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_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
Artificial Intelligence in Adaptive Radiation Therapy
[91] Goddard K, Roudsari A and Wyatt J C 2012 Automation bias: a systematic review of
frequency, effect mediators, and mitigators J. Am. Med. Inform. Assoc.
[92] Lavrova E, Garrett M D, Wang Y F, Chin C, Elliston C, Savacool M, Price M, Kachnic L A
and Horowitz D P 2023 Adaptive radiation therapy: a review of CT-based techniques Radiol.
Imaging. Cancer.
[93] Qiu Z, Olberg S, den Hertog D, Ajdari A, Bortfeld T and Pursley J 2023 Online adaptive
planning methods for intensity-modulated radiotherapy Phys. Med. Biol.
[94] Pokharel S, Pacheco A and Tanner S 2022 Assessment of efficacy in automated plan
generation for Varian Ethos intelligent optimization engine J. Appl. Clin. Med. Phys.
e13539
[95] Moazzezi M, Rose B, Kisling K, Moore K L and Ray X 2021 Prospects for daily online
adaptive radiotherapy via ethos for prostate cancer patients without nodal involvement
using unedited CBCT auto-segmentation J. Appl. Clin. Med. Phys.
[96] Nikolov S et al 2021 O Clinically applicable segmentation of head and neck anatomy for
radiotherapy: deep learning algorithm development and validation study J. Med. Internet
Res.
23 e26151
[97] Cardenas C E, Yang J, Anderson B M, Court L E and Brock K B 2019 Advances in auto-
segmentation Semin. Radiat. Oncol.
[98] Kaderka R et al 2022 Toward automatic beam angle selection for pencil-beam scanning
proton liver treatments: a deep learning-based approach Med. Phys.
[99] Sadeghnejad Barkousaraie A, Ogunmolu O, Jiang S and Nguyen D 2020 A fast deep
learning approach for beam orientation optimization for prostate cancer treated with
intensity-modulated radiation therapy Med. Phys.
[100] Ma L, Chen M, Gu X and Lu W 2020 Deep learning-based inverse mapping for fluence
map prediction Phys. Med. Biol.
[101] Wang W, Sheng Y, Palta M, Czito B, Willett C, Hito M, Yin F F, Wu Q, Ge Y and Wu Q J
2021 Deep learning-based fluence map prediction for pancreas stereotactic body radiation
therapy with simultaneous integrated boost Adv. Radiat. Oncol.
[102] Shen C, Chen L and Jia X 2021 A hierarchical deep reinforcement learning framework for
intelligent automatic treatment planning of prostate cancer intensity modulated radiation
therapy Phys. Med. Biol.
[103] Shen C, Chen L, Gonzalez Y and Jia X 2021 Improving efficiency of training a virtual
treatment planner network via knowledge-guided deep reinforcement learning for intelli-
gent automatic treatment planning of radiotherapy Med. Phys.
[104] Sprouts D, Gao Y, Wang C, Jia X, Shen C and Chi Y 2022 The development of a deep
reinforcement learning network for dose–volume-constrained treatment planning in pros-
tate cancer intensity modulated radiotherapy Biomed. Phys. Eng. Express
[105] Sonke J J, Aznar M and Rasch C 2019 Adaptive radiotherapy for anatomical changes
Semin. Radiat. Oncol.
[106] Lee D, Hu Y, Kuo L, Alam S, Yorke E, Li A, Rimner A and Zhang P 2022 Deep learning
driven predictive treatment planning for adaptive radiotherapy of lung cancer Radiother.
Oncol.
169 57–63
[107] Pastor-Serrano O and Perkó Z 2022 Millisecond speed deep learning based proton dose
calculation with Monte Carlo accuracy Phys. Med. Biol.
