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Imaging in Particle Therapy
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Imaging in Particle Therapy
Current practice and future trends
Chiara Paganelli, Chiara Gianoli and Antje Knopf
Chapter 10
Treatment verification in particle therapy
C Gianoli, M De Simoni and A Knopf
10.1 Introduction
During or immediately after treatment delivery, reactive treatment verification
techniques can be employed to evaluate the agreement of the delivered dose with
respect to the treatment planning scenario. Because of the several weeks of
fractionated treatment, unpredictable inter-fractional and intra-fractional anatomical changes, as well as inaccuracies in patient positioning, can occur, thus leading to
dosimetric inconsistencies with respect to the treatment planning scenario. In case of
relevant disagreements, a decision about possible replanning of the remaining
fractions of the treatment can be taken. Different from proactive treatment
verification techniques based on in-room tomographic imaging, reactive treatment
verification techniques require in general a new treatment planning x-ray CT.
Reactive treatment verification techniques exploit secondary emissions induced by
therapeutic radiation. However, the electromagnetic processes underlying the dose
distribution are fundamentally different from the nuclear mechanisms inherent to
secondary emissions. Due to the space-variant and time-variant relationship between
the distribution of the secondary emissions and the dose distribution, they are typically
referred to as indirect range verification techniques. For this reason, the range
verification paradigm primarily entails the comparison of the measured secondary
emissions with a prediction of their distribution based on Monte Carlo simulations of
the treatment planning scenario. So far, the clinically investigated range verification
techniques are based on positron emitters annihilating in photon pairs (positron emission
tomography, PET), prompt photons produced by the de-excitation of the nuclei (prompt
gammas, PGs) and, recently, charged secondary particles (mainly protons) produced by
nuclear fragmentation. Generally, reactive treatment verification techniques entail the
detection of the secondary radiation emerging from the patient produced by the
interaction of the therapeutic beam (i.e., the projectile) with the tissue (i.e., the target),
as shown in figure 10.1. This definition includes also neutrons, whose feasibility has been
investigated but not yet demonstrated (Ytre-Hauge et al 2019, Toppi et al 2020).
doi:10.1088/978-0-7503-5117-1ch10 10-1 ª IOP Publishing Ltd 2024
Imaging in Particle Therapy
Figure 10.1. Secondary radiation emerging from the patient produced by the interaction of the therapeutic
beam (i.e., the projectile) with the tissue (i.e., the target). A schematic representation of a detector
configuration relevant to each secondary radiation is depicted. In light blue, the PET detector for the
annihilation photon pair from a positron emitter; in orange, a PG detector provided with a collimator system
for prompt gammas; in green, a tracking system for charged secondary particles.
As an alternative to the indirect comparison of the measurement with a
prediction, the dose distribution can be retrieved from the distribution of the
secondary emissions by means of ‘dose reconstruction algorithms’, based on the
forward filtering approach, firstly proposed for PET imaging (Masuda et al 2019),
and then extended to PG imaging (Schumann et al 2016, Pinto et al 2020). These
include the analytical deconvolution (Parodi and Bortfeld 2006, Remmele et al
2011), the evolutionary algorithm (Schumann et al 2016, Hofmann et al 2019) and
the maximum likelihood expectation maximization (ML-EM) algorithm (Masuda
et al 2019, 2020), whose differences are mainly related to the objective function and
its optimization. Approaches based on deep learning have been specifically proposed
for dose reconstruction in PET imaging (Hu et al 2020,Maet al 2020, Rahman et al
2022) and PG imaging (Liu and Huang 2020). However, these approaches have been
preliminarily investigated relying on the ground truth distribution of the secondary
emission as obtained from simulations, thus not accounting for the detection and the
reconstruction of the events. Deep learning has been proposed to close this gap and
thus retrieving the PG distribution based on the measurements (Jiang et al 2023).
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Simulation platforms have been specifically adapted and developed for the
investigation of PET and PG imaging. The GATE (Geant4 Application for
Tomographic Emission) platform, based on the GEANT4 toolkit, is a Monte
Carlo simulation environment that was originally proposed for emission tomography and recently extended to radiation therapy (Jan et al 2011, Borys et al 2022),
provided with the modeling of complex detector and collimation systems. The
MEGAlib (Medium-Energy Gamma-ray Astronomy library) platform, also based
on the GEANT4 toolkit and originally intended for Compton telescopes in
astronomy, has been firstly used in medical imaging for the investigation of the
triple coincidence from β+ decaying isotopes emitting a third prompt photon
(γ-PET imaging; Lang et al 2014). The environment is equipped with capabilities
for advanced pre-processing of the raw data and tomographic image reconstruction
algorithms for both PET and PG imaging (Liprandi et al 2017, Lovatti et al 2020).
