Image-guided management of uncertainties in scanned particle therapy

Image-guided management of uncertainties
in scanned particle therapy
13 March 2014
Giovanni Fattori
Department of Electronics, Information and Bioengineering
Politecnico di Milano
-- PhD Thesis Defense --

Presentation outline
Image-guided particle therapy @ CNAO
 Optical tracking and X-ray imaging
 Development and clinical implementation
 Geometrical accuracy
 Dosimetrical aspects
Treatment of moving targets
 Optical tracking for real time motion monitoring
 4D dosimetry (experimental studies)
2 / 35

Particle radiotherapy
1. PHYSICS
Favorable tissue depth-dose distribution
2. RELATIVE BIOLOGICAL EFFECTIVENESS
Microscopic spatial energy distribution
 DOSE DELIVERY
Active scanning
Kramer et al. [2012 J. Phys]
Kramer et al. [2010 Eur. Phys. J. D ]
Durante et al. [2009 Nat Rev Clin Oncol]
3 / 35
RBE =
Dphoton
Dion iso

Uncertainties in therapy
Challenge
Development of technologies for therapy to manage treatment
uncertainties
 Setup errors
 Moving targets
Critical issues
 Particle sensitivity to tissue density
variation
 Organ motion
• Inter-fractional
• Intra-fractional
 Treatment of moving targets
 Questionable cost-benefit ratio
4 / 35

IGRT: the case of CNAO
 In-room imaging
 6 DOF treatment couch (PPS)
 Solutions for real time monitoring
OTS OPTICAL TRACKING SYSTEM
• Non-invasive
• Real time patient monitoring
PVS PATIENT VERIFICATION SYSTEM
• Schaer-engineering: isocentric double
projection
• Custom system for robotic imaging:
Radiograph and Cone Beam CT
TREATMENT ROOM 1-3 TREATMENT ROOM 2
TREATMENT
SETUP
PPS
OTS
PVS
NOMINAL
POSITION
5 / 35

Optical tracking
 Point-based patient registration and motion
monitoring
SMART, BTS Bioengineering
3 free-standing infrared TVC cameras
15 min calibration procedure
• Accuracy = 0.3 mm in 1 m3 volume
• Frequency: 70/100 Hz
PATIENT MODEL OPTICAL DATA
6 / 35
What is needed: nominal point-based geometry from planning CT images
Sub millimeter scale accuracy (1-3 mm slice thickness in CT)
 Manual segmentation
 Low 3D accuracy
 Inter-operator variability
 Automatic fiducials localization in CT images

Automatic fiducials localization in CT images
SURFACE
EXTRACTION
SURFACE
PROCESSING
MARKER
RECOGNITION
Geometric filters
1. 10 mm < diag < 22 mm
2. Hausdorff < 20 mm2
3. n° triangles < 650
4. Side difference < 5 mm
CANDIDATE SURFACE
Geometrical prior knowledge: aluminum spheres. (1cm diameter, 1800 HU)
Fattori et al. [2012 IEEE TBME]
7 / 35

Automatic fiducials localization in CT images
 LOCALIZATION ACCURACY
3 mm 1 mm
I 0.1640 0.2113
II 0.2374 0.0322
II
I
0.1414 0.2054
 LOCALIZATION ROBUSTNESS
Accuracy: 20 μm
N° patients CT resolution
Fiducials per
patient
N° of fiducials Fiducials found
10 head
25 thorax
1.27x1.27x3 mm
6/8 233 215
3 head 0 0 0
92.3%
LEICA LTD 500
** CLINICAL USE SINCE 2011 **
Fattori et al. [2012 IEEE TBME]
 High true positive ratio
 No false positive
 High accuracy
3D error [mm]
8 / 35

In-room imaging system for CNAO central room
Specifications:
 Limited operating space (Horizontal & Vertical beam lines)
 X-ray radiographs and Cone Beam CT
 Registration performance comparable with lateral rooms
 Geometric residual error < 1mm / 1
 Patient setup procedure < 2 min
 Integration with existing technologies (PPS, PACS)
Project leader:
Image registration:
Robot & Safety:
Software:
G. Baroni
M. Riboldi
G. Fattori
M. Peroni
P. Cerveri
A. Pella
G. Fattori
G. Fattori
M. Riboldi
Treatment Isocenter
Imaging Isocenter
9 / 35
2 YEARS PROJECT
1st year: 2D-3D
2nd year: 3D-3D

