7/24/2014
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DEPT OF RADIATION ONCOLOGY
Fast, near real-time, Monte Carlo dose
calculations using GPU
Xun Jia Ph.D.
DEPT OF RADIATION ONCOLOGY
Outline
GPU Monte Carlo
Clinical Applications
Conclusions
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DEPT OF RADIATION ONCOLOGY
Outline
GPU Monte Carlo
Clinical Applications
Conclusions
3
7/24/2014
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DEPT OF RADIATION ONCOLOGY
GPU A specialized accelerator (personal super-computer)
4-16 cores >1000 cores
Comparison CPU/GPU peak processing power
Comparison CPU/GPU performance in linear
algebra solver
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DEPT OF RADIATION ONCOLOGY
GPU Advantages
High processing power: 3-4 TFLOPS per card nowadays
Low cost: order of magnitude lower than CPU with similar
processing power
Easy to set up, maintain, and access
Full system control: do not rely on 3rd party management
Low burden of data communication: card is local at the
computer
Suitable for Medical Physics problems
Jia et. al. Review Article, PMB, 59, R151(2014)
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DEPT OF RADIATION ONCOLOGY
GPU Monte Carlo project
Since 2009 in UCSD
Utilize GPU to accelerate MC simulations in medical
physics
Use appropriate physics model to maintain accuracy
Design GPU-friendly implementations for high efficiency
Apply developed codes to solve medical physics
problems
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7/24/2014
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DEPT OF RADIATION ONCOLOGY
GPU Monte Carlo project Published >10 papers on peer-reviewed journals, with 6 more
under review/in preparation, and >20 conference presentations
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Four GPU training courses
Upcoming 5th one in Oct
2014 at UTSW
DEPT OF RADIATION ONCOLOGY
GPU Monte Carlo project
A wide spectrum of GPU-based MC packages
gDPM: MV photon/electron (2009)
gMCDRR/gCTD: kV photon (2011)
gPMC: proton (+electron) (2012)
gBMC: brachytherapy dose calculations (2014)
g????: MV photon/electron, openCL (2014)
……
Developing new GPU packages
Unifying code interface
Solve clinical problems
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DEPT OF RADIATION ONCOLOGY
gDPM gDPM
Maintain DPM physics and hence accuracy
Seek for optimal implementation and high efficiency
Phase space source model with GPU-friendly implementation
Results
Jia et. al., Phys. Med. Biol., 55, 3077 (2010)
Jia et. al., Phys. Med. Biol., 56, 7107 (2011)
Townson et. al., Phys. Med. Biol., 58, 4341(2013)
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DEPT OF RADIATION ONCOLOGY
• Average relative uncertainty, t-statistical test passing rate, execution
time T, and speed-up factor
• Multi-GPU
Source type # of Histories Case D/D
(%)
Pall
(%) TCPU (sec) TGPU (sec) TCPU/TGPU
20MeV e 2.5×106 water-lung-water 0.98 99.9 117.5 1.71 69.7
20MeV e 2.5×106 water-bone-water 0.99 99.8 127.0 1.65 77.0
6MV p 2.5×108 water-lung-water 0.72 97.7 1403.7 16.1 87.2
6MV p 2.5×108 water-bone-water 0.64 97.5 1741.0 20.5 84.9
6MV p 2.5×108 VMAT Prostate patient 0.78 N/A N/A 39.6 N/A
6MV p 2.5×108 IMRT HN patient 0.57 N/A N/A 36.1 N/A
Source type # of Histories Case TGPU (sec) T4GPU (sec) TGPU/T4GPU
6MV Photon 4×109 water-lung-water 312.82 78.4 3.99
6MV Photon 4×109 water-bone-water 403.75 101.19 3.99
CPU: Intel Xeon processor with 2.27GHz
GPU: NVIDIA Tesla C2050
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gDPM
DEPT OF RADIATION ONCOLOGY
Automatic commissioning
Phase spacelet (PSL)
Adjust PSL weights to match calculation with measurements
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gDPM Tian et. al., submitted to Phys. Med. Biol. (2014)
0 5 10 15 20 25 30-0.03
0
0.03
0.06
Do
se
diff
ere
nce
(Gy)
Depth (cm)
-0.02
0
0.02
0.04
Do
se
diff
ere
nce
(Gy) 0.1
0.3
0.5
0.7
0.9
1.1
Depth (cm)
Do
se
(G
y)
desired
uncommissioned
commissioned
0 5 10 15 20 25 30
0.2
0.4
0.6
0.8
1
Depth (cm)
Dose (
Gy)
desired
uncommissioned
commissioned0 5 10 15 20 25 30
0.2
0.4
0.6
0.8
1
Depth (cm)
Dose (
Gy)
desired
uncommissioned
commissioned
1.5x1.5
5x5
10x1015x15
1.5x1.5
15x15
(a)
(b)
(c)
-0.03-0.02-0.01
00.010.02
Diff
ere
nce
(Gy)
-12 -6 0 6 120
0.1
0.2
0.3
0.4
0.5
Off-axis distance (cm)
Do
se
(G
y)
4 5 6 7 8 9 10 110
0.2
0.4
0.6
0.8
1
Off-axis distance (cm)
Do
se
(G
y)
desired
uncommissioned
commissioned
4 5 6 7 8 9 10 11-0.04-0.03-0.02-0.01
00.01
Off-axis distance (cm)
Diffe
ren
ce
(Gy)
4 5 6 7 8 9 10 110.04
-0.03-0.02-0.01
00.01
Off-axis distance (cm)
Diffe
ren
ce
(Gy)
-0.04
-0.02