The FLUKA (Fluktuierende Kaskade, or fluctuating cascade) platform is a tool for
the simulations of particle transport and interactions with matter for an extended
range of applications, including medical physics, with reference to PET imaging as
range verification techniques (Sommerer et al 2006, Augusto et al 2018).
10.2 PET as range verification technique in particle therapy
10.2.1 Physics fundamentals
Nuclear interactions between the projectile and the target produce radioisotopes that
emit positrons. The reaction channels, involving both projectile and target,
determine the shape of the activity distribution and thus the relationship to the
dose distribution (Espana et al 2011, Parodi 2012). The positron (β
antiparticle of the electron (β
−
), annihilates with a tissue electron, thus converting its
mass into radiation energy. Due to conservation of momentum and spin, positron–
electron annihilation produces two γ-photons, emitted at an energy of 511 keV along
opposite directions.
Due to the composition of the tissue, the positron emitters produced are mainly
carbon and oxygen isotopes. In particular,
11C,10C,15O,13
N and12N whose halflives are about 20 min, 19 s, 2 min, 10 min and 11 ms, respectively, are the most
abundant (table 10.1) in both proton and carbon ion therapy (Bauer et al 2013,
Horst et al 2019). The former is crucial for PET acquisition after the treatment (i.e.,
in-room PET and near-room PET), while
acquisition during the treatment (i.e., in-beam PET). The activity density for
therapeutic ion beams is rather low, about 200 BqGy
about 600 BqGy
−1cm−3
for protons.
10
C and15O are important for PET
−1cm−3
At the detector level, the line along which the two γ photons are emitted is called
the line of response (LOR). The detection of the two coincident γ-photons along the
LOR is referred to as ‘count’. The distribution of the counts can then be obtained
according to tomographic image reconstruction. However, the count distribution
does not coincide with the activity distribution due to the positron range and the
half-life of the radioactive decay. The positron range indicates the distance between
the position of β
+
decay and the annihilation, which depends on the kinetic energy
+
), which is the
for carbon ions and
10-3
Imaging in Particle Therapy
Table 10.1. List of the positron emitting isotopes produced in hadron therapy, their half-life, their most
probable nuclear reaction channels and threshold energy. The most important nuclear reaction channels are
reported in bold. Since the radioactive isotopes (i.e.,
body, the negligible production nuclear reaction channels are reported in italics. The cross sections of the
radioactive capture reaction (p,γ) are typically three orders of magnitude smaller than the main channels.
13C,15
N and18O) have very low abundances in the human
Nuclear reaction
Radioisotopes Half-live
15
O 2.03 min
14
O 70.6 s
11
C 20.38 min12C(p,pn)11C 20.61 Zhu and El Fakhri (2013)
10
C 19.3 s
13
N 9.97 min
12
N11ms12C(p,n)12N 19.6 Ozoemelam et al (2020)
17
F 1.07 min
18
F 109.8 min18O(p,n)17F 2.60 Beebe-Wang et al (2003)
30
P 2.49 min
38
K 7.63 min
8
B 770 ms
channel
16
O(p,pn)15O 16.79 Zhu and El Fakhri (2013)
16
O(p,d)15O 14.3 Ozoemelam et al (2020)
14
N(p,γ)15O 0 Beebe-Wang et al (2003)
15
N(p,n)15O 3.8 Beebe-Wang et al (2003)
16
O(p,t)14O 21.7 Ozoemelam et al (2020)
16
O(p,nd)14O 28.3 Ozoemelam et al (2020)
16
O(p,p2n)14O 30.7 Pönisch et al (2004)
14
N(p,n)14O 6.6 Pönisch et al (2004)
12
C(p,d)11C 17.9 Ozoemelam et al (2020)
14
N(p,2p2n)11C 3.22 Zhu and El Fakhri (2013)
14
N(p,α)11C 3.44 Pönisch et al (2004)
16
O(p,3p3n)11C 59.64 Zhu and El Fakhri (2013)
16
O(p,αpn)11C 27.50 Beebe-Wang et al (2003)
13
C(p,p2n)11C 25.50 Beebe-Wang et al (2003)
15
O(p,αn)11C 14.70 Beebe-Wang et al (2003)
12
C(p,t)10C 25.3 Ozoemelam et al (2020)
12
