Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5522_Библиотеки_им_академика_М_И_Перельмана.pdf
Imaging in Particle Therapy
(Continued)
Light to charge/
voltage converter Imaging
numerical
Photomultiplier tubes Analytical and
stack of scintillating
— Range telescope as a
tomographic image
counters
reconstruction
(helium ions)
(Crowe et al 1975)
tomographic image
reconstruction
Photomultiplier tubes Analytical
calorimeter (earlier
experiments), range
Crystal scintillating
proportional
chambers
(protons) (Hanson
1979)
telescope as stack of
plastic scintillators
— Radiography
(later experiments)
Multiple layers of
(Pettersen et al
pixelated silicon
pixelated
2017)
detectors (CMOS/
APS)
silicon
detectors
et al 2020)
— Radiography (Alme
pixelated silicon
Multiple layers of
pixelated
(CMOS/APS)
detectors
(ALPIDE) +
silicon
detectors
aluminium
absorbers
(ALPIDE)
Table 6.2. Overview of list-mode prototypes (single tracking or rear tracking).
Tracking detector technology Energy detector technology
Schematic Project/prototype Entrance position Exit position Absorption detectors
chambers
Multi-wire proportional
Laboratory,
California
Figure 6.1(A) Lawrence Berkeley
— Multi-wire
California, Los
Alamos Scientific
University of
experiments),
figure 6.1(F) (later
Figure 6.1(E) (earlier
Laboratory
experiments)
6-11
— Multiple layers of
collaboration
Figure 6.1(F) Bergen pCT
— Multiple layers of
Imaging in Particle Therapy
Light to charge/
voltage converter Imaging
(Lo Presti et al 2016)
Hardware prototype
photomultiplier
Scintillating fibres Silicon
fibres
array
(Amaldi et al 2011)
Hardware prototype
photomultipliers
Silicon
stack of plastic
Range telescope as
multipliers
scintillator tiles
(GEMs)
Table 6.2. (Continued )
Tracking detector technology Energy detector technology
Schematic Project/prototype Entrance position Exit position Absorption detectors
— Scintillating
(Cagliari and
Figure 6.1(F) INFN and INAF
— Gas electron
Catania)
Figure 6.1(F) CERN and Tera
foundation
6-12
Science 1968)
reconstruction (broad beam) (Cormack and
First tomography, analytical image
Imaging in Particle Therapy
Koehler Phys. Med. Biol. 1976)
based on numerical decomposition of the
integrated signal of each pencil beams
Numerical tomographic image reconstruction
(Magallanes et al 2019)
based on passive and active energy variations
of the pencil beams (Telsemeyer et al 2012)
based on passive energy variations of broad
beam (Testa et al 2013)
Light to charge/
voltage converter Imaging
Energy detector technology
Absorption
detectors
tubes
Photomultiplier
Ionization
scintillating
counter
Range telescope as
chambers
a stack of plastic
absorption tiles
Heidelberg Ion Therapy Center,
GSI Helmholtz Center for Heavy
Ion Research
— Analytical tomographic image reconstruction
Pixelated
— Numerical tomographic image reconstruction
(commercial)
silicon detector
Pixelated
Heidelberg Ion Therapy Center,
German Cancer Research Center
— Radiography (Würl et al 2022)
(commercial)
silicon detector
Pixelated
Harvard University
Medical School
— Radiography (Schnürle et al 2023)
(commercial)
silicon detector
detectors
Pixelated silicon
München
München
Optical cameras Radiography (Darne et al 2022)
(CMOS)
Monolithic plastic
scintillator
Anderson Cancer CenterF
Table 6.3. Overview of integration-mode prototypes.
