

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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

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Experiment/Data Acquisition

• The mean displacement and s 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

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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. fxy(m) is the cross-correlation of 2
  mean-removed time series xn and yn
• Finite-length time series, XCOV becomes

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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 d is the phase shift

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Correlation Analysis Method

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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).

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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

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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.

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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-Vertica
  l

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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

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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.

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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.

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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.

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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.

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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

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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.

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Questions?

• GO GATORS!