C(p,nd)10C 32.1 Ozoemelam et al (2020)
12
C(p,p2n)10C 34.5 Pönisch et al (2004)
16
O(p,3p4n)10C 39.1 Pönisch et al (2004)
14
N(p,nα)10C 17.20 Beebe-Wang et al (2003)
16
O(p,2p2n)13N 5.66 Zhu and El Fakhri (2013)
16
O(p,α)13N 5.66 Beebe-Wang et al (2003)
14
N(p,pn)13N 11.44 Zhu and El Fakhri (2013)
12
C(p,γ)13N 0 Beebe-Wang et al (2003)
13
C(p,n)13N 3.20 Beebe-Wang et al (2003)
15
N(p,nd)13N 20.40 Beebe-Wang et al (2003)
15
N(p,t)13N 13.80 Beebe-Wang et al (2003)
16
O(p,γ)17F 0 Beebe-Wang et al (2003)
31
P(p,pn)30P 19.7 Zhu and El Fakhri (2013)
40
Ca(p,2p2n)38K 21.2 Zhu and El Fakhri (2013)
12
C(p,nα)8B 28.3 Ozoemelam et al (2020)
12
C(p,dt)8B 47.4 Ozoemelam et al (2020)
Threshold energy
(MeV) Reference
10-4
Imaging in Particle Therapy
spectrum of the positron, up to the maximum energy of the ‘end point’, typical of
each radioisotope. Since the annihilation cross section is greater at the lower kinetic
energies of positrons, the positron range can be up to several millimeters. The halflife indicates the time interval between the production of the positron emitter and the
+
β
decay, which is typically modeled by a Poisson distribution.
Treatment verification is based on the comparison of the PET image (i.e., the
measured PET) with a Monte Carlo prediction of the PET distribution based on the
treatment planning scenario (i.e., the expected PET), in terms of anatomy of
the patient and geometry and dosimetry of the irradiation. The prediction requires
the mass density and the stoichiometric composition of tissue, along with the timing
parameters of irradiation and imaging (i.e., irradiation time, delay between
irradiation and imaging, imaging time) as well as the washout estimation. In high
perfused tissue, the biological washout of the β
+
emitters degrade the activation level
and changes the shape of the activity distribution. The correction of biological
washout is based on models designed and characterized on animal studies. The tissue
is determined based on thresholds applied to the Hounsfield Units of the treatment
planning CT image, thus identifying hard bone, soft bone, fat, muscle and brain.
The expected PET is decomposed into three components undergoing fast, medium
and slow biological decays. Tissue-specific fractions and biological half-lives are
assigned to each of the three components. The β
biological washout is then calculated based on the timing parameters of imaging.
The Monte Carlo prediction of the β
+
+
emitter distribution accounting for
emitters can be complemented by the γ
annihilation, thus explicitly accounting for the positron range and the half-life of the
radioactive decay. This way, the effect of tomographic image reconstruction on the
count distribution can be predicted.
10.2.2 Quantitative range verification
The quantitative assessment in PET-based treatment verification entails a comparison between the measured PET and the expected PET to identify possible
inconsistencies. The expected PET is typically calculated by means of Monte
Carlo simulation, but also analytical attempts are reported in literature (Attanasi
et al 2011). The quantification has been typically based on the distal fall-off of PET
image profiles along the beam direction (Parodi et al 2007a, 2007b, Knopf et al 2008,
2011), which correlates to the particle range. The ranges of both the measured PET
and the expected PET are extracted and compared. Different range extraction
methods have been proposed, based on different percentages with respect to the
activity of individual profiles (80% or 90% of the peaks (Parodi et al 2007a)) and
global surfaces (2%–8% of the peak (Moglioni et al 2022)) or based on the
maximization of the cross-correlation of the normalized profiles (Knopf et al
2008). However, the extremely low counting statistics in the measured PET is one
of the main open challenges.