0
0.02
Diffe
ren
ce
(Gy) -0.07
-0.05-0.03-0.010.01
0.03
Diffe
ren
ce
(Gy)
-0.05
-0.03
-0.01
0.01
Diffe
ren
ce
(Gy) -0.06
-0.04
-0.02
0
0.02
Diffe
ren
ce
(Gy) 4 5 6 7 8 9 10 11
0
0.2
0.4
0.6
0.8
1
Off-axis distance (cm)
Do
se
(G
y)
(a1)
(a2)
(a3)
(a4)
(b1)
(b2)
(b3)
(b4)
2.5cm 2.5cm
2.5cm
10cm
10cm 10cm
20cm 20cm
20cm
2.5cm
10cm
20cm
-0.03-0.02-0.01
00.010.02
Diffe
ren
ce
(Gy)
-12 -6 0 6 120
0.1
0.2
0.3
0.4
0.5
Off-axis distance (cm)
Do
se
(G
y)
0
2
4
6
8
10(Gy)
a b
-15 -10 -5 0 5-0.3-0.2-0.1
00.10.2
z position in CT coordinate(cm)
Diffe
ren
ce
(Gy) 0
2
4
6
8
10
Do
se
(Gy)
-15 -10 -5 0 5-0.7
-0.5
-0.3
-0.1
0.1
z position in CT coordinate(cm)
Diffe
ren
ce
(Gy) 0
2
4
6
8
10
Do
se
(Gy)
desired
uncommissioned
commissioned
0
2
4
6
8
10
Do
se
(Gy)
desired
uncommissioned
commissioned
-15 -10 -5 0 5-0.7
-0.5
-0.3
-0.1
0.1
z position in CT coordinate(cm)
Diffe
ren
ce
(Gy)
(a1)
(a2)
(b1)
(b2)
DEPT OF RADIATION ONCOLOGY
gCTD/MCDRR Realistic simulations of CBCT imaging process
Facilitate CBCT related projects
Evaluate patient-specific imaging dose
Primary
Scatter
Noise
CBCT
Time:
1 min per projection simulation
20sec for dose calculations
Jia, et. al., PMB, 57, 577 (2012)
Jia, et. al., Med. Phys., 39, 7368 (2012)
(a) (b)(b)
Ray-tracing MC
Catphan phantom
(a) (b)
(c) (d)
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DEPT OF RADIATION ONCOLOGY
gPMC In collaboration with MGH group
Combines the physics from literatures
Time: 6~22 sec of computation time
In the process of clinical evaluations Jia, et. al., Phys. Med. Biol., 57, 7783 (2012)
Jia et. al., AAPM 2013
Giantsoudi, et. al., AAPM 2014
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DEPT OF RADIATION ONCOLOGY
Other GPU packages GPUMCD
~2 min dose calc + optimization,
one GPU/beam
GMC:
Geant4 implementation
349 sec on GTX 580
ARCHERRT
60~80 sec on Tesla M2090
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Hissoiny, et. al., Phys. Med. Biol., 56, 5119 (2011)
Bol et. al., Phys. Med. Biol., 57, 1375 (2012)
Jahnke, et. al., Phys. Med. Biol., 57, 1217 (2012)
Su, et. al., Med. Phys. 41, 071709 (2014)
DEPT OF RADIATION ONCOLOGY
Outline
GPU Monte Carlo
Clinical Applications
Conclusions
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7/24/2014
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DEPT OF RADIATION ONCOLOGY
Pencil beam dose calculations using MC
Adaptively sample particles based on optimized MU
Acceleration 4X (in addition to acceleration of MC by GPU)
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Treatment (re)-planning
Dash – Ground truth: 1×106 / beamlet
Solid - 5×103 / beamlet with 9 iteration
TH-E-BRE-8, Li, et. al.
Optimized fluence map with 1×106 / beamlet
and 1×104 / beamlet/iteration
DEPT OF RADIATION ONCOLOGY
A web-based service for treatment plan verification
User upload a plan
gDPM runs on a GPU server
Plan dose is verified and a report is generated
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Pre-treatment
DEPT OF RADIATION ONCOLOGY
In/post-treatment Real-time simulation of photon dose delivery using (simulated)
online machine log
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7/24/2014
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DEPT OF RADIATION ONCOLOGY
• Accurate evaluation of patient-specific dose in CT scans
(a) (b) (c)
x
y z
(a) (b) (c)
(d) (e) (f)
x
y
z
Montanari, et. al., PMB. 59, 1239 (2014)
(a) (b) (c)
(d) (e) (f)
x
y
z
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CBCT dose calculations
DEPT OF RADIATION ONCOLOGY
Estimate scatter signals using MC
2 FDK reconstruction + 1 MC in 30 sec
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CBCT scatter
0 200 400 600-1200
-1000
-800
-600
-400
-200
0
Pixel Position of the profile
Hu
Valu
e
Xu, et. al., submitted to PMB. (2014)
DEPT OF RADIATION ONCOLOGY
Outline
GPU Monte Carlo
Clinical Applications
Conclusions
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7/24/2014
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DEPT OF RADIATION ONCOLOGY
• GPU is a great tool for MC simulations
• Fast, or almost near real-time dose calculation is possible
• Enable new and novel applications
Conclusions
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DEPT OF RADIATION ONCOLOGY
Acknowledgement
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Steve Jiang, Ph.D.
Professor Xun Jia, Ph.D.
Assistant Professor
Zhen Tian, Ph.D.
Instructor Feng Shi, Ph.D.
Postdoctoral fellow
Michael Folkerts
Ph.D. student
Yongbao Li
Ph.D. student Yuan Xu
Ph.D. student
• Research team
• Collaborators
• MGH group
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