Download Correlation of External Markers and Functional for Respiration

Correlation of External Markers and
Functional for Respiration Compensation
in Radiotherapy
Tomas Krilavičius1,2
Indrė Žliobaitė 1,3
Rūta Užupytė 1,2
Henrikas Simonavičius 4
1Baltic
Institute of Advanced Technology
2Vytautas Magnus University
3Aalto University
4Rubedo systems
Problem
Positioning Patient
Compensate
Respiratory
Motion
Components
Couch
(HexaPOD)
Tracking device
Controller
Radiation Beam
Source
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Approaches
Do nothing
Gating
Controlled-breath
Probability-based planning (planning tumor volume)
Displacing multi-leaf collimator (MLC)
Changing configuration of MLC
Using patient support structure to compensate
movement
3
Research Directions
General solution
Determine position of tumor
Predict motion
Adapt dosimeter
Predict position of tumor (functional target from
external marker)
Common approaches (1 to 3 dimensions)
Pearson correlation and Gaussian filters
Fourier transformation and cross corellation
linear interpolation and partial-least squares
4
Signals
8 sets of 2D signals
3 surogate marker per record
6-10 points-of-interest
Duration: from 300 to 500 frames (150 - 400 sec.)
Overall 87 signal-pairs
We thank
Jonas Venius and his colleagues for help in collecting
signals
Gabrielius Čaplinskas for extracting them from DICOMs
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Signals
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Algorithmic Solution
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Loss Function:
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Results (Correlation)
xy
P3
P3
P4
P4
P5
P5
P6
P6
P7
P7
P8
P8
P9
P9
P0
P0
P1
P1
P2
P2
0.05
-0.33
0.98
-0.99
0.77
-0.97
0.41
-0.72
0.92
-0.99
0.97
-0.97
-0.09
-0.79
-0.06
0.37
-0.91
0.93
-0.64
0.88
-0.4
0.72
-0.91
0.93
-0.91
0.91
0.04
0.78
0.07
-0.33
0.97
-0.98
0.79
-0.95
0.42
-0.74
0.92
-0.97
0.96
-0.96
-0.05
-0.8
-0.07
0.26
-0.82
0.85
-0.65
0.82
-0.4
0.64
-0.79
0.84
-0.82
0.83
0.03
0.7
0.06
-0.3
0.93
-0.94
0.9
-0.96
0.41
-0.73
0.84
-0.93
0.91
-0.92
-0.01
-0.79
0.03
-0.24
0.89
-0.89
0.85
-0.91
0.39
-0.66
0.79
-0.88
0.86
-0.88
-0.06
-0.73
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Results (Prediction)
Model
MAE, mm
P4~P0
P4~P1
P4~P2
P5~P0
P5~P1
P5~P2
P7~P0
P7~P1
P7~P2
P8~P0
P8~P1
P8~P2
Overall average (all models)
Minimal error
Maximal error
0.55
0.79
0.89
0.51
0.61
0.61
0.62
0.87
0.95
0.85
1.04
1.1
1.1
0.26
3.4
p-value
0
0 0.96 0.89
0
0 0.93 0.74
0
0 0.87 0.81
0
0 0.53 0.81
0
0 0.55 0.7
0 0.03 0.72 0.86
0
0 0.82 0.88
0
0 0.83 0.72
0
0 0.66 0.8
0.13 0 0.94 0.85
0.03 0 0.91 0.68
0
0 0.85 0.77
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Prediction (Coordinate x from relation P5~P0)
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Prediction (Coordinate y from relation P5~P0)
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Prediction (Coordinate x from relation P4~P0)
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Prediction (Coordinate y from relation P4~P0)
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Results and Conclusions
Functional targets move more than external markers
Signals motion range depends on the directions
markers move more in anterior-posterior direction
targets - in superior inferior direction, then in
anterior-posterior
Better result are obtained using markers with a greater
range of movement and
middle abdomen if lateral direction is ignored
otherwise upper abdomen
Regression residuals are auto-correlated
Loss function (MAE) is sensitive to the range
15
Results and Conclusions
Functional targets move more than external markers
Signals motion range depends on the directions
markers move more in anterior-posterior direction
targets - in superior inferior direction, then in
anterior-posterior
Better result are obtained using markers with a greater
range of movement and
Due to signal recoding
middle abdomen if lateral direction
is ignored
specifics
using dMRI
otherwise upper abdomen motion (coil is placed on
the patient) is lower than
Regression residuals are auto-correlated
observed in “free”
Loss function (MAE) is sensitiveconditions
to the range
and literature
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Future Plans
Experiments with more complex regression cases
Solve the problem of residuals autocorrelation
Choose other quality measures
Analyze respiratory motion prediction and
design cases of an overall system radiation
therapy system with respiratory motion
compensation
We already have some results in predicting
respiratory motion
More results in relating external markers and
targets
17
THANKS
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Body and Directions
Anterior Posterior
Superior-Inferior
Lateral
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