The measured PET is obtained from tomographic image reconstruction of a few
sinogram counts, where the activity emerges from a noisy background in a restricted
area in the field of view. The quality of the reconstruction is therefore strongly
10-5
Imaging in Particle Therapy
limited by the noise break-up effect. A possibility to reduce noise is to stop the
iterative reconstruction at the first iterations. However, the reconstructed image
results far from convergence, and therefore is potentially biased. The bias can be
partially reduced by introducing the model of the spatial resolution and the time of
flight information within the tomographic image reconstruction (Kurz et al 2015)as
well as by using basis functions that are extended to regions rather than voxels
(Gianoli et al 2014). In order to handle the low counting statistics in quantification,
indices of agreement between the measured PET and the expected PET relying on
the entire activity distribution have been proposed, such as based on the statistical
distributions (Pearson’s correlation coefficient (Kuess et al 2012) or voxel-based
morphometry (Kraan et al 2022)). The expected PET has been also used not only as
a comparison for the measured PET, but also as ‘a guide’ for the tomographic image
reconstruction of the measured PET based on regional basis functions for 3D (threedimensional) PET-based treatment verification (Gianoli et al 2014). Despite the
increased robustness to noise and enhanced sensitivity to inconsistencies, the index
of agreement provides a relative quantification of such inconsistencies, which is of
difficult interpretability since it is not supported by a measure of the displacements
between the measured PET and the expected PET. For this reason, the absolute
quantification of the range is typically preferred. Recently, a 4D (four-dimensional)
ML-EM algorithm, originally presented for cardiac gated PET imaging, has been
proposed (Gianoli et al 2017). By interpreting the measured PET and the expected
PET as two different motion phases of a 4D dataset, the algorithm estimates a
measured PET of enhanced image quality, similar to count statistics optimization in
motion compensation strategies (Gianoli et al 2016), and the deformation field
mapping the expected PET onto the measured PET as an absolute measure of the
occurred displacements. However, the inaccuracies of the expected PET, especially
those due to the modeling of biological washout (i.e., tissue classification in Monte
Carlo simulations), translate into biases of the signal distribution, which can be
wrongly interpreted as inconsistencies of measured PET. To cope with the
inaccuracy of the expected PET, a reference measured PET taken at the beginning
of the treatment course can be adopted to assess the reproducibility, instead of the
absolute accuracy, of the treatment delivery (Nishio et al 2008, Moglioni et al 2022).
PET-based treatment verification has been demonstrated as a millimetric range
verification technique in low perfusion bony structures of intracranial and cervical
spine tumor patients. Patient positioning inaccuracies and anatomical changes have
been detected and the treatment has been accordingly adapted for the subsequent
treatment fractions. Limitations of this technique have been mainly associated to the
extremely low count statistics and high perfusion of tissue, which causes PET
activity washout, especially in brain and extra-cranial sites (i.e., abdominopelvic
tumours; Parodi et al 2007b, Knopf et al 2011). In the thoraco-abdominal area,
breathing motion represents an additional source of treatment uncertainty. With
respect to that, treatment verification of moving targets based on time-resolved PET
image acquisition can be accomplished by relying on the whole count statistics
image quality, as obtained from the application of 4D PET motion compensation
strategies (Knopf et al 2014
, Gianoli et al 2016, Kurz et al 2016).
10-6
Imaging in Particle Therapy
10.2.3 Imaging system configurations
The idea of exploiting PET imaging to verify the particle therapy treatment dates
back to 1969, when the proposal of radioactive beam visualization has been put
forward at the Lawrence Berkeley Laboratory (Llacer 1988), relying on
19
Ne beams.
Radioactive beam visualization has been not yet clinically applied but experimental
investigations have been conducted at the Heavy Ion Medical Accelerator in Chiba
(HIMAC) in Japan (Mohammadi et al 2019, 2022) and at the Gesellschaft für
Schwerioneneforschung (GSI) in Darmstadt (Boscolo et al 2021). Commonly, PET
image acquisition for treatment verification makes use of commercial or dedicated
PET scanners placed inside the treatment room or in adjacent PET imaging rooms
(figure 10.2; Shakirin et al 2011). Accordingly, three different PET-based treatment
verification techniques are defined: near-room PET, in-room PET and in-beam PET.
Extensive clinical experiences have been reported in recent years, starting with the
GSI with an in-beam PET prototype, followed by the Massachusetts General
Hospital (MGH) in Boston with near-room and in-room PET systems, and most
recently, the University Hospital of Heidelberg in collaboration with the Heidelberg
Ion beam Therapy centre (HIT) in Germany based on near-room PET systems.
10.2.3.1 Near-room PET
To acquire near-room PET images for treatment verification, patients are typically
moved to a dedicated imaging room after the treatment is delivered, where a
commercial PET/CT scanner is installed. The combined PET/CT scanner allows for
co-registration of the images, as the repositioning of the patient for the near-room
PET acquisition can be a source of misalignments with respect to the treatment
planning CT image. The time delay between the end of the treatment and the
beginning of the PET acquisition strongly influences the quality of the PET image
due to the effects of biological washout and the decrease of the count statistics. In
particular, the time required to collect meaningful statistics for image acquisition
can be up to ∼30 min, exacerbating the effects of biological washout and
substantially prolonging the image acquisition time, and thus patient discomfort.
Figure 10.2. PET-based treatment verification techniques: near-room PET (left), in-room PET (center), in-
beam PET (right).
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