Schematic Project/prototype
Figure 6.1(G) Harvard University Photographic film — First radiography (broad beam) (Koehler
Figure 6.1(G) Harvard University, Tufts University Crystal
Figure 6.1(H) Heidelberg University Hospital,
6-13
Figure 6.1(I) Heidelberg University Hospital,
Figure 6.1(I) Massachusetts General Hospital,
Figure 6.1(I) Ludwig-Maximilians-Universität
Figure 6.1(I) Ludwig-Maximilians-Universität
Figure 6.1(G) The University of Texas MD
Imaging in Particle Therapy
ProtonVDA LLC3has developed the only non-academic scanner. It is a compact
device, using two tracking modules with four directly adjacent layers of 1 mm
scintillating fibers each, two oriented vertically and two horizontally, to determine
proton position upstream and downstream of the imaged object. Always two layers
are displaced by half pitch; the SciFis are read out by SiPMs. Residual energy is
determined in a 40×40 cm
2
plastic scintillator block with sensitive thickness of only
10 cm, coupled to 16 PMTs. Due to aggressive SciFi grouping and the reduced
calorimeter thickness, the device relies on imaging radiation delivered by a pencil
beam treatment plan, featuring several energy layers (DeJongh et al 2021). A direct
comparison of Phase-II and ProtonVDA scanner can be found in Dedes et al (2022).
The PRaVDA collaboration
4
has developed a high rate-capable, all-silicon
prototype system with upstream and downstream tracking stations featuring six
single-layer silicon strip detectors each. The sampling range detector uses the same
silicon strip detectors, interleaved with 2 mm PMMA plates and has a limited energy
range of 30–80 MeV (Esposito et al 2018). The efforts are continued by the OPTIma
project
5
, aiming for a tracker with a total of 12 single-sided silicon strip detectors,
based on the ATLAS Inner Tracker (ITk) sensors (ATLAS Collaboration 2017),
followed by a calorimeter of segmented scintillators (Winter et al 2023).
The Istituto Nazionale di Fisica Nucleare (INFN) scanner features a four-layer
tracker. Each 20×5 cm
2
layer consists of eight 5×5 cm2single-sided silicon strip
modules, glued back-to-back to provide 2D position information. Residual energy is
measured in a 10 cm deep calorimeter, consisting of 2×7 YAG:Ce scintillators of
3×3 cm
2
cross section, interfaced by SiPMs. Due to the relatively long signal and
shaping time of the calorimeter, the readout rate is below 100 kHz (Scaringella et al
2023).
A collaboration of Northern Illinois University (USA), Fermi National
Accelerator Laboratory (USA) and University of Delhi (India) developed a 2
MHz rate capable scanner, foreseen to be gantry-mountable. The tracker consists of
0.5 mm diameter SciFis. Four layers are arranged into a tracking module with 2D
position information, and two tracking modules upstream and downstream of the
object provide position and direction information. 96 layers of 3.2 mm thick plastic
scintillator, as the trackers interfaced by SiPMs, provide the residual range
information (Naimuddin et al 2016). The collaboration funding ended before
remaining issues with defective SiPMs and the self-triggered DAQ system could
be resolved (Johnson 2017).
The iMPACT project
6
aims at developing a scanner, capable of handling 10
particles per second. The tracker should consist of monolithic active pixel sensors.
For prototyping the ALPIDE sensor, originally developed for the inner tracker of
ALICE, CERN, has been selected. The segmented calorimeter is envisaged to
employ rectangular fingers of plastic scintillator, individually coupled to SiPMs
2
9
3
https://protonvda.com/
4
https://www.liverpool.ac.uk/particle-physics/experiments/pravda/
5
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R023220/1
6
https://cordis.europa.eu/project/id/6490311
6-14
Imaging in Particle Therapy
(Baruffaldi et al 2018). In a concept study, only able to image small animal sizes,
Timepix semiconductor detectors have been used as readout structure for gaseous
Time Projection Chambers. The calorimeter is a BaF
crystal, coupled to a PMT
2
(Biegun et al 2015).
6.2.3.2 Rear tracking detector systems
Several prototype scanners with only a downstream tracking and range station have
been developed. The main advantage is the reduced complexity, thus potentially
facilitating clinical integration, at the cost of reduced image quality.
The TERA foundation developed in the AQUA project an imaging device with
an imaging device with a 30×30 cm
2
active area and 1 MHz rate capable DAQ
system. Position and direction of transmitted particles are determined in two tripleGEM detectors with 2D-readout. The residual range is measured in 48 layers of
3.2 mm thick plastic scintillators, each coupled via a WLS fiber to a single SiPM
(Bucciantonio et al 2013).
