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Radiation Protection and Dosimetry in Medicine Computational Issues: An Overview
Pedro Vaz, Wayne Newhauser and Bernadette Kirk
@ Workshop on Computational and Mathematical Challenges in Particle Therapy Nashville, TN, 19th April 2015
Outline
• The system of Radiological Protection – where are we and where do we go from here ?
• Computational issues & challenges in Medicine
Monte Carlo Treatment Planning
Grand Challenges in High Performance Computing for Medicine Some Examples from Particle Therapy
• Computational issues & challenges in (Radiation) Biology and Biosciences
Nanodosimetry and track structure simulation
• Assessment of the computational state-of-the-art
• Available computing power vs complexity (of the problems to be modelled)
• Conclusions
The Future System of Radiological Protection
“Radiation protection standards rely on current knowledge of the risks from radiation exposure. Any over-, or under-, estimation of these risks could lead either to unnecessary restriction or to a lower level of health protection than intended.”
Sou
rce:
HLE
G r
epo
rt (
20
08
)
Computational Phantoms for medical dosimetry (1)
• Three different formats for computational anatomic phantoms:
stylized (or mathematical)
• Flexible, allowing changes in organ size, body shape, and extremity positioning, but generally deficient with respect to anatomic realism
voxel (or tomographic)
• Three dimensional array of voxels, each with a unique organ identity, elemental composition, and density. Very difficult to alter to represent the body morphometry
hybrid
• Based upon NURBS and/or polygon mesh surfaces. Preserve both the anatomic realism of voxel phantoms and the mathematical flexibility of stylized phantoms
Computational Phantoms for medical dosimetry (2)
• Phantom Morphometric Categories:
– Reference
• reference phantom defined typically as an individual at 50th height/weight percentile in a given human population ICRP 110
– Patient-specific
• Uniquely match the body morphometry and organ anatomy of an individual medical patient
– Patient-Dependent Phantoms
• match patient to phantom using a large library of phantoms covering a broad range of body shapes and sizes
OECD/NEA + UF study Changing the paradigm…?
Effective dose
Effective individual risk
?
Reference individual
Adult male, Adult female, Pediatric male,
Pediatric female
ICRP phantom library
Broad range of body sizes
(height/ weight)
Use of non-reference phantoms Accuracy of dose calculations (1)
From Ted Lazo (NEA) @ Article 31 (2014) meeting
Use of non-reference phantoms Accuracy of dose calculations (2)
From Ted Lazo (NEA) @ Article 31 (2014) meeting
Monte Carlo Treatment Planning (MCTP) Rationale
Section II.A.1 Slopes of dose-effect curves “At this point, a 5% change in dose may result in a 10% to 20% change in tumor control probability at a TCP of 50%. Similarly, a 5% change in dose may result in a 20% to 30% impact on complication rates in normal tissues.”
Section II.A. Required Dose Accuracy
Section II.A.2 The level of dose differences that can be detected clinically “Thus it could be concluded that at least a 7% difference in dose delivered is manifested in the patient’s response to radiation treatment and is detectable clinically by a radiation oncologist.”
Use of Monte Carlo in Radiotherapy Treatment Planning
• Nowadays, most manufacturers deliver Monte Carlo Treatment Planning Systems – however their validation against state-of-the-art Monte Carlo programs still far from achieved
• Monte Carlo programs for Clinical Dose Calculation: – PEREGRINE used in CORVUS inverse TPS (NOMOS, Pittsburgh, PA);
– VMC++ used in Oncentra TPS (Nucletron B.V., Veenendaal, The Netherlands)
– Macro MC used in Eclipse TPS (Varian Medical Systems Inc., Palo Alto, CA)
– DPM used in Pinnacle TPS (Philips Radiation Oncology Systems, Madison, WI);
– PENFAST(PENELOPE) used in ISOgray TPS (DOSIsoft, Cachan, France);
– XVMC used in iPlan (BrainLAB AG, Feldkirchen, Germany), in XiO and Monaco (CMS Inc, StLouis, MO) and PrecisePLAN (Elekta Inc., Norcross, GA) TPS.
Commercial Treatment Planning
algorithms
Monte Carlo Treatment Planning
systems
Real Time Treatment Planning
(using MC ?)
