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
X
- •Editor biographies
- •Chiara Paganelli
- •Chiara Gianoli
- •Antje Knopf
- •List of contributors
- •Glossary
- •1.1 Basic concepts of particle therapy
- •References
- •2.1 Introduction
- •1.2 Rationale of imaging in a PT workflow
- •1.3 Summary of the book structure
- •2.2 Treatment simulation and plan optimization
- •2.2.1 Organ motion management during image acquisition
- •2.2.2 Organ motion management during plan optimization
- •2.3 Treatment delivery
- •2.3.1 Intra-fraction motion management during treatment delivery
- •2.3.2 Inter-fraction motion management during treatment delivery
- •2.4 Dose reconstruction and accumulation for treatment verification
- •2.5 Conclusions and future perspectives
- •References
- •3.1 Rationale of image registration in radiotherapy
- •3.2 Basic framework of image registration
- •3.2.1 Transformation model
- •3.2.2 Similarity metric
- •3.2.3 Optimization method
- •3.2.4 Interpolation
- •3.3 Image registration for treatment (re-)planning
- •3.3.1 Multi-modal image fusion
- •3.3.2 Atlas-based contouring
- •3.3.3 Contour propagation for re-planning
- •3.3.4 4D treatment planning
- •3.4 Intra-fractional image registration
- •3.4.1 Patient positioning
- •3.4.2 Online plan adaptation
- •3.5 Inter-fractional and post-treatment image registration
- •3.5.1 Dose accumulation
- •3.5.2 Dose monitoring
- •3.5.3 Re-irradiation
- •3.5.4 Follow-up evaluation
- •3.6 Challenges and opportunity
- •3.6.1 Caveats on validation of image registration
- •4.1 Introduction
- •4.2 Principles of x-ray computed tomography
- •4.3 Practical considerations for CT-based stopping-power prediction
- •4.3.1 CT acquisition and reconstruction parameters
- •4.3.2 Artefacts from high-density materials
- •4.3.3 Artefacts from organ motion
- •4.4 Conversion from x-ray attenuation to particle stopping power
- •4.4.2 Consideration of non-tissue materials
- •4.4.3 Uncertainties in HLUT-based range prediction
- •4.5 From single-energy CT to dual-energy CT
- •4.5.1 Technological aspects
- •4.5.2 Methodological aspects
- •4.6 Conclusion and outlook
- •References
- •5.1 Introduction
- •5.2 Image guidance in particle therapy
- •5.4 Approaches to volumetric image guidance
- •5.4.1 CBCT scanners mounted on robotic arms
- •5.4.2 CBCT scanners mounted on the couch
- •5.4.3 CBCT scanners installed in the gantry
- •5.4.4 CBCT scanners installed on the nozzle
- •5.4.5 CT scanners on rail
- •5.5 In room imaging for adaptive particle therapy
- •5.5.1 CBCT correction by virtual CT
- •5.5.2 CBCT correction at the projection level
- •5.5.3 4DCBCT
- •5.6 Outlook
- •References
- •6.1 Introduction
- •6.2 Detector technologies in ion imaging
- •6.2.1 Particle detector physics: interaction mechanisms and observables
- •6.2.2 Detector technologies for ion imaging
- •6.2.3 Detector systems for ion imaging
- •6.3.1 Tomographic ion imaging
- •6.3.2 Radiographic ion imaging
- •6.4 Artificial intelligence in ion imaging
- •7.1 Introduction
- •7.2 MR imaging
- •7.2.1 Imaging of the static anatomy
- •7.2.2 Imaging of the moving anatomy
- •7.3 In-beam MRI-guided proton therapy
- •7.3.1 Beam delivery, MR design and magnetic compatibility
- •7.4 MRI-guided PT workflow
- •7.4.1 Treatment planning
- •7.4.2 Off-line adaptation
- •7.4.3 Online adaptation
- •7.4.4 Follow-up examinations
- •7.5 Conclusion and future perspectives
- •References
- •8.1 Introduction
- •8.2 Neural network architectures, training, and evaluation
- •8.3 CBCT-to-CT conversion
- •8.4 MR-to-CT conversion
- •8.5 Future direction
- •References
- •9.2.1 Conventional motion modelling techniques
- •9.1 Introduction
- •9.2 Image-based motion modelling techniques
- •9.2.2 AI-based motion modelling techniques
- •9.3 Dose variations models
- •9.4 Conclusions and future perspectives
- •10.1 Introduction