An all-silicon imaging device has been proposed by the Bergen pCT collabo-
8
ration
. The two downstream tracking layers and the 41 layers in the sampling range
detector are formed by 108 ALPIDE sensors each, developed for the inner tracker of
the ALICE experiment, CERN. The only difference between tracking and range
detector layers is the sensor backing material: 0.2 mm thick carbon-epoxy sandwich
structures and 3.5 mm aluminum, respectively (Alme et al 2020).
On a much smaller scale, suited only for small animal imaging, a system based on
Timepix3 sensors has been developed. The position of transmitted particles is
measured in several pixelized sensors, and the residual energy is determined from
the measured particle energy loss inside each pixel (Würl et al 2020).
Equally suitable for small animal imaging, both with respect to active area and
possible particle energy, is a full-SciFi imaging system. Four layers of 0.5×0.5 mm
SciFis, two oriented in the same direction, measure the position of transmitted
particles. Smart grouping reduces the necessary number of SiPMs for fiber readout.
The range detector consists of 60 layers of the same SciFis, coupled layer-wise with
two WLS fibers to a SiPM (Lo Presti et al 2016).
7
,
2
6.2.3.3 Particle integrating detector systems
Systems with further reduced complexity are built to register the mean residual range
or energy of an ensemble of particles referred to as integration-mode detector
configuration. While these systems can be cost effective and relatively straightforward in analysis, the imaging dose is considerably higher than in single particle
tracking systems.
An ion imaging system, completely omitting spatially resolving detectors has been
realized based on a 30×30 cm
2
sampling multi-layer ion chamber. The spatial
information is instead provided by the clinical beam delivery and monitoring system.
61 3 mm thick PMMA absorbers, alternating with 6 mm wide planar, air- fi lled ICs,
7
https://project-aqua.web.cern.ch/home.html
8
https://www.uib.no/en/ift/1423566/medical-physics-bergen-pct-project
6-15
Imaging in Particle Therapy
determine the average range of typically 100 or more particles per spot. While the
system has the major advantage to use clinically available beam parameters, the
resolution, especially at interfaces between different materials, is only moderate
(Rinaldi et al 2014). More recent results have been reported from a similar system,
using a commercial multi-layer-IC (Krah et al 2018).
A spatially resolving particle integrating system has been realized by combining
liquid organic scintillator, contained in an acrylic cube (20 cm side length), with a
cooled charge-coupled-device (CCD) camera. The camera registers the scintillation
light production in beam direction via a 45° mirror. The amount of scintillation light
in each pixel can be calibrated to average energy loss (Darne et al 2019). The concept
can be extended to a plastic scintillator system with multiple camera view directions
(Darne et al 2022).
The above-mentioned integrating systems operate with monoenergetic scanned
pencil beams. If, alternatively, the particle energy is scanned, integrating particle
imaging systems can be realized with a single layer of pixelized, energy-loss sensitive
detectors. Each pixel measures an increasing energy loss, i.e., signal amplitude, as
the beam energy is decreased. When the particle range is smaller than the WET of
the object upstream of the respective pixel, the measured amplitude drops to zero.
These amplitude-energy relations can be calibrated to WET.
A commercial diode-array detector, consisting of 249 semiconductor diodes at
7 mm pitch, has been used to record the temporal variation of dose per pixel, created
by a quickly spinning moderator wheel. Although limited in spatial resolution, the
system is very fast and in principle able to image moving structures (Testa et al
2013).
A similar concept, using energy modulation by active beam delivery, has been
realized with a commercial CMOS pixel detector. Its size of 11.4 × 6.5 cm 2 is
sufficient for small animal imaging. Due to the small pixel pitch of 49.5 μm, the
achievable spatial resolution is competitive (Schnürle et al 2023).