Hardware components 10 / 35
Custom C-arm
SID=1772.2 mm;
SAD=1272.2mm
Robotic arm
Kawasaki
ZX300S
Flat panel detector
Varian 4030D
(30 Hz, 2048x1536 pixels
193.8x194 um pitch)
X-ray source
Varian A277
Generator
Sedecal HF
series
Exposure controlGantry pendant Intrusion detection

X-ray Patient Positioning Verification software
 Gantry control
 Exposure settings
 Automatic registration
 PPS communication
 PACS integration
ROI
Interactive
checkerboard
Images
overlay
Visualization
settings
Automatic
registration
Manual
registration
ROI
Load/Save
11 / 35

Geometry calibration
Calibration phantom (Brandis
Medizintechnik Vertriebs GmbH,
Weinheim, Germany)
36 metal bearings
Optimization of on-plane
projection error
Free parameters:
• Image center
• Panel orientation
System geometric daily QA
SOURCE
(0,0)
Center of
rotation
(-cx,-cy)
Panel
rotations
Imaging Isocenter
12 / 35

System installation in CNAO Room 2
CLINICAL WORKFLOW
PPS MOTION TO TREATMENT ISOCENTER
OTS Point-based registration at treatment isocenter
(≈1min)
PPS MOTION TO IMAGING ISOCENTER (≈3min)
Image-based registration at imaging isocenter
PPS MOTION TO TREATMENT ISOCENTER
(≈3min)
Treatment start
1. Images acquisition
2. Automatic registration
(≈1min)
3. PPS correction
4. Images acquisition
(verification)
5. Automatic registration
Image-based registration
13 / 35

XPPV – Double projection patient registration
 Projection angels
• RL (right-left)
• AP (anterior-posterior)
 Flat panel
• Resolution: 2048x1536 with 193.8x194 um pitch
 2D-3D registration (< 1 min)
• Adapted from plastimatch reg23
• Gradient difference metric
• Evolutionary 1+1 optimizer
14 / 35

XPPV – CBCT patient registration
 Gantry rotation
• 220 in ≈ 40 sec (5.4 /sec)
• Post acquisition recover ≈ 20 sec
 Flat panel
• Frame rate = 15Hz (0.36 per image)
• Resolution: 1024x768 with 387.7x388 um pitch
 CBCT (adapted from plastimatch FDK)
• Field-of-view: 30x20x15 cm
• Resolution: 1.1x1.1x1.5 mm (≈70 sec)
 3D-3D registration (ITK) (< 1 min)
• Normalized Mutual Information metric
• Nelder Meade optimizer

CBCT Image Quality
SENSITOMETRY (CTP 404)SPATIAL RESOLUTION (CTP
528)
CATPHAN-600 PHANTOM
Air
PMP
LDPE Delrin
Teflon
1.25 mm
1 mm
0.83 mm
1.1x1.5 mm
1.67 mm
2.5 mm
FUTURE DEVELOPMENTS
 Artifacts analysis/mitigation
• Shading artifact
• Streaks artifact
 Large deviation within inserts
16 / 35

System geometric accuracy
 PVS / OTS agreement
1. Manual alignment at treatment Iso (lasers)
2. OTS setup verification
3. Reg23 setup verification
 Recovery of 3 transformation (range: 1 cm, 5°)
3. Setup perturbation
Rx 1°
Setup error (3D/3D)
Setup error (2D/3D)
Delta wrt imposed error (3D/3D)
X 1mm
Y 1mmZ 1mm
Rx 1°
Ry 1° Rz 1°
Setup error (3D/3D)
Setup error (2D/3D)
Z 1mm
Rx 1°
Ry 1°
Setup error (3D/3D)
Setup error (2D/3D)
PTW P43029
 Good agreement between OTS and PVS
 Good agreement between 2D-3D and 3D-3D
17 / 35

Registration performance (2D-3D)
** ROOM 2 IN OPERATION SINCE APRIL 2013 WITH DOUBLE PROJECTION **
Simulation of 10 setup errors (range: 1 cm, 5°)
1. 2D-3D Correction vector calculation
2. Implementation of PPS correction
3. 2D-3D Correction vector calculation
 Sub-millimeter / sub-gradual residual error
Translational error Rotational error
Alderson RANDO man
Antropomorphic phantom
18 / 35