- •10.2 PET as range verification technique in particle therapy
- •10.2.1 Physics fundamentals
- •10.3 PG detection as range verification technique in particle therapy
- •10.3.1 Physics fundamentals
- •10.3.3 Prompt-gamma timing
- •10.4 Emerging range verification techniques
- •References
- •11.1 Introduction
- •11.2 Quantitative imaging techniques
- •11.2.1 PET
- •11.2.2 MRI: DWI and DTI
- •11.2.3 MRI: PWI—DSC, DCE and ASL
- •11.2.4 MRI: MRS
- •11.2.5 MRI: BOLD and OE-MRI
- •11.2.6 CT: perfusion CT
- •11.2.7 CT: dual-energy CT
- •11.3 Applications in PT
- •11.3.1 Contouring
- •11.3.2 Biological target volume and dose painting
- •11.4 Challenges and perspectives
- •References
- •12.1 Introduction
- •12.2 Macroscopic modelling
- •12.2.1 Conventional models
- •12.2.2 Radiomics
- •12.2.3 Dosiomics
- •12.2.4 Voxel-based analysis
- •12.3 Towards microscopic modelling
- •12.3.1 Q-imaging-driven TCP/NTCP models
- •12.3.2 Microstructural models
- •12.4 Deep learning modelling
- •12.5 Challenges and future perspectives
- •References
- •13.1 Introduction
- •13.2 Imaging for static/rigid treatment sites
- •13.2.1 Brain
- •13.2.2 CSA
- •13.2.3 Extremities
- •13.3 Treatment sites requiring adaptation or motion management
- •13.3.1 Prostate
- •13.3.2 Abdomen
- •13.3.3 Lung
- •13.3.4 Head and neck
- •13.3.5 Breast
- •13.4 User satisfaction
- •13.5 Research activities and future perspectives
- •References
- •References

Imaging in Particle Therapy
MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients Phys.
Med. Biol.
Thummerer A, Zaffino P, Meijers A, Marmitt G G, Seco J, Steenbakkers R J, Langendijk J A,
Both S, Spadea M F and Knopf A C 2020b Comparison of CBCT based synthetic CT
methods suitable for proton dose calculations in adaptive proton therapy Phys. Med. Biol.
095002
Tinganelli W and Durante M 2020 Carbon ion radiobiology Cancers 12 1–43
Tinganelli W, Durante M, Hirayama R, Krämer M, Maier A, Kraft-Weyrather W, Furusawa Y,
Friedrich T and Scifoni E 2015 Kill-painting of hypoxic tumours in charged particle therapy
Sci. Rep.
Tong N, Gou S, Yang S, Ruan D and Sheng K 2018 Fully automatic multi-organ segmentation
for head and neck cancer radiotherapy using shape representation model constrained fully
convolutional neural networks Med. Phys.
van Timmeren J E, Cester D, Tanadini-Lang S, Alkadhi H and Baessler B 2020 Radiomics in
medical imaging—’how-to’ guide and critical reflection Insights into Imaging
Vogelius I R, Petersen J and Bentzen S M 2020 Harnessing data science to advance radiation
oncology Mol. Oncol.
Volz L, Sheng Y, Durante M and Graeff C 2022 Considerations for upright particle therapy
patient positioning and associated image guidance Front. Oncol.
Wagenaar D, Schuit E, van der Schaaf A, Langendijk J A and Both S 2021 Can the mean linear
energy transfer of organs be directly related to patient toxicities for current head and neck
cancer intensity-modulated proton therapy practice? Radiother. Oncol.
Webb S and Nahum A E 1993 A model for calculating tumour control probability in radiotherapy
including the effects of inhomogeneous distributions of dose and clonogenic cell density Phys.
Med. Biol.
Wei R, Chen J, Liang B, Chen X, Men K and Dai J 2023 Real-time 3D MRI reconstruction from
cine-MRI using unsupervised network in MRI-guided radiotherapy for liver cancer Med.
Phys.
Wu A et al 2020 Dosiomics improves prediction of locoregional recurrence for intensity
modulated radiotherapy treated head and neck cancer cases Oral Oncol.
Wu S, Jiao Y, Zhang Y, Ren X, Li P, Yu Q, Zhang Q, Wang Q and Fu S 2019 Imaging-based
individualized response prediction of carbon ion radiotherapy for prostate cancer patients
Cancer Manag. Res.
Xie Y, Zhao J and Zhang P 2021 A multicompartment model for intratumor tissue-specific
analysis of DCE-MRI using non-negative matrix factorization Med. Phys.
