Daily prostate positioning
Frank Van den Heuvel Ph.D.
Lei Dong Ph.D.
David Jaffray Ph.D.
27th July 2004
• Measure with a micrometer.
• Measure with a micrometer.
• Mark with chalk.
• Measure with a micrometer.
• Mark with chalk.
• Cut with an axe.
• Measure with a micrometer.
• Mark with chalk.
• Cut with an scalpel.
Now that we have IMRT.
• Measure with a micrometer.
• Mark with chalk.
• Cut with an scalpel.
Now that we have IMRT.
But, we are very precise in determining the thickness of our
chalk!
The Goal of Image Guided Radiation Treatment
The Goal of Image Guided Radiation Treatment
Reduction of the PTV margin!
The Goal of Image Guided Radiation Treatment
Reduction of the PTV margin!
The PTV margin depends on:
• Organ
• Patient
• Modality of Treatment
van Herk
et al.[1]
proposes a margin recipe, based on:
van Herk
et al.[1]
proposes a margin recipe, based on:
• Obtaining a 98% equivalent uniform dose (EUD)
van Herk
et al.[1]
proposes a margin recipe, based on:
• Obtaining a 98% equivalent uniform dose (EUD)
• For 90% of the patients
van Herk
et al.[1]
proposes a margin recipe, based on:
• Obtaining a 98% equivalent uniform dose (EUD)
• For 90% of the patients
• Calculate this using a simulation procedure, using MC
calculations to simulate population spread.
Recipe:
M = 2.5Σ + 0.7σ − 3mm
van Herk
et al.[1]
proposes a margin recipe, based on:
• Obtaining a 98% equivalent uniform dose (EUD)
• For 90% of the patients
• Calculate this using a simulation procedure, using MC
calculations to simulate population spread.
Recipe:
M = 2.5Σ + 0.7σ − 3mm
• Σ: Spread of the systematic errors SD(Σi )
van Herk
et al.[1]
proposes a margin recipe, based on:
• Obtaining a 98% equivalent uniform dose (EUD)
• For 90% of the patients
• Calculate this using a simulation procedure, using MC
calculations to simulate population spread.
Recipe:
M = 2.5Σ + 0.7σ − 3mm
• Σ: Spread of the systematic errors SD(Σi )
• σ: Average of the random errors < σi >
Reducing the Margin
Reducing the Margin
We can reduce Σ and/or σ
Reducing the Margin
We can reduce Σ and/or σ
Reduction of Σ is most efficient and easiest to do
Reducing the Margin
We can reduce Σ and/or σ
Reduction of Σ is most efficient and easiest to do
Off Line
Is the most popular correction system, to
date.
1. Gather data to sample positional distribution (up to
six fractions).
2. Apply a correction to minimize the systematic error.
3. Adapt margins to reflect the variations.
On–line adjustment:
reduces both Σ and σ at
the same time.
1. No “bad” treatments
2. Conceptually simple, no assumptions on type of
probability distributions
How[2]
Essentially all on–line adjustments are identical, only the
modalities differ.
1. Patient is setup in treatment position.
2. The target organ (prostate) is detected and its position
(or a representation thereof) is measured.
3. When a threshold offset is detected the patient is
repositioned using a variety of methods.
4. A reassessment of the position is usually performed
before start of treatment.
On-line adjustment modalities
• Ultrasound detection
• Implanted Markers
–
–
Active
Passive
∗ US
∗ EPID
Ultrasound adjustment
Verification of Ultrasound based adjustment[3]
• A total of 19 patients have been enrolled.
• Compiled data of 17 patients will be presented.
• 275 treatment setups with markers have been analyzed.
• 156 image pairs using Ultrasound adjustment.
• 119 image pairs using traditional treatment.
25
no BAT
BAT
20
15
Antero-posterior (mm)
10
5
0
-5
-10
-15
-20
-25
-25
-20
-15
-10
-5
0
5
10
Cranio-Caudal (mm)
Figure 1: Coronal Plane
15
20
25
25
no BAT
BAT
20
Cranio-caudal (GT is positive) (mm)
15
10
5
0
-5
-10
-15
-20
-25
-25
-20
-15
-10
-5
0
5
10
Lateral (mm)
Figure 2: Sagittal Plane
15
20
25
25
no BAT
BAT
20
Antero-posterior (Up is positive) (mm)
15
10
5
0
-5
-10
-15
-20
-25
-25
-20
-15
-10
-5
0
5
10
15
Lateral (mm)
Figure 3: Transverse plane
20
25
Why doesn’t Ultrasound aided repositioning perform better?
Why doesn’t Ultrasound aided repositioning perform better?
• User subjectivity[4].
Why doesn’t Ultrasound aided repositioning perform better?
• User subjectivity[4].
• Discrepancy between contoured anatomy and actual
anatomy[5].
Figure 4: Inter–observer variation of contouring bladder
Why doesn’t Ultrasound aided repositioning perform better?
• User subjectivity[4].
• Discrepancy between contoured anatomy and actual
anatomy[5].
• Intra–fractional movement between the Ultrasound
procedure and the treatment[6].