6.3 Methodological fundamentals and detector configurations for
ion imaging
Although the current clinical workflow in ion beam therapy is based on x-ray
imaging, the native imaging technique for ion beam therapy is ion imaging (Parodi
2014, Johnson 2017). The most promising prototypes for ion imaging are conceived
as ‘list-mode’ detectors (Schneider and Pedroni 1995, Pemler et al 1999, Sadrozinski
et al 2004, Schulte et al 2004, Bashkirov et al 2016a). This detector configuration is
typically based on the synchronization of a residual energy detector with a tracking
system. Individual ions are therefore tracked and fully or partially absorbed. The
energy detector can be designed either as a thick absorber measuring the residual
energy or the energy loss and thus, the range of the ion traversing the object of
interest (section 6.2). The tracking system typically consists of thin trackers
upstream and downstream of the object of interest (ref. Detector technologies in
ion imaging). With pencil beam scanning systems, the upstream tracking can be also
removed. Fast tracking systems can in principle retrieve directly the residual energy
6-16
Imaging in Particle Therapy
by the TOF measurement between different tracking positions, placed downstream
of the object of interest (Ulrich-Pur et al 2022). The residual energy or range is then
calibrated to WET relying on proper detector characterization. List-mode data are
defined by assigning the WET to the trajectory of each ion.
Simplified imaging prototypes composed of only the energy detector without
an additional tracking system are proposed for pencil beam scanning, typically
referred to as ‘integration-mode’ detectors. This detector configuration infers the
mixed residual energy or range components of the pencil beam (due to lateral and
traversal inhomogeneities) through either the total energy loss in multiple
absorption and detection layers (Rinaldi et al 2013, Magallanes et al 2019)or
the time resolved energy loss of multiple initial beam energies in a single thin
absorption and detection layer (Telsemeyer et al 2012,Testaet al 2013, Schnürle
et al 2023). These mixed compone nts for each pencil beam can be resolved by
means of linear decomposition of the laterally segmented spatial signal (total
energy loss in multiple absorpt ion and detection layers) or the temporal signal
(multiple energy losses i n a single thin absorption and detection layer). This way,
information about range variations due to lateral inhomogeneities can be
retrieved (Krah et al 2015, Meyer et al 2017). The retrieval of traversal
inhomogeneities requires instead also the traversal spatial segmentation of the
signal relevant to the pencil beam (Chen et al 2022). With respect to that,
semiconductor detectors working as thin absorption and detecti on layers (and as
downstream tracking systems) can offer fine pixel ation of th is signal.
Relying on the calibration of the resolved components to WET, a WET histogram expressing the relative occurrence of each WET component is therefore
obtained for each pencil beam. Integration-mode data are defined by the WET
histogram assigned to the straight pencil beam direction, as provided by the
synchronization of the absorption detector with the pencil beam scanning system.
The WET histogram enables the computation of a weighted mean WET (WET
components weighted by the relative occurrences and averaged) and a mode WET
(WET component with the most frequent relative occurrence), thus referring to
integration-mode mean and mode, respectively (Gianoli et al 2019). It is worth
noticing that in literature, integration-mode mode is also referred to as integrationmode max to avoid the ‘mode’ word repetition.
List-mode or integration-mode data covering the object of interest at a certain
projection angle are referred to as an ion radiography (iRad). By mounting the
detector on the rotating gantry (or by rotating the object of interest via patient couch
or chair), several iRads can be acquired. Since the WET is modeled as the integral
RSP along an estimated ion trajectory, the iRads correspond to a forwardprojection of the unknown ion computed tomography (iCT). In particular, the
estimate of ion trajectory is based on the tracking for list-mode data and assumed to
coincide with the straight line along the pencil beam direction for integration-mode
data. The iCT can be therefore obtained by means of tomographic image
reconstruction of the iRads acquired at different projection angles.
6-17
Imaging in Particle Therapy
6.3.1 Tomographic ion imaging
The accuracy and spatial resolution of iCT greatly depend on the estimate of ion
trajectory and therefore, on the detector configuration. To estimate the tracked ion
trajectory inside the object of interest for list-mode data, multiple Coulomb
scattering (MCS) models of ion trajectories are adopted. The scattering model for
list-mode detector configuration can be described as a bi-variate Gaussian distribution with increasing standard deviation along the initial ion direction (i.e., the
direction in air prior to scattering in the object of interest) and with decreasing
standard deviation along the final ion direction (i.e., the direction in air after
scattering in the object of interest), according to a ‘scattering spindle‘ (figure 6.1).