5 e230011
29 185–97
47 880–97
65 235035
66 134002
48 1909–20
29 245–57
67 105006
19 121–7
68 10TR01
23
22 82–93
49 4293–304
6 100672
8 045008
10-23
Artificial Intelligence in Adaptive Radiation Therapy
[108] Vazquez I, Gronberg M P, Zhang X, Court L E, Zhu X R, Frank S J and Yang M 2023 A
deep learning-based approach for statistical robustness evaluation in proton therapy
treatment planning: a feasibility study Phys. Med. Biol.
[109] Wen L, Xiao J, Tan S, Wu X, Zhou J, Peng X and Wang Y 2023 A transformer-embedded
multi-task model for dose distribution prediction Int. J. Neural Syst.
[110] Jiao Z, Peng X, Wang Y, Xiao J, Nie D, Wu X, Wang X, Zhou J and Shen D 2023
TransDose: transformer-based radiotherapy dose prediction from CT images guided by
super-pixel-level GCN classification Med. Image Anal.
68 095014
33 2350043
89 102902
10-24
IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 11
Artificial intelligence-based intrafraction motion
monitoring for precise adaptive radiation
therapy delivery
Lianli Liu and James M Balter
Patient motion during treatment delivery results in discrepancies between the actual
dose delivered and the planned dose distribution and is a significant source of
radiation treatment uncertainty. Real-time adaptation of treatment delivery parameters based on changing patient anatomy has the potential to reduce dose delivery
uncertainty and improve treatment precision. This chapter will review the current
status of motion monitoring for real-time adaptive radiation therapy and discuss the
challenges and the use of artificial intelligence (AI) in supporting real-time adaptive
treatment workflow.
11.1 Introduction
Adaptive radiation therapy (ART) is based on the premise that modifying treatments to changes in patient configuration and/or biology will yield improved tumor
control and/or organ-at-risk (OAR) sparing. While there are now a number of
systems and workflows in place that enable adaptation as frequently as daily (or
even multiple times in a fraction, although this is impractical in most systems at the
time of writing due to the complexity of the adaptation workflows currently in place)
to such changes, intrafraction patient motion can significantly alter the shape and
position of targets, as well as surrounding OARs, thus potentially diminishing the
benefit of adaptation. Various physiologic sources contribute to intrafraction
motion, with breathing being the most significant for thoracic and abdominal
targets, where motions up to 50 mm have been reported [1, 2]. Cardiac motion also
contributes to target position uncertainty and has been observed in lung tumors,
mediastinal lymph nodes, and liver tumors [3–5]. Peristalsis-induced motions, of a
similar magnitude to respiratory motion, have been reported for abdominal cases [6,
7]. Slow internal configuration changes can become significant with elongated
doi:10.1088/978-0-7503-6119-4ch11 11-1 ª IOP Publishing Ltd 2025. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.
Artificial Intelligence in Adaptive Radiation Therapy
treatment times, where position drifts of over 5 mm have been observed within a 20
min time window [8]. Changes in organ filling status, such as the bladder and
rectum, also lead to intrafraction motion for both the organs themselves as well as
and nearby targeted tissue and OARs, with motion greater than 30 mm being
reported [9–11].
The changes of configuration and position of targets and OARs due to intrafraction motion can result in discrepancies between the actual dose delivered and
that planned based on static patient anatomy. Real-time implementation of adaptive
radiotherapy (‘real-time’ ART) aims to adjust treatment delivery parameters based
on changing patient anatomy and has the potential to reduce dose delivery
uncertainty and improve treatment precision. With the development of both imaging
and radiation delivery techniques, the potential for ‘real-time’ monitoring of, and
reaction to, intrafraction motion is emerging. In this chapter, we will first review the
current status and challenges of real-time ART for maintaining precise treatment
delivery, then describe both practical use and research of AI in supporting real-time
ART workflows. Finally, we will discuss potential future areas of development to
further support ‘real-time’ adaptation during treatment delivery.