Wayne Newhauser
Grand Challenges in High Performance Computing
for Medicine: Some Examples from Particle Therapy
Some Grand Challenges
Optimize outcomes
Model how physical dose regulates biologic outcomes
Visualize all the radiation exposure (dose and quality) to all the tissues
Model interaction of radiation, drugs,
Personalize medicine (e.g., model inter-patient variations)
19
Newhauser and Durante (Nature Reviews Cancer 2011)
Radiation Exposure to Patients
20
Therapeutic
Leakage (Challenging)
Scatter (Challenging)
(Easy)
21
Calculate Radiation Exposures
Proton absorbed dose
(easy)
Neutron absorbed dose
(challenging)
Newhauser et al (2008; 2009), Taddei et al (2009, 2010); Zhang et al (2014); Giebeler et al (2013); Perez-Andujar et al (2012)
1 day w/ 1 CPU
3 wks w/ 1072 CPUs
From Newhauser et al, PMB (2009) and Miralbell et al., IJROBP (2002). See related studies by Taddei et al PMB (2009), Brodin et al Acta Oncologica (2011), Zhang et al (2014)
Photon IMRT (15 MV, 9 field)
Photon CRT (6 MV, 1 field)
Protons (SOBP, 1 field)
Risk: 55% 31% 4-5% Rel risk: 12 7 1
Compare Treatment Strategies: Predicted Doses and Risks of SMN after Photon vs Proton Therapies
Routine Calculation of All the Dose
to All the Tissues
23
Sagittal equivalent dose planes overlaying a thoracic
CT image of the HL patient showing (a) proton
equivalent dose and (b) combined proton and neutron
equivalent dose. Equivalent dose values are
percentages of the prescribed target equivalent dose,
i.e., 36 Sv. The mediastinal tumor and healthy
thyroid are contoured in black.
Eley, Newhauser, Homann, Howell, Schneider, Durante Bert.
Cancers 2015, 7, 427-438
Easy
Challenging
Moving Organs and Beam
24
Easy Challenging
Challenging
Eley, Newhauser, Luchtenborg, Graeff, Bert. Phys. Med. Biol. 59 (2014) 3431–3452
“4D optimization of scanned ion beam tracking therapy for moving tumors”
From Newhauser and Durante, Nature Rev Ca, 2011
Radiation Absorbed Dose
Risk of SMN Incidence
Risk of SMN Mortality
Visualize Dose & Risk Is “dose” enough? Absorbed dose? Equivalent dose? Effective dose? Integral dose? Ambient dose equivalent? Is “risk” enough? Incidence? Mortality? Absolute? Relative? Timepoint?
Algorithmically Optimize Outcomes
26
Rechner, Eley, Howell, Zhang, Mirkovic, Newhauser, Risk-optimized proton therapy to minimize radiogenic second cancers (in review)
Axial slice showing risk-optimized proton therapy (ROPT) treatment plans with and without DVH constraints applied during the planning process
Algorithmically Optimize Outcomes
27
Predicted excess relative risk (ERR) versus beam angle (θ) for second cancer in the bladder and rectum using the linear-non-threshold risk model
Rechner, Eley, Howell, Zhang, Mirkovic, Newhauser, Risk-optimized proton therapy to minimize radiogenic second cancers (in review)
Technical Computing Challenges
Reproducibility: Osterwiel et al., Science, 325 1622 (2009)
Scalability (software development and parallelization)
Heat generation and removal (operating costs)
Bandwidth associated with input and output of data
Latency as information travels between parts of a supercomputer
29
Summary: Computing Aspects
Now feasible to reconstruct whole body radiation doses and risks of second cancer
Despite rapid progress in hardware and software, still many large gaps in knowledge
Increasingly personalized medicine will require huge increase in computing in radiotherapy
Computational issues & challenges in (Radiation) Biology and Biosciences
Nanodosimetry and track structure simulation
Modelling Radiation Biology
From: Carmen Villagrasa (IRSN), EURADOS Winter school : “Status and Future Perspectives of Computational Micro- and Nanodosimetry”
Issues (1)
De Broglie wavelength for a 10 eV electron: =h
mv= 0.39 nm
DNA transverse dimension: 2-3 nm
Inelastic cross-sections of low energy electrons (and other particles) MUST
Some data for water. How about other “materials” ?
Tracking particles down to the few eV energy range
For each particle fully simulate the ionization pattern
track structure Monte Carlo simulation !
Issues (2)
• Track structure Monte Carlo simulation programs:
Perform the transport of particles simulating each particle´s interaction
Time consuming
Limited to microscopic spatial dimensions
Utilize DNA models of different complexity
Some simulate processes such as DNA damage repair
Insufficient benchmarking and validation?