Yang F, Simpson G, Young L, Ford J, Dogan N and Wang L 2020 Impact of contouring
variability on oncological PET radiomics features in the lung Sci. Rep.
Yang S S et al 2023 Dosiomics risk model for predicting radiation induced temporal lobe injury
and guiding individual intensity modulated radiation therapy Int. J. Radiat. Oncol. Biol.
Phys.
Ye Y, Cai Z, Huang B, He Y, Zeng P, Zou G, Deng W, Chen H and Huang B 2020 Fully-
automated segmentation of Nasopharyngeal Carcinoma on dual-sequence MRI using
convolutional neural networks Front. Oncol.
Zeineldin R A, Karar M E, Coburger J, Wirtz C R and Burgert O 2020 DeepSeg: deep neural
network framework for automatic brain tumor segmentation using magnetic resonance
FLAIR images Int. J. Comput. Assist. Radiol. Surg.
65 235036
65
5 1–13
45 4558–67
11 91
14 1514–28
12 930850
165 159–65
38 653–66
50 3584–96
104 104625
11 9121–31
48 2400–11
10 1–10
115 1291–1300
10 166
15 909–20
12-22

Imaging in Particle Therapy
Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X and Liao L 2022 Deep learning with
radiomics for disease diagnosis and treatment: challenges and potential Front. Oncol.
Zhang Y, Lobo-Mueller E M, Karanicolas P, Gallinger S, Haider M A and Khalvati F 2020
CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging BMC
Med. Imaging
Zhang Y, Knopf A, Tanner C and Lomax A J 2014 Online image guided tumour tracking with
scanned proton beams: a comprehensive simulation study Phys. Med. Biol.
Zwanenburg A and Löck S 2018 Why validation of prognostic models matters? Radiother. Oncol.
127 370–3
Zwanenburg A et al 2020 The image biomarker standardization initiative: standardized
quantitative radiomics for high-throughput image-based phenotyping Radiology
20 1–8
59 7793
12 1
295 328–38
12-23

IOP Publishing
Imaging in Particle Therapy
Current practice and future trends
Chiara Paganelli, Chiara Gianoli and Antje Knopf
Chapter 13
Integration of imaging in clinical protocols of
particle therapy
P Trnkova, A Bolsi, A Knopf and A Hoffmann
13.1 Introduction
In this chapter, the contribution of image guidance in particle therapy (PT) is
addressed considering the requirements related to the anatomical tumour location,
the clinical experience and clinical needs of many particle therapy centres; those needs
are currently in the focus of the PT imaging research community. The Particle
Therapy Co-Operative Group (PTCOG) is an organisation connecting the research
and clinical community of proton, light ion and heavy charged particle radiotherapy,
which regularly collects information on all the centres and their clinical and research
activities. As of 2023, there were 29 clinically operational particle therapy (PT) centres
in Europe, 44 in USA, 24 in Japan and 16 in the rest of the Asia. Out of those, 70%
had less than 10 years of experience, 40% less than 5 years. There were large
differences among the centres with regards to number of treatment rooms, vendors,
and beam delivery technology, as well as academic or clinical settings.
The information in this chapter about the integration of medical imaging in
clinical PT protocols is mainly based on several surveys that were conducted
between 2016 and 2022. The European Particle Therapy Network (EPTN) collected
data from 19 European particle therapy centres in 2016–17 on the assessment of
current practice in image-guided particle therapy (IGPT; Bolsi et al 2018). A more
detailed body site-specific survey on current practice was performed between the
years 2019–22. Response data from 20 European PT centres was collected and
analysed for brain, prostate, abdomen, cranio-spinal axis irradiation (CSA) and
extremities. The Patterns of Practice for Adaptive and Real-Time Particle Therapy
(POP ART PT) survey collected answers from 70 worldwide PT centres between
July 2020 and June 2021 on establishing the current status of clinical implementation
in real-time respiratory motion management (RRMM) and adaptive particle
therapy (APT; Trnkova et al 2023, Zhang et al 2023). The survey additionally
doi:10.1088/978-0-7503-5117-1ch13 13-1 ª IOP Publishing Ltd 2024

Imaging in Particle Therapy
explored what the biggest burdens in the clinical implementation of the existing
technologies are. In contrast to EPTN surveys, which were fully focussed on imaging
in every step of the clinical particle therapy workflow, the POP ART PT survey
aimed at RRMM and APT and imaging was only a marginal part of the survey.
Results of all surveys are summarized here to highlight the most important
site-specific aspects of imaging in clinical proton therapy.