11.2 Real-time ART during treatment delivery: current status and
challenges
To adapt to changes, patient anatomical information needs to be sampled frequently
during treatment delivery. Motion information is then estimated based on the
sampled data and adjustments to treatment delivery parameters are made accordingly. Figure 11.1 outlines the major steps of real-time ART during treatment
delivery. Successful implementation of real-time ART therefore requires several
technical components including imaging tools that sample patient data at a temporal
resolution high enough to capture the motion-induced anatomical changes, data
analysis tools that extract motion information from the acquired patient images, and
Figure 11.1. Major steps of real-time ART.
11-2
Artificial Intelligence in Adaptive Radiation Therapy
treatment delivery tools that adjust treatment parameters quickly based on the
motion information.
11.2.1 Imaging-based motion monitoring
Various imaging techniques have been developed to sample patient motion
information during treatment delivery. These techniques vary in both imaging
frequency and imaging content and are therefore designed to monitor different types
of motion. For example, fast breathing motion requires sub-second sampling rates
but can be estimated from external patient images. On the other hand, slow prostate
motion can be monitored less frequently but requires direct imaging of the internal
patient anatomy. The motion information extracted from the images also varies
from simple 1D shift information to high-dimensional deformation vector fields.
Infrared and optical imaging systems have been developed to image reflective
markers or the patient’s skin surface [12–15]. External surrogate information,
extracted from the marker positions or external body contour, is used to infer
changes in internal patient anatomy. External surrogate-based motion imaging is
mostly used to monitor respiratory-induced motion or bulk patient movement, given
the strong correlation between the surrogate and internal motions. Implanted
devices, such as gold fiducials and electromagnetic transponders, serve as internal
surrogates and have been combined with x-ray imaging or electromagnetic receivers
for motion monitoring, primarily in the prostate [16–19]. During treatment delivery,
the positions of the implants are calculated from the acquired images to reflect the
position change of the tissue of interest. Hybrid techniques that combine optical
imaging of external markers and x-ray imaging of implants or internal anatomy are
used in at least one commercial treatment delivery system to take advantage of the
high imaging speed of optical imaging and the internal anatomy information from
x-ray imaging [20–23]. Internal motion information is inferred from the position
changes of external markers and the modeled correlation between internal and
external marker motions. Hybrid techniques have been used to monitor respiratory
motion in patients with tumors in the lung and liver. Ultrasound and magnetic
resonance imaging (MRI) are non-ionizing imaging techniques that provide soft
tissue contrast. Ultrasound imaging has been used to monitor prostate motion [24–
26], while MRI integrated with a linear accelerator (MR-linac) is in use worldwide
for multiple body sites including the abdomen, thorax, and prostate [27–31].
Figure 11.2 shows example elements of surface imaging, marker-based imaging,
and anatomical imaging [32] for patient motion monitoring.
11.2.2 Delivery system actions
Based on the motion monitoring results, rapid changes of radiation delivery
parameters can be made to account for the changing patient anatomy. The simplest
change of delivery parameters is through gating, a binary process where the delivery
of radiation is turned on/off when patient motion is inside/outside a treatment gating
window that specifies the acceptable range of motion [33–35]. Treatment gating has
been implemented on clinical systems in combination with various imaging
11-3
Artificial Intelligence in Adaptive Radiation Therapy
Figure 11.2. Example elements of systems for motion monitoring. (a) Infrared imaging of reflective markers
produces 1D breathing motion traces of the patient’s surface. (b) X-ray imaging of golden fiducial markers
implanted in soft tissue . (c) Anatomical imaging (cine MRI) visualizes tumor location in the sagittal and
coronal planes. ((a) and (b) Adapted from [
Wiley & Sons. Copyright 2020 American Association of Physicists in Medicine.)