• Primary target for radiation-induced damage – DNA molecule
– single and clustered damage
From: Fundamentals of micro and nanodosimetry, Hans Rabus, 2011
Nanodosimetry and biological effectiveness
Nanodosimetry Track structure
Particle track-structure analysis
From: Fundamentals of micro and nanodosimetry, Hans Rabus, 2011
Nanodosimetry Cluster size distributions
• Ionization cluster size distributions ()
– Number of ionizations produced by a single-particle track in the DNA segment
• Diffusion and recombination of radiation-induced water radicals
Track structure / nanodosimetry Monte Carlo simulations
+ GEANT4-DNA (http://geant4-dna.org)
Fro
m:
H. N
ikjo
o, R
ad. M
eas
. (2
00
6)
Assessment of the computational state-of-the-art
Shielding design Accuracy of deep penetration simulations
10-14
10-13
10-12
10-11
10-10
10-9
10-8
10 100 1000
Fig. 8 Neutron spectra inside iron for 1 GeV neutrons.
GEANT-4(4m)(SATIF-10)
PHITS(4m,R=3m)(SATIF-8)
FLUKA(4m)(SATIF-10)
ROZ-6.6(4m)(SATIF-8)
MARS(4m)(SATIF-8)
HETC-3STEP(4m,SATIF-6)Neu
tron
s/M
eV
/cm
2 p
er
n/c
m2
Neutron Energy (MeV)
Results presented at SATIF-10
From H. Hirayama (KEK) @ SATIF-12 Meeting
Shape of FLUKA is different from others.
MCNPX larger than others.
10-5
10-4
10-3
10 100 1000
Al, 1GeV proton at 15 degrees
FLUKA 2011MARS 1514 (LAQGSM)PHITSMCNPX Version2.7Geant4 V10.00p01FLUKA 2011 2b5
Neu
tro
ns/M
eV
/sr
Energy (MeV)
From H. Hirayama (KEK) @ SATIF-12 Meeting
10-2
10-1
0 20 40 60 80 100 120 140 160
Al, 1 GeV proton
FLUKA 2011MARS 1514(LAQGSM)PHITSGeant4 V10.00p01 Version2.7FLUKA 2011 2b5
Neu
tro
n flu
en
ce a
bo
ve
20
MeV
(neu
tro
ns/s
r)
Angle (Degrees)
From H. Hirayama (KEK) @ SATIF-12 Meeting
10-2
10-1
100
0 20 40 60 80 100 120 140 160
Al, 10GeV proton
FLUKA 2011MARS 1514(LAQGSM)PHITSGeant4 V10.00p01MCNPX Version2.7FLUKA 2011 2b5
Ne
utr
on
flu
en
ce
ab
ove
20
Me
V(n
eu
tro
ns/s
r)
Angle (Degrees)
Differences between
code become larger at
large angle.
From H. Hirayama (KEK) @ SATIF-12 Meeting
10-1
100
0 20 40 60 80 100 120 140 160
Al, 100GeV proton
FLUKA 2011MARS 1514(LAQGSM)PHITSGeant4 V10.00p01MCNPX Version2.7FLUKA 2011 2b5
Ne
utr
on
flu
en
ce
ab
ove
20
Me
V(n
eu
tro
ns/s
r)
Angle (Degrees)
Differences between
code become larger at
large angle.
MCNPX results are larger and PHITS
results are smaller than others.
From H. Hirayama (KEK) @ SATIF-12 Meeting
Computing Power vs. Monte Carlo simulations
http://www.intel.com/technology/mooreslaw/index.htm
100
0+ line
s of cod
ing
100000+
lines of
coding
500000+
lines of
coding
Co
mp
lexit
y
of
Pro
ble
m
Computers
PC
Clusters of PCs
Highly parallel computers
M
E
M
O
R
Y
Megabyte/Megavoxel 220
Gigabyte/Gigavoxel 230
Terabyte/Teravoxel 240
Petabyte/Petavoxel 250
ICRP
MIRD
Rigid 3-D
Moving 4-D
Computational Challenge for Radiation Therapy and Imaging
From: Bernie Kirk, Source: George Xu
Outlook and Conclusions (1)
• Advances in Medicine and in the Biosciences (at large) impose:
– Specific computational requirements concerning the modelling and simulation of the interaction of radiation with matter
– Challenging approaches to the understanding of the biological effects of ionizing radiation Microdosimetry and Nanodosimetry
• The way forward encompasses:
– Individual dose and risk assessment in radiation therapy
– Tailoring treatments for the individual patient
– Real Time Tretatment Planning Systems
Outlook and Conclusions (2)
• Applications driving computational requirements (Monte Carlo, deterministic and hybrid methods and programs): – Medical uses of ionizing radiation (diagnostic, therapy, nuclear
medicine and interventional procedures) – Emerging and innovative nuclear technology systems
• Future evolution calls for:
– Effective hybrid methods – More and better cross-section data – Efficient tools for:
• Sensitivity/uncertainty analysis, • Variance reduction, • Tallying, • Input and output
– Full 3D and time-dependent capabilities and calculations