Clinical implementation (2D-3D)
HEAD
21 pat. 633 fract.
PELVIS
8 pat. 236 fract.
PROSTATE
2 pat. 32 fract.
 ROOM 2: Custom robotic imaging + XPPV
 ROOM 1, 3: Shaer Engineering imaging + MedCom Verisuite PVS
HEAD
5 pat. 105 fract.
PELVIS
5 pat. 46 fract.
0.94 mm 0.9°
1.32 mm 1.34°
2.82 mm
1.28°
2.58 mm
1.04 °
1.56 mm
0.58°
19 / 35
Residual error
Correction vector

Dosimetric consequences of setup errors after IGRT
Nominal
Setup errors
D95
D105D05
HU-WE
Setup
CI =
Vol95%
VolCTV
IC =
(MaxDose - MinDose)
MinDose
 Purpose
To provide clinicians with dosimetric information
about treatment setup besides the residual
geometric error
Range
 Interpretation of results
Indexes clearly readable by clinicians
• Envelope DVH (ΔD95CTV, ΔD105CTV, ΔD05OAR)
• Conformity Index for CTV
• Inhomogeneity Coefficient for CTV
 Materials and Methods
• Image processing & TRiP98 (M. Krämer)
• Comparison of treatment delivery in nominal
situation and in presence of uncertainty
(Optimum=1)
(Optimum =0)
20 / 35

Simulation of setup errors
ERROR SPACE SAMPLING
Orthogonal sampling (64 simulations)
• 6 Dimensions (translations,
rotations)
Implementation of isocentric 6DOF Correction vector on patient CT
1. Image resampling T
2. Dose calculation
3. Dose cube resampling Tinv
Figure 2. Plan no 299. (Upper part) Convergence of the various algorithms as function of iteration
steps (left hand side) and computation time (right hand side). (Lower part) DVH (left hand side)
and dose distribution in a CT-slice (right hand side). Only results of CGFR optimization are shown.
The indicated isodoses are in percent of the prescribed dose.
For the sake of completeness we additionally present the Levenberg–Marquardt
minimization (LMM) which we also investigated. As far as the number of iterations are
concerned (see figure 1, upper left part) LMM looks quite promising but the computation
times are extremely large (see figure 1, upper right part). The disadvantage of LMM is that in
every iteration step a large system of linear equations has to be solved. Solving the system of
linear equations with the Cholesky decomposition requires about 60 times more computation
time compared with CGFR (figure 1). We investigated alternative equation solvers, for
example the iterative Krylov subspace methods. With the Krylov subspace methods the
computation times could be decreased by a factor of approximately 3 (Buschbacher 2009),
which is by far not enough to allow the usage of LMM in our context.
We further investigated the distribution of the resultant particle numbers on the raster
grid. This is important because large fluctuations of particle numbers between rasterspots
might require changing of the particle intensities by the ion accelerator system. This is time
consuming and could potentially decrease the number of patients treated per day. We examined
some treatment plans and independently from the chosen algorithm we did not observe large
fluctuations of particle numbers between neighbouring rasterspots.
20%
40%
60%
80%
95%
105%
> 105%Figure 2. Plan no 299. (Upper part) Convergence of the various algorithms as function of iteration
steps (left hand side) and computation time (right hand side). (Lower part) DVH (left hand side)
and dose distribution in a CT-slice (right hand side). Only results of CGFR optimization are shown.
The indicated isodoses are in percent of the prescribed dose.
For the sake of completeness we additionally present the Levenberg–Marquardt
minimization (LMM) which we also investigated. As far as the number of iterations are
concerned (see figure 1, upper left part) LMM looks quite promising but the computation
times are extremely large (see figure 1, upper right part). The disadvantage of LMM is that in
every iteration step a large system of linear equations has to be solved. Solving the system of
linear equations with the Cholesky decomposition requires about 60 times more computation
time compared with CGFR (figure 1). We investigated alternative equation solvers, for
example the iterative Krylov subspace methods. With the Krylov subspace methods the
computation times could be decreased by a factor of approximately 3 (Buschbacher 2009),
which is by far not enough to allow the usage of LMM in our context.
We further investigated the distribution of the resultant particle numbers on the raster
grid. This is important because large fluctuations of particle numbers between rasterspots
might require changing of the particle intensities by the ion accelerator system. This is time
consuming and could potentially decrease the number of patients treated per day. We examined
some treatment plans and independently from the chosen algorithm we did not observe large
fluctuations of particle numbers between neighbouring rasterspots.
6. Summary and conclusion
The task for the optimization of RBE-weighted dose is
depending nonlinearly on the particle numbers
20%
40%
60%
80%
95%
105%
> 105%
-30° Dos
+30° CT
1 mm 1 setup error
21 / 35