32]. CC BY 3.0. (c) Adapted from [30] with permission from John
modalities. Alternatively, treatment tracking keeps the radiation delivery on
throughout the treatment. The delivery parameters are adjusted during treatment
to adapt the radiation dose distributions to the moving treatment target, so that the
tumor is always inside the high-dose region. Treatment tracking has been implemented on the CyberKnife system, where the linac position is adapted to patient
motion through a robotic arm [21, 36]. Treatment tracking through beam collimation adaptation has also been implemented on TomoTherapy systems [37, 38] and,
more recently, on PET/CT-linac systems [39], where collimator shapes are adjusted
to account for patient motion during treatment delivery. Real-time re-planning
during treatment delivery has also been investigated to adapt treatment to patient
motion, where rapid dose recalculation is performed based on the updated patient
anatomy, followed by rapid re-planning to account for the change in dose
distributions [40]. While preliminary investigations have suggested the feasibility
of such a treatment adaption scheme, it has not been implemented on commercial
systems to date.
11.2.3 Challenges for real-time ART implementation
11.2.3.1 Motion monitoring frequency and complexity
To monitor patient motion in real time, a trade-off must be made between motion
monitoring frequency and the complexity of the motion information obtained. For
example, respiration occurs at a temporal rate of 3–5 s/cycle and the range of motion
during a motion cycle can be up to several centimeters. To capture the rapid
anatomy changes induced by respiration with sufficient accuracy to limit dose
deviations from motion to reasonable levels, patient motion states need to be
updated at a sub-second temporal rate through imaging or some other means of
motion monitoring. Infrared and optical-based imaging technologies have the
advantage of high sampling frequency (>20 Hz) [41, 42]; however, these surface
11-4
Artificial Intelligence in Adaptive Radiation Therapy
imaging techniques cannot directly capture internal anatomy changes. Instead, they
rely on a correlation model between external and internal motions, potentially
degrading the accuracy of obtained motion information. Furthermore, the motion
information obtained is limited to low-dimensional signals (1D shift or 3D shift and
rotations). These low-dimensional motion signals are simplified representations of
complex anatomy changes, which include both rigid motion and non-rigid deformation. X-ray imaging of implanted fiducial markers or patient anatomy can
provide direct information on internal motion, however the imaging frequency is
usually lower due to concerns over patient imaging dose and the extra time required
for image analysis, for example, marker segmentation or image registration, to
extract the motion information. An imaging frequency of 9.5 Hz has been reported
for prostate motion monitoring [43] and imaging time intervals of 10–50 s has been
used when combining x-ray imaging with high frequency optical imaging [44]. The
clinically reported imaging interval of CyberKnife orthogonal x-ray projection
imaging is sometimes longer than 1 min and is used to monitor erratic movements of
patients during craniospinal treatments. The x-ray projection imaging has also been
combined with fast surface-based imaging to update a motion model as described in
section 11.3.1.1 for breathing motion monitoring.
Cine MRI mode has been used to monitor motion on MR-linac systems for
multiple body sites including the thorax, abdomen, pelvis, and prostate. MRI
provides superior soft tissue contrast compared to x-ray imaging with no imaging
dose. Yet the slower imaging speed and the time required to calculate target
deformation based on the acquired image samples limit the imaging frequency of
cine MRI to around 4–5Hz[45, 46]. Cine MRI is also limited to one or a few 2D
planes and cannot fully capture the motion in 3D space. Ultrasound is another
ionization-free imaging modality with good soft tissue contrast. Currently the only
commercial system for ultrasound-based motion tracking is designed for monitoring
3D rigid prostate motion. The system creates a continuously scanned 3D-reconstructed volume with a motor-driven sweeping motion. An image reading frequency
of 2 Hz has been used [47]. In-house investigations have also demonstrated the
potential of ultrasound in monitoring breathing motion [ 48], where robotic arms and
breathing motion control/compression were used to ensure close contact between the
ultrasound probe and the patient surface.
