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Imaging in Particle Therapy
12.3 Towards microscopic modelling 12-8
12.3.1 Q-imaging-driven TCP/NTCP models 12-8
12.3.2 Microstructural models 12-9
12.4 Deep learning modelling 12-12
12.5 Challenges and future perspectives 12-13
References 12-15
13 Integration of imaging in clinical protocols of particle therapy 13-1
P Trnkova, A Bolsi, A Knopf and A Hoffmann
13.1 Introduction 13-1
13.2 Imaging for static/rigid treatment sites 13-3
13.2.1 Brain 13-5
13.2.2 CSA 13-5
13.2.3 Extremities 13-6
13.3 Treatment sites requiring adaptation or motion management 13-7
13.3.1 Prostate 13-9
13.3.2 Abdomen 13-10
13.3.3 Lung 13-10
13.3.4 Head and neck 13-11
13.3.5 Breast 13-11
13.4 User satisfaction 13-11
13.5 Research activities and future perspectives 13-12
References 13-13
14 Conclusions and future perspectives of imaging in particle
therapy
C Paganelli, C Gianoli and A Knopf
References 14-3
x
14-1
Preface
The physical and radiobiological advantages of particle therapy (PT) require
dedicated imaging technologies and methodologies to achieve accurate treatment
planning and delivery. In this book we aim at providing the basis of imaging in PT as
well as research and clinical trends on the role of imaging in PT. A focus is put on
near-room, in-room and in-beam technologies clinically available and under
development for treatment planning and delivery, as well as for treatment verification, to trigger off-line or online adaptation. At the same time, methodological
solutions based on the described imaging modalities to accurately model range
uncertainties, anatomo-pathological variations and biological properties are also
reported and discussed.
xi
Editor biographies
Chiara Paganelli
Chiara Paganelli, PhD, is Associate Professor at the Department of Electronics,
Information and Bioengineering at Politecnico di Milano, Milano, Italy. She
obtained her PhD in Bioengineering at Politecnico di Milano in 2016. Her main
research is on image-guided radiotherapy and particle therapy with a focus on MRIguidance and personalized radiotherapy.
Chiara Gianoli
Chiara Gianoli, PhD, is a scientist affiliated to the Ludwig-Maximilians-Universität
München since October 2014 and currently in the Habilitation program. Since
November 2017 she has been the principal investigator of the Deutsche
Forschungsgemeinschaft project ‘Hybrid imaging framework in hadron therapy for
adaptive radiation therapy’ at the Department of Experimental Physics—medical
physics in the faculty for physics of the same university. Her interest is focused on
imaging in medical physics, with particular reference to imaging technologies and
methodologies, including approaches relying on the use of artificial intelligence, for
ion beam therapy.
Antje Knopf
Antje Knopf, obtained her PhD degree in Physics in 2009 from the Ruperto Carola
University Heidelberg, Germany, carrying out the related research at the
Massachusetts General Hospital/Harvard Medical School in Boston, USA.
Afterwards, she pursued an international academic career in medical physics with
a focus on adaptive treatment approaches, image guidance, motion management
and particle therapy. Since 2022, she has been a Full Professor for Medical Imaging
and Medical Image Processing at the University of Applied Sciences and Arts
Northwestern Switzerland.