25%
20%
15%
10%
5%
0%
25%
20%
15%
10%
5%
0%
Simulation study: 5 head chordoma patients
• 4.4 Gy (RBE)
• 2mm CTV-to-PTV margin
• OAR: Brainstem
• 2-3 treatment fields
22 / 35
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
Simulation study design:
1. Setup error simulation
2. On worst case envelope,
simulation of range uncertainty
IC CI Δ D95CTV Δ D105CTV Δ D05OAR
Worst cases for D95CTV and D05OAR
25%
20%
15%
10%
5%
0%
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
0.5
1
1.5
2
2.5
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25
1 2 3 4 5
0
5
10
15
20
25

Patient 1: worst case simulation for D95CTV
ce of the various algorithms as function of iteration
ght hand side). (Lower part) DVH (left hand side)
de). Only results of CGFR optimization are shown.
scribed dose.
20%
40%
60%
80%
95%
105%
> 105%
rgence of the various algorithms as function of iteration
me (right hand side). (Lower part) DVH (left hand side)
nd side). Only results of CGFR optimization are shown.
e prescribed dose.
ionally present the Levenberg–Marquardt
ed. As far as the number of iterations are
looks quite promising but the computation
ht part). The disadvantage of LMM is that in
20%
40%
60%
80%
95%
105%
> 105%
SETUP ERROR
SETUP AND RANGE ERROR
(Expected) Results:
 Dose coverage remains acceptable
 Conformity is reduced
 Inhomogeneity is increased
 Quantification of dosimetric deviations wrt nominal condition
23 / 35
LL:
AP:
SI:
Pitch:
Rotate:
Roll:
-0.34mm
-0.87mm
-0.97mm
-0.19°
-0.97°
0.78°
Setup error
+
Rel WEL +2.6%

IGRT: Conclusion and Limitations
 Tools for automated fiducials localization in treatment planning CT images
 Development of a custom robotic in-room imaging system
 Implementation at CNAO
 Double projection: Clinical use since April 2013
 CBCT: foreseen for April 2014
 Overall residual setup error following CNAO IGRT strategy (OTS + in-
room imaging): Millimeter and degree scale.
Tool to provide valuable dosimetric information to clinicians
Not far from pre-treatment setup verification: about 10 mins (single
simulation)
Treatment plan robustness test: 2 hours
24 / 35

From static to moving target with active scanning
X-RAY SOFT-TISSUE IMAGING
US MRI
PHASE1
4D IMAGING
(4D CT)
PHASEN
OPTICAL TRACKING +
CORRELATION MODELS
BASIC ASSUMPTION:
TARGET MOTION REPEATIBILTY
4D TREATMENT PLAN (TP)
TIME RESOLVED TREATMENT DELIVERY
ENERGY ADAPTATIONLATERAL DEFLECTION
MOTION MONITORING
DOSE DELIVERY (beam tracking)
MOTION MONITORING SYSTEMS
 Real time feedback to TCS to drive
the treatment delivery
 Verify consistency wrt TP
 Trigger image acquisition
MOTION MITIGATION STRATEGIES
 BEAM TRACKING
 GATING
 RESCANNING
Direct observation Surrogate signal
25 / 35

Optical tracking for time resolved treatment
PURPOSE
To interface a commercial solution for optical tracking with a Therapy Control System
for particles: beam tracking and gating
WHAT IS REQUIRED
 Real time monitoring of multiple surrogates
 Compatibility with 4DCT acquisition protocols
 Real time communication with TCS: delay compensation
Static
Residual
Interplay
26 / 35

The tracking code package
Optical Tracking System Therapy Control System
Correction
vector
3D OTS DATA
BTU
Wedge
range shifter
Steering
magnets
Depth
compensation
Lateral
compensation100 Hz frame rate
LABELLER
TARGE
T
FRAMES
INTERPOLATION
POLYNOMIAL
COEFFICIENTS
TIMECRITICALTHREADOTSDRIVENTHREAD
BREATHING
SIGNAL
MOTION PHASE
DETECTION
CORRELATION MODELS [ x y z ]
[ MP ]
PATIENT
MODEL
MOTION
PHASE TABLE
RCS
TRANSFORM
MATRIX
SHARED RESOURCES
DIGITALCOMMUNICATION(UDPSOCKET)
Treatment
plan
Fattori et al. [2012 AAPM]
KEY FEATURES:
 Phase/Amplitude 4DCT
 Ethernet link (UDP)
 Signal time prediction
 Ready for Gating and Beam
tracking experiments
27 / 35