11.2.3.2 System latency
When patient motion occurs, the treatment delivery system processes the motion
information and takes action to adapt to the changing anatomy. The processing time
results in system latency, which refers to the time delay between the motion
occurring and the system acting. Various factors contribute to system latency,
including the image acquisition time, the time required to process the images to
extract motion information, and the time needed for the system to adjust delivery
parameters. The system latency therefore depends on (i) the imaging modality, for
example, video camera-based external marker imaging systems generally have a
shorter latency than radiographic or tomographic anatomical imaging systems;
(ii) the complexity of motion analysis (rigid or deformable); and (iii) the mechanism
11-5
Artificial Intelligence in Adaptive Radiation Therapy
of system action for motion adaption, for example, the binary action of turning on/
off the beam (treatment gating) generally takes less time than treatment tracking or
re-planning. A wide range of system latencies have been reported, from below 100
ms for external marker imaging-guided treatment gating/tracking [49, 50], to around
300 ms for MRI-guided treatment gating [51, 52]. The acceptable system latency
depends on the velocity of the motion the system is trying to account for. For
example, adapting to fast breathing motion will require a system with shorter
latency than adapting to slow prostate motion. In vitro studies of ultrasound-guided
treatment tracking have demonstrated adequate compensation of prostate motion
with a total system latency of −1s[53]. The ability to predict motion trajectories can
allow for longer latencies with acceptable residual errors.
11.3 AI in real-time ART workflows
While intrafraction motion-induced anatomy changes are complex, those changes
are continuous and correlated both spatially and temporally. Such correlation
implies that historic information of patient anatomy and motion might provide
valuable knowledge on future anatomical changes of a patient during treatment
delivery. Indeed, when medical experts review patient images acquired over time,
they can make reasonable conjectures on future patient motion, for example,
transition from inhale to exhale, and identify image artifacts that are not caused
by patient motion. AI models, by definition, are models that can learn at least
somewhat like human beings do [54]. By training AI models from historic patient
motion data, prior knowledge embedded in the models can be taken advantage of to
support the real-time ART workflow including efficient sampling of patient data
during treatment delivery, accurate motion analysis, and fast system reaction based
on the motion information. While recently developed AI approaches to address
motion are mostly deep learning models, in this chapter we adopt the broad
definition of AI and review both clinical and experimental AI models of various
architectures for real-time ART workflow.
11.3.1 Improving intrafraction motion monitoring through AI
AI models have been developed to overcome temporal and spatial limitations of
different imaging techniques for motion monitoring to produce high-quality motion
information at high temporal sampling rates.
11.3.1.1 Correlating external imaging with internal anatomy through AI
External imaging has the advantage of high sampling frequency, yet the motion
monitoring accuracy is compromised by monitoring patient surface motion instead
of internal anatomy changes. Fiducial and anatomical imaging, on the other hand,
can directly monitor internal motion but are limited by lower sampling frequencies.
An external–internal correlation model (ECM) has been developed to combine
different imaging techniques together for high frequency monitoring of internal
motion. As illustrated in figure 11.3, prior knowledge of the correlation between
external and internal motion is learnt from a pre-treatment training dataset that
11-6
Artificial Intelligence in Adaptive Radiation Therapy
Figure 11.3. Illustration of pre-treatment model training and in-treatment motion monitoring, with the
CyberKnife system as an example. (Reproduced from [
32]. CC BY 3.0.)
consists of both internal images and external images at different motion states.
During treatment delivery, internal motion is predicted by combining the ECM
model with external motion information sampled at a high frequency. The ECM
model may also be updated online during treatment delivery, when new pairs of
internal and external images are acquired.