xii
List of contributors
Amstutz, Florian, PhD, Division of Medical Radiation Physics and Department
of Radiation Oncology, Inselspital, Bern University Hospital, and University of
Bern, Switzerland, florian.amstutz@insel.ch
Baroni, Guido, Prof., Dipartimento di Elettronica, Informazione e Bioingegneria,
Politecnico di Milano, Milano, Italy, guido.baroni@polimi.it
Bolsi, Alessandra, MSc, Paul Scherrer Institute, Center for Proton Therapy,
Villigen, Switzerland, alessandra.bolsi@psi.ch
Bortfeldt, Jonathan, PhD, Ludwig-Maximilians-Universität München (LMU
Munich), Germany, jonathan.bortfeldt@lmu.de
Buizza, Giulia, PhD, Dipartimento di Elettronica, Informazione e Bioingegneria,
Politecnico di Milano, Milano, Italy, giuliabuizza.gb@gmail.com
De Simoni, Micol, PhD, Istituto Superiore di Sanità (Italian National Institute of
Health), National Center for Radiation Protection and Computational Physics,
Milano, Italy, micol.desimoni@iss.it
Gianoli, Chiara, PhD, Ludwig-Maximilians-Universität München (LMU
Munich), Germany, chiara.gianoli@physik.uni-muenchen.de
Hoffmann, Aswin, Prof., OncoRay – National Center for Radiation Research in
Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus,
TUD Dresden University of Technology, Helmholtz-Zentrum DresdenRossendorf, Dresden, Germany aswin.hoffmann@uniklinikum-dresden.de
Hua, Chia-Ho, PhD, St. Jude Children’s Research Hospital, Memphis,
Tennessee, USA, chia-ho.hua@stjude.org
Knopf, Antje, Prof., University of Applied Sciences and Arts Northwestern
Switzerland, antje.knopf@fhnw.ch
Kurz, Christopher, PhD, Department of Radiation Oncology, LMU University
Hospital, LMU Munich, Munich, Germany christopher.kurz@med.uni-muenchen.de
Landry, Guillaume, Prof., Department of Radiation Oncology, LMU University
Hospital, LMUMunich, Munich, Germany,guillaume.landry@med.uni-muenchen.de
Maspero, Matteo, PhD, Radiotherapy Department, University Medical Center
Utrecht, Utrecht, The Netherlands, m.maspero@umcutrecht.nl
Meschini, Giorgia, PhD, Dipartimento di Elettronica, Informazione e
Bioingegneria, Politecnico di Milano, Milano, Italy, giorgia.meschini@polimi.it
Molinelli, Silvia, PhD, Centro Nazionale di Adroterapia Oncologia, Pavia, Italy,
silvia.molinelli@cnao.it
Morelli, Letizia, MSc, Dipartimento di Elettronica, Informazione e
Bioingegneria, Politecnico di Milano, Milano, Italy letizia.morelli@polimi.it
xiii
Imaging in Particle Therapy
Nakas, Anestis, MSc, Dipartimento di Elettronica, Informazione e Bioingegneria,
Politecnico di Milano, Milano, Italy, anestis.nakas@polimi.it
Oborn, Bradley, PhD, Centre for Medical Radiation Physics, University of
Wollongong, NSW, Australia, boborn@uow.edu.au
Paganelli, Chiara, Prof., Dipartimento di Elettronica, Informazione e
Bioingegneria, Politecnico di Milano, Milano, Italy, chiara.paganelli@polimi.it
Parker, Geoff JM, Prof., Centre for Medical Image Computing, Department of
Medical Physics & Biomedical Engineering, University College London, London,
United Kingdom, geoff.parker@ucl.ac.uk
Parrella, Giovanni, MSc, Dipartimento di Elettronica, Informazione e
Bioingegneria, Politecnico di Milano, Milano, Italy, giovanni.parrella@polimi.it
Peters, Nils, PhD, Harvard Medical School & Massachusetts General Hospital,
Boston, USA, npeters8@mgh.harvard.edu
Riboldi, Marco, Prof., Ludwig-Maximilians-Universität München (LMU
Munich), Germany, marco.riboldi@physik.uni-muenchen.de
Richter, Christian, Prof., OncoRay – National Center for Radiation Research in
Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus,
TUD Dresden University of Technology, Helmholtz-Zentrum DresdenRossendorf, Dresden, Germany, christian.richter@oncoray.de
Schulte, Reinhard, Prof., Loma Linda University, Loma Linda, California,
rschulte@llu.edu
Smolders, Andreas, MSc, Paul Scherrer Institute, Center for Proton Therapy,
Villigen, Switzerland, andreas.smolders@psi.ch
Spadea, Maria Francesca, Prof., Institute of Biomedical Engineering, Karlsruhe
Institute of Technology (KIT), Karlsruhe, Germany, mf.spadea@kit.edu
Thummerer, Adrian, PhD, LMU University Hospital, LMU Munich, Germany,
adrian.thummerer@med.uni-muenchen.de
Trnkova, Petra, PhD, Department of Radiation Oncology, Medical University of
Vienna, Vienna, Austria petra.trnkova@meduniwien.ac.at
Wohlfahrt, Patrick, PhD, Siemens Healthineers, mpwohlfahrt@gmail.com
Zaffino, Paolo, PhD, Università degli Studi Magna Graecia di Catanzaro,
Catanzaro, Italy p.zaffino@unicz.it
Zampini, Marco Andrea, PhD, MR Solutions Americas LLC, marco.