Additional sources of information considered in this chapter were the reports from
the yearly 4D workshop (Knopf et al 2010, 2014, 2016,Bertet al 2014, Trnková et al
2018,Czerskaet al 2021) and PTCOG Clinical Subcommittees Consensus Guidelines
for head and neck (Lin et al 2021) and thorax (Chang et al 2017). The 4D workshop
has taken place annually since 2009. During the workshop the status of research and
clinical implementation for motion management in PT is addressed. The development
of high-quality imaging suitable for PT is regularly discussed. The reports from the
workshops were published in peer-reviewed journals. PTCOG Clinical Subcommittees
aim at deriving recommendations for specific treatment sites. So far recommendations
for head and neck and thorax have been published. In table 13.1,anoverviewofthe
source of information per treatment site is provided.
Most of the PT centres in all the performed surveys revealed that they gained their
knowledge on the use of imaging and relevant protocols either from already existing
centres or from photon therapy clinics. As of 2022, there is still a lack of guidelines
for imaging and image guidance in PT. Imaging is currently mainly used for the
following steps of the treatment workflow: diagnosis, treatment planning, patient
positioning, evaluation of the necessity of the plan adaptation and motion
monitoring and follow-up. The POP ART PT survey indicated that most of the
3D imaging modalities are located outside of the PT treatment room (referred to as
near-room) and that there is a lack of in-room and in-beam volumetric imaging. As
of 2021, depending on the treatment site, in-room and/or in-beam imaging on its
own was used only in 18%–32% of APT workflows and in 14%–33% in combination
with near-room imaging (Trnkova et al 2023).
Table 13.1. Overview of the treatment sites, their workflow specifics and source of information.
Treatment site Workflow specifics Source of information
Brain Static EPTN Survey
CSA Static, multi-isocentre EPTN Survey
Extremities Static EPTN Survey
Prostate Adaptive EPTN Survey
POP ART PT Survey
Abdomen Adaptive, motion management EPTN Survey
Lung Adaptive, motion management POP ART PT Survey
PTCOG Thorax Subcommittee
Head and neck Adaptive POP ART PT Survey
PTCOG Head&Neck Subcommittee
Breast Motion management —
13-2

Imaging in Particle Therapy
13.2 Imaging for static/rigid treatment sites
The brain, cranio-spinal axis (CSA) and extremities can be considered as static
treatment sites for which imaging is required at several steps of the PT workflow
(figure 13.1).
In the planning process, the dose calculation is performed based on computed
tomography (CT) images. Different CT modalities are used among different
institutes: most centres use single-energy CT (SECT), with a CT calibration to
proton stopping power procedure in place (Schneider et al 1996), whilst a few other
centres use direct relative proton stopping power computed from dual-energy CT
(DECT; Wohlfahrt and Richter 2020). CT acquisition protocols differ among
institutes, and they are specific for each treatment site (Bolsi et al 2018).
Depending on the specifi c needs, additional CT imaging with contrast and
algorithms for metal artefacts reduction is used in the clinical practice. The benefit
and accuracy of those algorithms has been shown in multiple studies (Wei et al 2006,
Andersson et al 2014). Typically, tumour delineation benefits from multi-modal
imaging, including magnetic resonance imaging (MRI) and positron emission
tomography (PET) combined with CT imaging (PET-CT). Those images are
acquired and registered with the planning CT scan, based on rigid registration.
Only in a few centres, a near-room MRI scanner is available in the PT department
and image acquisition with treatment fixation devices are possible, which results in
registration uncertainties. In case patient positioning is the same for all the imaging
acquisitions, including the planning CT scan, the uncertainties in the registration
process can be minimised.
Figure 13.1. Schematic overview of the steps of PT workflow where imaging is performed. ‘f’ stands for a
treatment fraction.
13-3

Imaging in Particle Therapy
For static target volumes, image guidance for patient positioning verification is
mostly 2D IGPT, based on mixed x-ray projections matched with digitally
reconstructed radiographs (DRRs) from planning CT scans. For some centres the
use of 3D IGPT, either based on in-beam cone-beam CT (CBCT) or on in-room CT,
is part of the clinical routine, with reduced frequencies as compared to daily
imaging. For both 2D and 3D IGPT, the registration of daily and reference images
is focused on accurate bone matching. Volumetric images provide important
information related to positioning accuracy/reproducibility and anatomical changes
and they might be used as a trigger for the adaptation process. As tumours and
anatomical treatment sites are stable over the course of treatment, plan adaptation is
rather ad-hoc and it can be triggered by routine 3D image acquisitions, which are
mostly based on re-evaluation CT. An example of 2D/2D, 2D/3D and 3D/3D image
guidance for patients treated in the head is reported in figure 13.2.