Procedure for system latencies quantification
DEPTH
WE
compensation
Mean
Std.Dev
1 mm 27.43 7.51
9 mm 34.1 6.29
5measurements(0,
DEPTH
Calculatedbydiffere
TOTAL–LATERAL
Laser distantiometer
OTS marker
Fattori et al. [2013 TCRT Express]
LAT ERAL
OTSbenchmark with Laser distantiometer (1KHz frm.rate)
5 measurements(0,5,10,15,20 msec.advance prediction)
DEPT H
Calculated by difference
TOTAL– LATERAL
Motion OTS
LATERAL
OTSbenchmark with Laser distantiometer (1KHz frm.rate)
5 measurements(0,5,10,15,20 msec.advance prediction)
DEPTH
TOTAL– LATERAL
Motion OTS TCS
34
(mea
1
MAGNETS
WEDGEFILTER
LATERAL
14.6 msec
DEPT H
TOTAL– LATERAL
Motion OTS
28 / 35

Signal time prediction accuracy
 Polynomial fitting: Ist order
 5 samples history, 100 Hz data
 Time compensated Vs. Reference
REFERENCE
NON COMPENSATED
TIME COMPENSATED
• Reference = Non-compensated + δ (=14.6ms)
• 10 mins acquisition:
RMS = 0.05 mm
RMS = 0.1 mm

Beam tracking @ GSI: Setup
Purpose:
 To evaluate the feasibility of
OTS driven 4D treatment
Steidl et al. [2012 PMB]
Breathing phantom
Correlated target/thorax motion
10x5x10 cm (x,y,depth)
Treatment plan
1 Gy homogeneous, 12C
35 mm side
4DCT: 8 MPh, phase
binned
Motion monitoring:
SMART-DX100, 2 TVC
Dose measurement
16 PTW Pinpoint ionization
30 / 35

Beam tracking @ GSI: Results
Note:
 Pure translational target motion
 No soft tissue material inside the thorax
 Excellent target and thorax motion repeatibility
Median(IQR) 2.0 (25.9) % -0.3 (2.3) % -1.2 (9.3) %
Measured delta wrt static irradiation
31 / 35

Lateral beam tracking @ CNAO
 Phantom
- Planar target motion (2D)
- 25 mm (lat) 18 mm (vert) peak-to-peak
- Planarity: median 0.038 mm (IQR:0.09)
- Repeatibility: mean std 0.18 0.3 mm
 ‘Treatment plan’
- Squared PTV
- 6 cm side
 Purpose
Proof the OTS/TCS integration
STATIC TRACKING INTERPLAY GATING
Average flatness 4 % 5.7 % 24 % 9.5 %
Average penumbra 9.2 mm 9 mm 19 mm 9.1 mm
32 / 35
Pella, Fattori et al. [PTCOG52]

Moving targets: Conclusion and Limitations
 General solution for OTS/TCS integration was described
 Ethernet link, UDP protocol
 Procedure for delays quantification
 The tracking code package available for research (CNAO, GSI,… )
 Development and benchmark of int-ext correlation model (M. Seregni)
 Functional gating and beam tracking modules
 GSI: 3D optical driven beam tracking
 CNAO: 2D optical driven beam tracking and gating
 BEAM TRACKING: how to deal with deviations from treatment plan?
1. Real time dose compensation with beam tracking [Lüchtenborg 2012, Med Phys]
2. Dose changes outside the VOIs (inverse interplay effect)
33 / 35

Final remarks
 IGRT
1. Development and implementation of state-of-the art methods for IGRT
• Point based
• Anatomical information (bone anatomy + soft tissue imaging)
2. CNAO Room 2: Custom system for robotic imaging:
(!) 2D-3D available for clinical use
(!) CBCT almost available for clinical use
(!) Double projection & CBCT dataset: 2D-3D / 3D-3D Comparison
1. GSI: beam tracking, lateral and depth compensation
2. CNAO:
• lateral compensation
• ready for gated treatment:
(!) strategy to compensate for residual motion in the gating window
34 / 35

Future directions
 IGRT @ CNAO
1. ‘CT-of-the-day’ software module
• Pre-treatment dose simulation on the updated CT
• Anatomical information in perspective of PET in-room
2. 4D CBCT
Tailored treatment on patient specific basis:
• Motion reduction: gating + rescanning/overlapped pencil
beams
• Motion compensation: multiple points + tumor tracking
• Adequate strategy for margin definition
35 / 35

Thank you
May 2012 Beamtime, GSI

Image-guided management of uncertainties in scanned particle therapy

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