The most probable ion trajectory inside the object of interest is typically derived
relying on the Bayes formalism of the maximum likely path (MLP; Schulte et al
2008) or its approximations (Collins-Fekete et al 2015). The scattering spindle is the
statistical description of the uncertainties of the MLP relevant to each ion. Different
from the MLP, the scattering spindle does not provide the most probable ion
trajectory but rather the statistical description of the ion trajectories constrained by
the ion tracking upstream and downstream of the object of interest. The MLP can be
embedded in the forward-projection model of numerical algorithms for tomographic
image reconstruction. This methodology, eventually provided with superiorization
(Penfold and Censor 2015), is the current state-of-the-art for list-mode data and
potentially enables eliminating (or reducing to less than 1%) the intrinsic inaccuracies of the treatment planning x-ray CT (Meyer et al 2019). Computationally faster
analytical algorithms based on modified filtered back-projection along the scattering
curves can be also considered when list-mode data satisfy the required mathematical
hypotheses about continuity (Rit et al 2013). The MLP as the most probable ion
trajectory turns into a straight line along the pencil beam direction for integrationmode detector configuration. This direction corresponds to the pencil beam
direction deduced from the treatment planning system or the beam monitoring
system. The statistical description of the uncertainties of the straight line along the
pencil beam direction is referred to as ‘scattering cone’ (figure 6.1). For this reason,
tomographic image reconstruction for integration-mode data is traditionally limited
to the use of the straight lines in either analytical or numerical algorithms. However,
the straight line describes only the most probable range component (mode WET) of
the pencil beam. The use of scattering models for integration-mode data is reported
in literature (Rescigno et al 2015, Seller Oria et al 2018). Analytical algorithms based
on filtered back-projection along the scattering cones are proposed for the
unresolved range component of the pencil beam (Rescigno et al 2015). The
scattering cone for each range component of the pencil beam is also directly
embedded in the forward-projection model of numerical algorithms, guaranteeing
reduced noise break-up and better convergence of the tomographic image reconstruction (Seller Oria et al 2018).
The scattering models behind the scattering curve are typically given in homogeneous water. Lateral and traversal inhomogeneities of the object of interest makes
the scattering model inconsistent to both list-mode and integration-mode data.
6-18
Imaging in Particle Therapy
The inconsistency of the scattering curves is demonstrated to affect the accuracy of
tomographic image reconstruction of list-mode data, nevertheless remaining superior to integration-mode data (Gianoli et al 2019). To cope with the inconsistency of
the scattering model due to inhomogeneities, different adjustments of the scattering
curve for list-mode data are implemented relying on prior anatomical information.
The elemental composition and mass density of the water are replaced by those of
the biological tissue as given in the stoichiometric calibration (Collins-Fekete et al
2017). Alternatively, maintaining the elemental composition of water but scaling the
mass density according to the RSP along an estimate of ion trajectory, as
implemented in analytical dose calculation algorithms, the scattering model is
extended to water equivalent inhomogeneity (Gianoli et al 2019).
6.3.1.1 Clinical considerations for tomographic ion imaging
Tomographic ion imaging can be obtained when the beam line and the detector are
embedded in a rotating gantry for patients positioned on beds or by rotating the
treatment seat for ocular and cranial patients, thus probing the object of interest
from different angles with a series of ion radiographies for tomographic image
reconstruction. Research towards the development of cost-effective rotational beam
lines are currently ongoing (Meer and Psoroulas 2015). However, in addition to
geometrical limitations, the need to minimize the (extra) imaging dose to the patient
puts constraints on the possibilities to obtain a full iCT image on a daily basis
(Murphy et al 2007, Hansen et al 2014a). Hence, dedicated reconstruction methodologies are currently under investigation with the purpose of optimizing image
quality in presence of geometrical limitations and dosimetric constraints, based on
priors coming from other imaging acquisitions such as the pCT image, under the
assumption of CT-iCT registration (Hansen et al 2014b).