Different imaging modalities have been investigated for ECM model-based
motion monitoring. Bertholet et al [55] utilized pre-treatment free-breathing
CBCT and infrared images of reflective markers (RPM) to construct the ECM
model. The ECM model parameters were optimized to output 3D motion information based on 1D motion traces extracted from an infrared imaging system, which
captures the motion of a block placed on the patient’s skin surface. Fiducial markers
were segmented in CBCT projections, and 3D motion trajectories were estimated
through maximum likelihood estimation, assuming a Gaussian spatial distribution
of the fiducial markers. The CyberKnife Synchrony system trains the ECM model
using orthogonal x-ray images and images of light-emitting diode (LED) markers
placed on the patient’s surface. During model training, at least eight pairs of x-ray
images are acquired at different breathing phases and are used to determine internal
target motions. The ECM model is trained to correlate the internal treatment target
motion to the external LED marker motion detected by cameras. Chen et al [56]
investigated the feasibility of estimating internal motion from surface imaging
through ECM. 4DCT images of different breathing motion phases and surface
images extracted from the 4DCT scan were used for model training. Internal and
external motion information was estimated by registering internal organ meshes and
external surface meshes, respectively, using a non-rigid point matching algorithm.
The ECM model was trained using a composite matrix that consists of both internal
and external deformation vector fields, allowing internal motion vectors to be
predicted based on external ones.
ECM models are limited to respiratory-induced motions, as other internal
anatomy changes, such as prostate motion caused by organ fillings, do not
significantly correlate with external surface motion. Various model parameterization
strategies have been proposed for ECM. The simplest assumes a linear relationship
between the 3D internal motion vectors and the 1D external motion signals.
11-7
Artificial Intelligence in Adaptive Radiation Therapy
More complicated quadratic models have also been investigated to better represent
the relationship between internal and external motions. Time-delayed samples [57]
and the fi rst-order derivative [58] of external motion have been combined with linear
or quadratic models to account for breathing hysteresis. Alternatively, the
CyberKnife system uses two quadratic models for the inhale phase and exhale
phase, respectively. To model higher-dimensional motion signals, such as deformation vector fields, principal component analysis (PCA)-based models have been
investigated [56]. Principal motion characteristics are learned from a training dataset
that composites external and internal deformation vector fields. By projecting
external deformation vector fields onto the subspace spanned by principal components (eigenvectors), the model parameters (eigenvalues) associated with the motion
state can be determined and used to calculate the corresponding internal deformation vector fields.
11.3.1.2 Markerless motion monitoring through AI
Implanted markers, such as gold fiducials and electromagnetic transponders, have
been used to provide real-time position information of internal targets. However,
marker implantation is invasive, adds workflow complexity, and carries the risk of
patient bleeding and marker migration. Markerless motion monitoring using x-ray
imagers equipped on linacs is desirable. However, the limited soft tissue contrast and
tissue overlay in 2D projection images may compromise the accuracy of markerless
motion tracking.
AI models have been developed to integrate prior knowledge of high contrast 3D
patient images, acquired before treatment, with low contrast 2D images acquired in
real time to improve 3D motion monitoring accuracy. Template matching methods
have been investigated for lung and spine target tracking, where prior knowledge of
patient anatomy was modeled as a template library of digitally reconstructed
radiographs (DRRs). During treatment delivery, motion information has been
obtained by registering acquired 2D x-ray images to template DRRs. Cai et al
generated a template library of both MV and kV DRRs from high-resolution CBCT
reconstructions [59]. 3D target shifts were calculated from kV-to-DRR and MV-toDRR registration results. Motion monitoring using kV images only has also been
investigated, where kV DRRs at different gantry angles were created from planning
CT images. During treatment, kV projections were acquired at 7 frames per second
and matched to the DRR templates to determine 2D target position. 3D motion
information was then obtained through triangulation of matched projections.
Deep learning models have also been investigated for markerless motion
monitoring, where a neural network was trained to localize tumors from 2D kV
x-ray images. During network training, either a 4DCT dataset or a 3D CT dataset
undergoing different deformations was used to mimic anatomical changes at the
time of treatment delivery. Digitally reconstructed radiographs (DRRs) were
generated from the CT datasets to simulate kV images acquired during treatment.
The network was optimized to take the simulated DRRs as input and output the
corresponding target locations, which are known from the training CT images.
Grama et al [60] proposed a Siamese network comprising twin subnetworks to
11-8