zampini@mrsolutions.com
Zhang, Ye, PhD, Paul Scherrer Institute, Center for Proton Therapy, Villigen,
Switzerland ye.zhang@psi.ch
xiv
Glossary
18
F-FDG fluorodeoxyglucose
4DCT respiratory-correlated four dimensional CT
4DDC 4D dose calculation
4DMRI respiratory-correlated four dimensional MRI
AAPM american association of physicists in medicine
AD and RD axial and radial diffusivity
ADC apparent diffusion coefficient
AI artificial intelligence
AP anterior–posterior
APT adaptive particle therapy
ART adaptive radiotherapy
ASL-MRI arterial spin labelling MRI
BEV beams eye view
BH breath-hold
BOLD blood-oxygen-level-dependent
bSSFP balanced steady state free precession
BTV biological target volume
CA contrast agent
CBCT cone beam CT
CBF cerebral blood flow
cGAN conditional generative adversarial network
CNN convolutional neural networks
CSA cranio-spinal axis
CT computed tomography
CTN CT number
CTV clinical target volume
DCE-MRI dynamic contrast-enhanced MRI
DECT dual-energy CT
DIR deformable image registration
DL deep learning
DOF degrees of freedom
DPBC dose painting by contours
DPBN dose painting by numbers
DRR digitally reconstructed radiography
DSC Dice similarity coefficient
DSC-MRI dynamic susceptibility contrast MRI
DTI diffusion tensor imaging
DVF displacement/deformable vector field
DVH dose volume histogram
DWI diffusion weighted MRI
EPID electronic portal imaging devices
EPTN European Particle Therapy Network
FA fractional anisotropy
FDK Feldkamp–Davis–Kress
FFE fast field echo
FLASH-RT FLASH radiotherapy (irradiation of tissue at ultra-high dose rates)
FOV field of view
xv
Imaging in Particle Therapy
GAN generative adversarial network
GTV gross tumor volume
HLUT Hounsfield look-up table
HU Hounsfield unit
IR-GRE inversion-recovery gradient echo
IGPT image guided particle therapy
IGRT image guided radiotherapy
IMTP intensity modulated particle therapy
ITV internal target volume
IVIM intra-voxel incoherent motion
J Jacobian
LASSO least absolute shrinkage and selection operator regression
LEM local effect model
LET linear energy transfer
LET
d
dose-averaged LET
linac linear accelerator
LOR line of response
MAE mean absolute error
MAROOSTER
motion-aware reconstruction method using spatial andc temporal
regularization
MC Monte Carlo
MD mean diffusivity
MDA mean distance to agreement
ME mean error
MI mutual information
MKM microdosimetric kinetic model
ML machine learning
ML-EM maximum likelihood expectation maximization
MRI magnetic resonance imaging
MRI-linac MRI integrated with linear accelerator
MRS magnetic resonance spectroscopy
NTCP normal tissue complication probability
OARs organs at risks
OE-MRI oxygen-enhanced MRI
OER oxygen enhancement ratio
PBS pencil beam scanning
PCA principal component analysis
PET positron emission tomography
PG prompt gamma
PGI prompt gamma imaging
PGS prompt gamma spectroscopy
PGT prompt gamma timing
PGTI prompt gamma timing imaging
POP ART PT patterns of practice for adaptive and real-time particle therapy
PSNR peak signal-to-noise ratio
PSPT passive scanning PT
PT particle therapy
PTCOG particle therapy co-operative group
PTV planning target volume
PWI perfusion weighted MRI
xvi
Imaging in Particle Therapy
QIB quantitative imaging biomarker
Q-imaging quantitative imaging
qMRI quantitative MRI
RBE radiobiological effectiveness
r-COX cox proportional hazards model regularized with an elastic net penalty
RECIST response evaluation criteria in solid tumours
RL right–left
ROI region of interest
ROS reactive oxygen species
RQS radiomics quality score
RRMM realtime respiratory motion management
RSI restriction spectrum imaging
RSNA Radiological Society of North America
RT radiation therapy
sCT synthetic CT
SDD source-to-detector
SECT single-energy CT
SI superior-inferior
SID source-to-isocenter
SNR signal to noise ratio
SPGR spoiled gradient recalled acquisition in steady state
SPR stopping-power ratio
SSD sum of squared differences
SSIM structural similarity index measure
TCP tumor control probability
TOF time of flight
TOLD tumor oxygenation level dependent
TPS treatment planning system
TRE target registration error
US ultra sound
v4DCT virtual 4DCT
VB voxel-based
vCT virtual CT
VERDICT vascular extracellular and restricted diffusion for cytometry in tumours
WED water equivalent depth
WEL water equivalent path length
WET water equivalent thcikness
WHO World Health Organization
xvii
IOP Publishing
Imaging in Particle Therapy
Current practice and future trends
Chiara Paganelli, Chiara Gianoli and Antje Knopf
Chapter 1
Introduction
C Paganelli, C Gianoli and A Knopf
1.1 Basic concepts of particle therapy
During the past decade, external beam radiotherapy has been established as best
practice care in approximately 50% of all cancer cases and it has undergone major
technological and methodological developments (Rosenblatt 2017).