Figure 13.2. Example of different image guidance options for intracranial cases: (a) 2D/2D topogram
comparison; (b) 2D/3D comparison between reference DRR and x-ray; and (c) volumetric comparison
between planning CT and daily CBCT scans.
13-4

Imaging in Particle Therapy
CSA irradiations are included among the rigid treatment sites, but they present special
challenges mainly due to the extent of the volume to be treated (from top of the head to
end of the spinal cord; figure 13.2) and the limited size of the image and treatment fields.
Therefore, daily, multiple images need to be taken along the full length of the spine;
positioning corrections need to be computed from those different images. To reduce the
number of x-ray acquisitions, some centres have introduced optical surface imaging
systems which help in the initial setup of the patients (Liu et al 2021). Those systems
provide the advantage of dose-free images, and they are mainly used for extracranial
treatments, especially in case of positioning systems without indexing (i.e. matrasses).
Intracranial tumour patients are generally immobilised with bite blocks or
personalised thermoplastic masks, with a patient-specific mould-care pillow or
standard neck rest. In those cases, the use of surface imaging is redundant, and
therefore not applied.
13.2.1 Brain
The EPTN survey results on brain treatments include feedback of 6 treating PT centres,
most of them with extensive experience in such treatments. The clinical IGPT workflow
for this body site is based on the specific experience of each centre and on data published
in literature (Amelio et al 2013). The treatment planning CT scan is generally performed
with SECT, as DECT is currently only available and implemented in the clinical
workflow in very few centres. MRI is generally used for tumour delineation purposes,
basedonspecific MRI pulse sequences, such as inversion-recovery gradient echo (IR-
GRE) and T1-MPRAGE that have been included in the standardized Brain Tumour
Imaging Protocol (Ellingson et al 2015). The MR images are rigidly registered with the
planning CT images. In most cases, the MRI acquisitions are not performed in
treatment position, thus increasing the inaccuracy of image registration. Repeated
MR imaging during the treatment course can be used to check treatment response and
anatomical changes. PET acquisitions are also generally used in the treatment
preparation phase; those images are rigidly registered with planning CT ones. Pretreatment image guidance is generally based on 2D x-ray images matched with DRR
(Shafai-Erfani et al 2018, Zechner et al 2022); only a few centres acquire further intreatment images. 3D images (CBCT) are currently limited in very few centres, and they
are acquired on regular intervals (daily or weekly after the first fraction CBCT
acquisition). In most of the centres intra-fractional motion is monitored by the
acquisition of post-treatment 2D images. Image guidance in both the treatment
planning phase and the positioning verification is performed with specific protocols
defined for paediatric patients in 50% of the centres, with the goal of reducing the image
guidance delivered dose. The rest of the centres use the same imaging protocols for
children and adults.
13.2.2 CSA
Eleven centres treating CSA patients provided feedback in the CSA specific EPTN
survey, half of them treating very few patients per year (<10/y) and half of them with
larger experience (>10/y or more); and all the information about CSA is based on
13-5

Imaging in Particle Therapy
Figure 13.3. Typical dose distribution for CSA treatment, which is followed by a local boost. The craniocaudal extension of field, depending on the age and height of the patient, can be from 40 cm up to 100 cm. In
this case the patient is in supine positioned and treated with two posterior-anterior fields at narrow angles.
unpublished data. In comparison to other treatment sites, the prone position is often
used for treatment. As the CSA treatment is usually done in children, paediatric
protocols are commonly used for immobilization (i.e. mould-care to shell the whole
patient), and patients are treated under anaesthesia. SECT is the main treatment
planning imaging modality, with currently very limited clinical implementation of
DECT. MRI images are frequently used for delineation purposes. The recommended MRI pulse sequences for CSA imaging were published by the SIOPE-Brain
Tumour Group (Ajithkumar et al 2018, Wood et al 2019).
The most relevant differenc e t o other treatment sites is the use of several
isocentres and merged fields as the treatment volume is long (Farace et al 2017,
Medek et al 2019; figure 13.3). For thi s reason, a daily setup procedure can take
up to 45 min. All the centres perform verification imaging before every fraction,
and typically images are taken before treating each isocentre. In all centres a 2D
IGRT approach is routinely applied, a nd in some, an additional 3D IGPT
strategy is implemented with repeated CT acquisitions. Each centre has developed specific protocols to deal with the registrations of the different merged
fields, con sidering potential d iscrepancies between the resulting correction
offsets.
