CECE-Therapy: Patient Motion: Adaptive RT
Adaptive Management of Patient
Motion in Radiotherapy
Di Yan, D.Sc.
William Beaumont Hospitals & Research Institute
Educational Objectives
I.
To learn the options of 4D planning
II.
To understand the sensitivity of 4D planning
on motion uncertainties, as well as the
methods for uncertainty management
III.
To learn the key components of adaptive
treatment process and their functions
Outlines
I.
Geometry based and dosimetry based 4D
planning for motion compensation
II.
Motion uncertainty and its dosimetric
effect on 4D planning. The management
options
III.
Key components and functions of adaptive
treatment process
Page 1
4D Planning: Geometry Based ITV Construction
™
Patient specific target for motion
compensation constructed using
target motion excursion (Margin =
0.5*Excursion)
¤ determined using fluoroscopic
image (Balter JM, et al. Med Phys
1994, 21:913)
™
™
¤ 4D CT or directly from a MPI
image (Underberg Rene, et al.
IJROBP 2005, 63:253-60)
Purely geometric compensation, no
dose distribution is used in the
margin design
Overestimate the target margins
significantly
Motion Effect in Dose Distribution
™
Motion blur effect of dose distribution
has been demonstrated long time ago
using the convolution approach,
spatially invariant dose distribution +
motion pdf (Leong J. PMB 1987, 32:32737)
™
In reality, one should also consider the
effect of patient internal density
variation & the leave interplay effect if
dose is delivered using the MLC based
IMRT (Chui CS, et al. Med Phys 2003,
30:1736-46, & Bortfeld T, et al. Med Phys
2002, 47:2203-20)
4D Dose Summation
Tissue density distribution
Machine output
VoI subvolume position
D (v ) =
n
∑∫
i =1
t∈Ti
dD v
(x t ( v ), ρv t , uvt ) ⋅ dt
dt
In time domain
=∫
(∫ D ( xv , ρv , uv ) ⋅ pdf ( xv , ρv ) ⋅ d xv ⋅ dρv )⋅ pdf ( uv ) ⋅ d uv
=∫
(
=
)
v v
v
v
v
v
v
∫ D ( x , ρ M , u ) ⋅ pdf ( x ) ⋅ d x ⋅ pdf (u ) ⋅ d u
v
∫ D (x,
v
v
v
v
ρ M , u c ) ⋅ pdf ( x ) ⋅ d x
In freq domain
Apply the mean CT
Constant output
Page 2
4D Planning: Dose Based ITV Construction
Courtesy Dr Liang from WBH
pdf
™
™
Dose convolution with motion pdf measured at treatment simulation
Perform the margin calculation iteratively by adjusting beam aperture
Effect of The Prescription Dose
3 cm
™
Target margin is strongly
dependent on the prescription
dose point
85% of the iso
M1 = 7.7 mm
™
Therapeutic ratio could be
future increased by reducing
treatment beam aperture &
allowing higher heterogeneity
dose in the target (Engelsman M,
et al. IJROBP 2001, 51:1290-8)
3 cm
70% of the iso
M1 = 4.1 mm
Inter-patient Heterogeneity
™
™
Target margin depends on the
dose distribution which greatly
relies on the tumor location
M1 = 7.5 mm
M2 = 5.6 mm
M1 = 2.9 mm
M2 = 2.6 mm
With respect to the prescription
dose of 75% ~ 95%, Target
Margin = 0.1 ~ 0.4*Excursion
2.5 cm
Page 3
4D Planning: 4D Inverse Planning
™ Similar to 3D Inverse Planning, but Include the motion
pdf from m-phase 4D CT in the 4D dose summation
(Alexei Trofimov, et al. PMB 2005, 2779-98)
v v
v
v v
v v
D ( v ) = ∫ D ( x ( v ), ρ , uc ) ⋅ pdf ( x , ρ ) ⋅ dx ⋅ dρ
=
Opt
v
v v v
v v v
D ( x1 , ρ1 , uc ) + ⋅ ⋅ ⋅ + D ( xm , ρ m , uc )
m
v
F ( D ( v , u c ) , v ∈ VoIs
)
{ uc}
Zhang P, et al. submitted to IJROBP
4D Planning Methods: Summary
™ 4D Planning Methods for Motion Compensation
ƒ
Geometry based ITV (Margin = 0.5*Excursion),
ƒ
Dosimetry based ITV (Margin = 0.1 ~ 0.4*Excursion)
ƒ
4D inverse planning (Margin = 2 mm)
™ All 4D planning methods perform treatment planning
adaptable to patient motion measured at the pretreatment simulation alone, but not those during the
treatment delivery
Page 4
Clinical Observations
™ Significant inter-treatment baseline variation & breathing
pattern (cycle to cycle) variation (in time domain), but
relatively small variation in motion standard deviation (in
frequency domain). (Geoff Hugo, et al. Radiother Oncol 2006, 78:326331. Jan-Jakob Sonke, et al. IJROBP 2008, 70:590-8)
™ Intra-treatment baseline drift is limited within small group
(5%~10%) of patients
™ Dose response related variations (volume shrinkage,
baseline position change, relative distance change, et al)
could be significant after the first few weeks of treatment
resulting significant dose variation in normal organs
Uncertainties of Motion pdf