Why doesn’t Ultrasound aided repositioning perform better?
• User subjectivity[4].
• Discrepancy between contoured anatomy and actual
anatomy[5].
• Intra–fractional movement between the Ultrasound
procedure and the treatment[6].
• Heisenberg ∆x∆p >
h̄
2
Improvements to reduce User subjectivity
Improvements to reduce User subjectivity
1. BAT (Nomos Inc)
• Live CT–contour projection during acquisition.
• Seed localizers (Visicoil, Radiomed)
Improvements to reduce User subjectivity
1. BAT (Nomos Inc)
• Live CT–contour projection during acquisition.
• Seed localizers (Visicoil, Radiomed)
2. Zmed (Varian Inc)
• Pseudo 3D acquisition.
• Live adjustment.
Improvements to reduce User subjectivity
1. BAT (Nomos Inc)
• Live CT–contour projection during acquisition.
• Seed localizers (Visicoil, Radiomed)
2. Zmed (Varian Inc)
• Pseudo 3D acquisition.
• Live adjustment.
3. IBeam (CMS Inc)
• Pseudo 3D acquisition and reconstruction.
• Ultrasound contouring and export to planning
system.
Marker based protocols
Marker based protocols
Ultrasound
Do not look for prostate, but look for long
seeds (need 3D acquisition)
EPID
Take images and visualize the seeds
Calypso
Seeds reflect RF radiation use detector to see
where they are.
Assessment of marker daily repositioning
The protocol used is as follows:
1. Obtain 2 images (45◦ and 315◦ ) using low exposure.
Assessment of marker daily repositioning
The protocol used is as follows:
1. Obtain 2 images (45◦ and 315◦ ) using low exposure.
2. Determine 3D position of the markers or any measure
allowing to determine the position of the target w/
respect to the iso–center.
Assessment of marker daily repositioning
The protocol used is as follows:
1. Obtain 2 images (45◦ and 315◦ ) using low exposure.
2. Determine 3D position of the markers or any measure
allowing to determine the position of the target w/
respect to the iso–center.
3. Calculate and perform shifts necessary to correct the
patient’s position.
Results
An IRB approved study with 15 patients total accrual
(currently 14 accrued) was initiated, using the
abovementioned protocol.
Results
An IRB approved study with 15 patients total accrual
(currently 14 accrued) was initiated, using the
abovementioned protocol.
Results shown here are for 10 patients. A total of 270
treatments were analyzed (27 fractions average per
patient).
Results
An IRB approved study with 15 patients total accrual
(currently 14 accrued) was initiated, using the
abovementioned protocol.
Results shown here are for 10 patients. A total of 270
treatments were analyzed (27 fractions average per
patient).
Every fraction had before and after measurements.
Resulting in 540 total position measurements (270 before
and after pairs).
mean (mm)
SD (mm)
before
AP
7.45
5.99
LAT
1.29
5.34
CC
5.12
4.44
after
AP
0.65(P < 0.00001)
2.82(P < 0.00001)
LAT
0.11(P < 0.00001)
2.64(P < 0.00001)
CC
0.46(P < 0.00001)
2.22(P < 0.00001)
Table 1: Pooled results, significance of reduction using t–
test(mean) and F–test(Variances)
After
Before
30
20
AP (mm)
10
0
-10
-20
-30
-30
Figure 5:
-20
-10
0
CC (mm)
10
20
30
Scatterplot showing before and after positions for a total of 540 measurements. All sizes are in millimeter. The ellipses cover 95% of all data
points.
Margin Calculations
Σ (mm)
σ (mm)
Margin (mm)
before
AP
3.6
5.08
9.6
LAT
3.41
4.29
8.5
CC
3.88
2.36
8.4
after
AP
0.56
2.64
LAT
0.39
2.37
CC
0.40
1.95
Table 2: Population statistics and margins calculated using
2.5Σ + 0.7σ − 3mm.
Margin Calculations
Σ (mm)
σ (mm)
Margin (mm)
before
AP
3.6
5.08
9.6
LAT
3.41
4.29
8.5
CC
3.88
2.36
8.4
0.25
after
AP
0.56
2.64
LAT
0.39
2.37
CC
0.40
1.95
Table 2: Population statistics and margins calculated using
2.5Σ + 0.7σ − 3mm.
Margin Calculations
Σ (mm)
σ (mm)
Margin (mm)
before
AP
3.6
5.08
9.6
LAT
3.41
4.29
8.5
CC
3.88
2.36
8.4
after
AP
0.56
2.64
0.25
LAT
0.39
2.37
-0.63
CC
0.40
1.95
Table 2: Population statistics and margins calculated using
2.5Σ + 0.7σ − 3mm.
Margin Calculations
Σ (mm)
σ (mm)
Margin (mm)
before
AP
3.6
5.08
9.6
LAT
3.41
4.29
8.5
CC
3.88
2.36
8.4
after
AP
0.56
2.64
0.25
LAT
0.39
2.37
-0.63
CC
0.40
1.95
-0.64
Table 2: Population statistics and margins calculated using
2.5Σ + 0.7σ − 3mm.