The flexibility of pencil beam scanning offers unique opportunities to reduce
imaging dose, similar to what is performed in x-ray imaging with tube current
intensity modulation (Dickmann et al 2021) by optimizing the theoretical noise of
the region of interest (Rädler et al 2018). A non-uniform sampling of the iRads can
be obtained by varying the beam fluence or intensity per pencil beam, thus
maintaining the image quality in the region of interest and to penalize only less
interesting regions without a loss of relevant information. In particular, an optimal
fluence/intensity modulation pattern, minimizing dose exposure and maximizing
information in regions of interest can be defined based on priors coming from
treatment planning x-ray CT image.
6.3.2 Radiographic ion imaging
Alternatively to the replacement of the treatment planning x-ray CT based on
tomographic image reconstruction of several iRads, the use of a limited number of
iRads is proposed to potentially minimize the intrinsic inaccuracies of the semiempirical calibration of the treatment planning x-ray CT (Schneider et al 2005) due
to tissue-specific and patient-specific RSP variations. The stoichiometric calibration
is optimized relying on the forward-projection of the treatment planning x-ray CT
6-19
Imaging in Particle Therapy
along the estimated ion trajectory, yielding the water equivalent Digitally
Reconstructed Radiography (weDRR). Relying on this forward-projection model,
the optimization algorithm maximizes the matching between the iRad and the
weDRR, expressed as a function of the unknown adjustments of the stoichiometric
calibration. This optimization can be implemented as a linear minimization if prior
segmentation of the treatment planning x-ray CT is applied. Alternatively, an
optimization algorithm embedding forward-projection and segmentation of the
iteratively adjusted treatment planning x-ray CT can be adopted. Despite the
computational advantage of a linear minimization (Collins-Fekete et al 2017,
Krah et al 2019, Zhang et al 2019), the adoption of a non-linear optimization
algorithm enables overcoming the limitation of the prior segmentation and the
forward-projection of the treatment planning x-ray CT (Schneider et al 2005,
Doolan et al 2015, Gianoli et al 2020).
Depending on the detector configuration, the trajectory is either estimated for
each ion (list-mode data; Collins-Fekete et al 2017, Gianoli et al 2020) or assumed to
be a straight line for each pencil beam (integration-mode data; Doolan et al 2015,
Zhang et al 2019, Krah et al 2019, Gianoli et al 2020). Exact registration between the
treatment planning x-ray CT and the iRad is assumed. Therefore, clinical application of the optimization of the treatment planning x-ray CT based on iRads can only
rely on compensation of anatomical changes based on 2D-3D deformable image
registration (DIR) for treatment planning adaptation (Palaniappan et al 2021,
Palaniappan et al 2022). The use of a limited number of iRads is therefore proposed
also to compensate anatomical changes in adaptive proton therapy.
The optimization of the treatment planning x-ray CT based on iRads has been
investigated by excluding non-straight proton trajectories from the optimization in a
pioneering imaging prototype of a list-mode detector in a dog patient (Schneider
et al 2005) or by excluding regions relevant to range mixing in real tissue samples in
an integration-mode detector (Doolan et al 2015). However, in these two works the
ground truth iCT was not available, thus making the optimization of the objective
function hardly interpretable as a minimization of the intrinsic inaccuracies in the
optimized calibration curve. Such optimization of the treatment planning x-ray CT
based on iRads has been also numerically investigated in anthropomorphic
phantoms for proton list-mode data at relatively high initial beam energy
(Collins-Fekete et al 2017) and regularized based on information about the
inaccurate calibration curve (Zhang et al 2019) or the (actually unknown) true
one (Krah et al 2019). In these studies, realistic calibration curves have been adopted
as inaccurate, without controlling the applied inaccuracies. The extensive application of controlled inaccuracies with respect to the ground truth iCT has been
numerically investigated in realistic Monte Carlo simulations of clinical data, for
proton, helium and carbon ion pencil beams (Gianoli et al 2020). List-mode and
integration-mode data have been compared. The use of a scattering model consistent
to the integration-mode data has also been introduced. The robustness against
controlled inaccuracies applied to the realistic calibration curve has been proven in
analytical simulations of an anthropomorphic phantom. However, in clinical data
the optimization of the treatment planning x-ray CT based on iRads has been
6-20