™ Variations between the reference motion pdfr and
those during the treatment deliveries, pdftx
SI Displacement
Tx 2
Tx n
Tx 1
…
µ1
µ
µ2
µn
™ Systemic Error,
µ , for the entire treatment
™ Systemic error,
µk , for each treatment
Uncertainties of Motion pdf
™ Uncertainty depends on the motion management
25%
R e fe re n c e
M e a n C o r r e c t io n
B o n e C o r r e c t io n
N o C o r r e c t io n
Motion PDF
20%
15%
10%
5%
0%
-2
- 1 .5
-1
-0 .5
0
0 .5
1
1 .5
2
2 .5
S I D ir e c t io n ( c m )
Page 5
2%
0%
7.2
∆ Dose
-2%
-4%
MeanCorrection
∆µ (mm) ∆σ (mm)
−0.2
0.0
BoneCorrection
1.0
0.2
NoCorrection
2.6
2.0
-6%
-8%
-10%
-12%
Target (SI Direction)
Large Motion pdf Variation
25%
R e fe re n c e
M e a n C o r re c tio n
B o n e C o rr e c tio n
N o C o rre c tio n
Motion PDF
20%
15%
10%
5%
0%
-2
-1
0
1
2
3
S I D ir c tio n io n (c m )
10%
5%
0%
∆ Dose
-5%
7.2
7.7
8.2
8.7
9.2
9.7
-10%
-15%
∆µ (mm) ∆σ (mm)
-20%
-25%
MeanCorrection
-30%
BoneCorrection
-35%
NoCorrection
0.0
10.0
12.0
−0.2
0.5
0.1
-40%
Target SI Directin
Page 6
Motion Uncertainty Management
™ Robust Planning (Timothy Chan, et al. PMB 2006, 51:2567-83)
¤ include the bounds of motion uncertainties
(previously determined) in the pre-treatment
planning
¤ If motion variations are within the bounds, the
treatment plan needs no modification
¤ However, the treated volume can be quite large, if
generic variation, specifically the systematic
variation, is considered
Change in The Mean Target Position (mm)
Clinical Observation: Baseline Variation
20
P a tie n t
P a tie n t
P a tie n t
P a tie n t
P a tie n t
15
1
3
5
7
9
P a tie n t
P a tie n t
P a tie n t
P a tie n t
P a tie n t
2
4
6
8
10
10
5
0
-5
-1 0
σ = 6.7 mm
-1 5
-2 0
S e ssio n 1
S e ssio n 2
S e ssio n 3
S e ssio n 4
S e ssio n 5
S e ssio n 6
S e ssio n 7
S e ssio n 8
G. Hugo, Radiother Oncol, 2006, 78:326-331
Clinical Observation: SD Variation
Change in standard deviation (mm)
20
P a tie n t
P a tie n t
P a tie n t
P a tie n t
P a tie n t
15
1
3
5
7
9
P a tie n t
P a tie n t
P a tie n t
P a tie n t
P a tie n t
2
4
6
8
10
10
5
0
-5
-1 0
σ = 1.7 mm
-1 5
-2 0
S e ssio n 1
S e s sio n 2
S e s sio n 3
S e s sio n 4
S e ss io n 5
S e ss io n 6
S e ss io n 7
S e ssio n 8
G. Hugo, Radiother Oncol, 2006, 78:326-331
Page 7
Motion Uncertainty Management
™ Adaptive Management
¤ Modify the treatment plan to cope with the patient
specific variations while the treatment is running
¤ Identify characteristics of individual motion to
provide a proper decision for baseline correction
or adaptive planning modification
¤ Dose feedback to support the adaptive planning
evaluation & modification
Model Identification Adaptive Control
(MIAC) Radiotherapy Process
ADJUSTMENT
MECHANISM
Goals
ADAPTIVE
PLANNING
MOTION
IDENTIFICATION
Plan
TREATMENT
DELIVERY SYSTEM
Delivered Dose feedback
Adaptive Motion Management
™
Motion Identification & Management
¤ Baseline Variation – detected and corrected
using onboard CBCT directly (G. Hugo, IJROBP
2007, 69:1634-41)
¤ The pdf Pattern Variation – detected using
fluoroscopic image, CB projection images or
a surrogate such as surface motion detection
Page 8
Online Motion Verification (CB Fluoro Imaging)
kV Fluoro
10%
-12
8%
-8
Probability
Sup.<--- Tumor Position (mm) --->Inf.
Ref or DRF
-4
0
4
8
Ref 0.0 (4.5)
6%
Port -3.5(4.0)
4%
2%
Ref 0.0 (4.5)
Port -3.5 (4.0)
12
0
5
10
15
Time (second)
20
25
0%
30
10
5
0
-5
-10
Courtesy Dr Liang from WBH
Online Motion Verification (CB Projection Imaging)
CB Projection
Courtesy Dr Hugo from WBH
Adaptive Motion Management
™
Adaptive Planning Modification
¤ Dose feedback + 4D robust inverse planning
¤ Include all pre-measured pdfs in the planning
v
Dn (v , u c ) = Dk ( v )
v v
v
v v
v v
+ ( n − k ) ⋅ ∫ d ( x ( v ), ρ , u c ) ⋅ pdf 1− k ( x , ρ ) ⋅ d x ⋅ d ρ
Opt
v
v
F ( D n ( v , u c ) , v ∈ VoIs
)
{ uc }
Page 9
Adaptive Motion Management: Summary
™ The most important issue in managing patient motion is
to eliminate the baseline variation
™ Daily CBCT imaging localization & correction could be
the most efficient method to perform this task
™ Intra-treatment motion pdf variation can be detected
using CB fluoro, CB projection imaging or (maybe) a
surface surrogate. This detection is used to guide the
selection of the adjustments
™ Adaptive planning modification = Dose Feedback + 4D
Robust Inverse Planning
Page 10