Figure 6:
The model proposed by van Herk
we are measuring.
et al.
is not valid at the values
Important!
Important!
The residual errors observed were after the adjustment, but
before the treatment. In order to gauge the efficacy of any
repositioning scheme we need data during the treatment.
Important!
The residual errors observed were after the adjustment, but
before the treatment. In order to gauge the efficacy of any
repositioning scheme we need data during the treatment.
We need to address the following problems:
• Patient position at time of treatment (not before or
after).
• Treatment delivery.
Important!
Important!
There are only two modalities that allow in–treatment
patient monitoring.
• EPID
• Calypso
They need to address the following problems:
• Patient position at time of treatment (not before or
after).
• Treatment delivery.
Calypso
has the (not proven) capability to perform
in–treatment monitoring of the position if the detector
is built in the couch. Dose verification could be
possible if combined with Radiation Dosimetry (Sicel
Technologies Inc.)
EPID
can perform in–treatment position verification
(even for IMRT) and intensity map verification, or even
absolute measurement (A Radiation Dosimeter can
replace one of the seeds).
Figure 7: Portal Image of an IMRT treatment
The previous image was taken using an electronic portal
imager in cumulative mode (e.g. All frames taken are
averaged).
The previous image was taken using an electronic portal
imager in cumulative mode (e.g. All frames taken are
averaged).
Intensity Modulation
This information shows up
as fairly large variations in contrast (steps are at least
5%).
Patient Anatomy
Small signals indicating bony
edges or implanted markers.
Other stuff
Scattered radiation, source variations, (in
short things we don’t want to see).
36000
Patient Image
OpenImage
35500
PixelValue (arbitrary units)
35000
34500
34000
33500
33000
32500
32000
0
50
100
150
200
250
Pixel Number
Figure 8:
Cross–section of two images. One obtained during an IMRT–treatment
(bottom) with the patient in place, the second without the patient (top).
The major difference between the two signals is due to increased scattered radiation reaching the imager and decreased direct signal through
patient attenuation of the primary dose.
35550
Image
adjusted open field
35500
Imager response (arbitrary units)
35450
35400
35350
35300
35250
35200
35150
35100
0
Figure 9:
50
100
150
200
Pixel number
250
300
350
The same cross–section of two images as shown in Figure 8. The open
field is scaled linearly to minimize the χ2 –goodness of fit. The fit is
performed on the pixels inside of the field only. The good agreement
outside of the field is likely due to chance and the fact that a small
phantom will not change the out–of–field scatter significantly.
(a)
Treatment
(b) Open Field
(c) Subtracted
Field
Figure 10:
Two–parameter adaptive subtraction, using identical image geometry.
An open image was obtained using the intensity map, the phantom
placed on the table was positioned in the beam and a new images was
acquired.
Practical application
1. Detect the field outline.
2. Adjust magnification and position of open field to
match the original portal image.
3. Mathematical morphological erosion (to stay within the
boundaries).
4. Minimize χ2 -function for all pixels within detected field.
5. Determine a and b.
6. Subtract adjusted open field from original portal image.
7. Renormalize values for display.
Figure 11: Adjusted
But wait! There’s more
But wait! There’s more
The final χ2 –value is sensitive to the intensity modulation
function. If the incorrect value is used the value will be
higher than normal.
But wait! There’s more
The final χ2 –value is sensitive to the intensity modulation
function. If the incorrect value is used the value will be
higher than normal.
This allows to verify intensity modulated fields, if we know
how they would interact with an electronic portal imaging
device.
Pixel j
Pixel i
Figure 12:
The χ2 value is calculated along each line shown in this graphical representation. This is repeated for all pixels in the image. The “error”–map
is generated by multiplying the four values, after which thresholding is
performed. Pixels that remain highlighted indicate errors in the intensity map.
Figure 13: Intensity map , With 6mm error introduced during
10% of the treatment
Figure 14: 6mm leaf error introduced in 1 segment (10.11%
of the treatment
Figure 15: Fit map , thresholded to point out error in treatment
Things to do
• Determine adequate threshold values (ROC analysis).
• Generate EPID-open fields.
• Can we “pre–port”?
Things to do
• Determine adequate threshold values (ROC analysis).
• Generate EPID-open fields.
• Can we “pre–port”?
Delivering a small amount of dose to the patient,
mimicking the treatment at a smaller dose rate.
Things to do
• Determine adequate threshold values (ROC analysis).
• Generate EPID-open fields.
• Can we “pre–port”?
Delivering a small amount of dose to the patient,
mimicking the treatment at a smaller dose rate.
what is an adequate pre-port amount 5MU?
Acknowledgments
• Therapists
• Jesse Larrew
• Pat McDermott
• Part of this research was sponsored by the Uniformed
Services University of the Health Sciences through
Contract No. MDA905-92-C-0009 awarded to the
Henry M. Jackson Foundation for the Advancement of
Military Medicine. The content of the information does
not necessarily reflect the position or the policy of the
government, and no official endorsement should be
inferred.
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of geometric uncertainties in treatment plan evaluation.
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ultrasound based image-guided radiation tre atment,
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