Surface imaging is getting more and more integrated in clinical routine for the
initial patient setup: optical images are used to adjust the patient position before
proceeding with x-rays acquisition, thus reducing the number of x-ray acquisitions
and thus the non-therapeutic dose.
13.2.3 Extremities
Eight centres provided feedback on the extremities survey, five of which treat extremity
patients. Most of the centres are using SECT for treatment planning, and only very few
routinely use DECT. For challenging cases, when anatomical changes are detected and
re-planning is required, this will be based on repeated CT acquisitions, with the same
conditions (CT scanner and protocol settings) of the nominal planning CT scan. MR
13-6

Imaging in Particle Therapy
imaging is used mainly for delineation purposes for most of the centres: in half of the
cases the MR acquisition is performed with specific MR sequences for radiation therapy
and with the extremities in treatment position, to minimise uncertainties in the
registration. For most of the centres daily image guidance is based on 2D x-rays vs.
DRR match based on bone anatomy. Surface imaging is rarely used as well as CBCT
imaging. Intra-fraction monitoring using post-treatment control images is rarely
performed. Treatment of extremities, despite being performed in multiple centres,
does not involve a large patient population, which is normally limited to a maximum of
10–20 patients per centre. Specific literature on this topic is scarce. Therefore, most of
the centres base their IGPT workflow on their own experience.
13.3 Treatment sites requiring adaptation or motion management
In moving targets or in targets requiring adaptation, imaging is included in several
steps of the treatment preparation and delivery phase. In case of a site with large
inter-fraction variability (e.g., shrinkage of the tumour in head and neck treatments),
the imaging used in treatment preparation, during the treatment and in follow-up is
the same as for static targets (section 13.1). However, an additional workflow step is
introduced to evaluate the impact of the variation on plan quality (figure 13.4). If the
Figure 13.4. Schematic overview of the steps of adaptive PT workflow where imaging is performed. ‘f’ stands
for a fraction.
13-7

Imaging in Particle Therapy
Figure 13.5. Schematic overview of the steps of PT workflow where imaging is performed. ‘f’ stands for a
fraction.
plan quality is compromised and plan adaptation is required, an offline planning CT
scan is acquired for a new treatment plan (Trnkova et al 2023). The monitoring of
plan quality, i.e. for triggering of plan adaptation is performed either with in-beam
CBCT, in-room CT or near-room repeated CT or MRI. If the treatment sites are
rather stable over the course of treatment, plan adaptation is initialized ad hoc when
needed. For treatment sites where anatomical changes are expected, repeated
imaging is performed on a regular basis as indicated by institutional protocols.
In the case of treatment sites impacted by breathing (i.e., intra-fraction variability), motion mitigation is necessary for safe treatment delivery (figure 13.5; Keall
et al 2006, Trnková et al 2018). Motion mitigation can either be passive, e.g.,
application of safety margins or rescanning, or active by motion suppression or
irradiation only at certain phases of the motion (Zhang et al 2023). For understanding the amplitude and frequency of the motion, 4DCT obtaining images at
several phases of breathing motion, eventually supported by 4DMRI, is commonly
acquired, which serve as basis for 4D dose calculation or for motion monitoring and
integral target volume (ITV) definition (Knopf et al 2022). For the acquisition of 4D
imaging, motion monitoring is needed and defined as tracking the motion states
during or directly before imaging and treatment. External optical or electromagnetic monitoring systems are used to characterize the motion amplitude and
to assort the images into individual phases (Fattori et al 2017). However, the low
temporal resolution, insensitivity to motion variations and off-line acquisition still
limit the optimal consideration of 4DCT motion during treatment planning and
online motion monitoring (Czerska et al 2021).
For the verification of patient positioning, in-beam 2D x-ray or CBCT imaging is
used. In some institutes also in-room CT imaging is integrated in the workflow. In
case of moving targets, the imaging can be performed statically, with a comparison
of the daily imaging to the reference DRR generated from a static CT image. The
static CT image can be either an average CT calculated from all 4DCT images, or an
image corresponding to a certain phase (i.e. mid-ventilation). For monitoring of the
internal motion during the treatment, high-quality 4D imaging like fluoroscopy or
13-8
Соседние файлы в папке Библиотека им академика М.И. Перельмана