External beam radiotherapy makes use of an external source to treat a target
while trying to spare surrounding organs at risk (OARs). Photons (i.e. x-rays),
produced by linear accelerators (linac), are the external source used in conventional
radiotherapy (RT). Charged particles, including protons or heavy ions (typically
carbon ions), produced by more complex machines (cyclotrons or synchrotrons), can
be exploited in particle therapy (PT) (Linz 2011, Loeffler and Durante 2013,
Durante 2017, Grau et al 2020). Out of the approximately two-thirds of patients
with cancer treated with RT, most of them receive RT and less than 1% receive PT
(Durante et al 2017), a proportion that is rapidly increasing thanks to physical and
radiobiological advantages of PT with respect to RT.
The rationale for PT arises from their favorable dose deposition properties,
described by the Bragg peak (figure 1.1). Unlike for x-ray irradiation, for PT the
energy deposited per unit track increases with depth, reaching a sharp and narrow
maximum peak close to the end of the range. This feature is characterized by the
linear energy transfer (LET [keV μm
particle, through its interaction with matter, per unit of the trace length), which
inversely depends on the particle kinetic energy and directly on its effective charge.
At the beam entrance, the relative dose shows an initial plateau, which is associated
with low LET at high particle energies. At lower particle energies, i.e. at larger
penetration depths, two phenomena occur: the LET tends to increase in accordance
with its inverse dependency on energy, while the effective projectile charge rapidly
decreases, as the projectile collects electrons from the traversed matter (Kraft 2000).
The combination of these phenomena generates the sharp Bragg peak, located just
before the end of the particle range, that is its maximum penetration depth.
−1
], i.e. the energy released by a charged
doi:10.1088/978-0-7503-5117-1ch1 1-1 ª IOP Publishing Ltd 2024
Imaging in Particle Therapy
Figure 1.1. Depth dose profiles for conventional and particle beam radiation therapy. Reproduced from Grau
et al (
2020). CC BY 4.0.
The Bragg peak can be precisely adjusted by changing the initial energy of the
particle beam, leading to a better dose conformation on the target volume and
sparing of surrounding OARs than RT. This makes PT optimal for the treatment of
deep-seated tumours or tumours in proximity to OARs. To cover the 3D geometry
of the target, the Bragg peak has to be widened, creating a spread-out Bragg peak
(SOBP). In the early days of PT, an SOBP was generally achieved through passively
scattering a monoenergetic beam through absorbers and collimators. Nowadays,
almost all newly opened particle therapy centers use pencil beam scanning (PBS) and
intensity-modulated PT (IMPT), in which targets are scanned by small pencil beams
in iso-energy slices, and those slices are reached in depth by actively changing the
beam energy, for different particle beam fields (Fokas et al 2009, Linz 2011, Durante
et al 2017).
An additional advantage of PT is the higher radiobiological effectiveness with
respect to conventional RT, which allows for treating rare and radioresistant
tumours. This is accounted for through the quantity relative biological effectiveness
(RBE; Kraft 2000). The notion of RBE is typically used to compare the biological
effect of different radiation species and is defined as the ratio between the x-rays dose
and particle dose producing the same biological effect.
The RBE depends on several factors, such as the radiation type, the dose, the
tissue radiosensitivity (i.e. α/ β) and the LET, and it is estimated by means of
dedicated radiobiological models of the interaction between the particle beam and
the irradiated biological system (Karger and Peschke 2017). The dependence of RBE
on LET implies that the biological effect is dependent on penetration depth into
tissues and in particular, the RBE increases at the end of the particles’ range, where
the ionization density is highest (Kraft 2000). As such, the higher the ionization
density of the radiation, the greater its biological efficacy: in the presence of a high
ionization density, the probability of complex molecular effects (such as complex
1-2