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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
X
- •Foreword
- •Acknowledgments
- •Editor biographies
- •Yi Wang
- •X. Sharon Qi
- •List of contributors
- •1.2.3 Feature engineering and representation
- •1.2.4 Linear separability
- •1.2.5 Classical models
- •1.1 A brief introduction to AI
- •1.2 Machine learning basics
- •1.2.1 Learning paradigms
- •1.3 Artificial neural networks
- •1.3.1 Feed-forward neural networks
- •1.3.2 Recurrent neural networks
- •1.3.3 Convolutional neural networks
- •1.3.4 Attention
- •1.3.5 Training neural networks
- •1.3.6 Applications and use cases of deep learning
- •1.4 Model training and evaluation
- •1.4.1 Hyperparameters
- •1.4.2 Data split
- •1.4.3 Evaluation metrics
- •1.5 Generative models
- •1.5.1 Generative adversarial networks
- •1.5.2 Diffusion models
- •1.5.3 Applications and use cases
- •1.6 Ethical consideration and bias
- •1.6.1 Transparency and explainability
- •1.6.2 Bias and fairness
- •1.6.3 Data privacy violation
- •1.6.4 Risk and misuse
- •1.7 Summary
- •References
- •2.1 Introduction
- •2.1.2 Staff roles in radiation therapy
- •2.2 Overview of AI in radiation therapy
- •2.2.1 Patient evaluation and dose prescription
- •2.2.2 Treatment simulation
- •2.2.3 Contouring
- •2.2.4 Treatment planning
- •2.2.5 Quality assurance
- •2.2.6 Treatment delivery
- •2.2.7 Response assessment and toxicity management
- •2.3 Summary
- •3.1 Introduction
- •3.1.1 Introduction of clinical decision making and AI
- •3.1.2 The role of AI in clinical decision making
- •3.2 AI algorithms for clinical decision making
- •3.2.2 Radiomics
- •3.2.3 Data integration by AI
- •3.2.4 Interpretability of AI models
- •3.3 Application of AI in clinical decision making
- •3.3.1 Diagnosis and disease phenotyping
- •3.3.2 Personalized treatment
- •3.3.3 Treatment outcome and prognosis prediction
- •3.4 Challenges and future directions of AI in clinical decision making
- •3.4.1 Challenges and concerns
- •3.4.2 Future directions
- •3.5 Summary
- •References
- •4.1 Introduction
- •4.2 Imaging for treatment planning
- •4.2.1 CT simulation
- •4.2.2 4D-CT
- •4.2.3 PET/CT
- •4.2.4 MRI
- •4.3 Imaging for treatment guidance
- •4.3.1 Portal imaging
- •4.3.2 CBCT
- •4.3.3 CT-on-rail and CT-linac
- •4.3.4 MR-linac
- •4.3.5 PET-linac
- •4.4 Imaging for motion management
- •4.4.1 ExacTrac
- •4.4.2 Varian triggered imaging
- •4.4.3 4D-CBCT
- •4.4.4 Cine MRI
- •4.4.5 4D-MRI
- •4.4.6 Surface imaging
- •4.5 Imaging for treatment assessment
- •4.5.1 Contrasted CT
- •4.5.2 PET/CT
- •4.5.3 Functional MRI
- •4.6 Summary
- •5.1 Introduction to big data in radiation oncology
- •5.1.1 Overview of big data
- •5.1.2 Sources of big data in radiation oncology
- •5.1.3 Big data and AI in radiation oncology
- •5.2 Big data lifecycle in radiation oncology
- •5.2.1 Data aggregation and storage
- •5.2.2 Data sharing and security
- •5.4 The application of big data in radiation oncology
- •5.4.1 Medical image segmentation
- •5.4.2 Automatic treatment planning
- •5.4.3 Treatment response prediction
- •5.4.4 Quality assurance and patient safety
- •5.4.5 Clinical decision support
- •5.2.3 Data visualization
- •5.2.4 Knowledge creation and implementation
- •5.2.5 Data archiving and deletion
- •5.3 Big data analytics with AI
- •5.3.1 Data processing and integration
- •5.3.2 AI modeling
- •5.5 Challenges and future perspectives
- •5.6 Summary
- •Reference
- •6.1 The road to ART
- •6.1.1 3D conformal radiotherapy (3DCRT)
- •6.1.2 Intensity modulated radiotherapy (IMRT)
- •6.1.3 Image-guided radiotherapy (IGRT)
- •6.1.4 Adaptive radiotherapy (ART)
- •6.2 ART workflow and implementation
- •6.2.2 Current practice
- •6.2.3 Clinical impact
- •6.3 Considerations for implementing online ART
- •6.3.1 Time as a limiting factor
- •6.3.2 Implications for fast and reliable re-planning
- •6.3.4 Clinical considerations
- •6.4 Summary
- •7.1 Components of ART workflow
- •7.1.1 Simulation
- •7.1.2 Pre-planning
- •7.1.3 Online imaging and daily re-planning
- •7.1.4 Quality assurance
- •7.2 AI-driven ART
- •7.2.1 Simulation
- •7.2.2 Pre-planning
- •7.2.3 AI for delivery
- •7.3 Outlook and future directions
- •7.3.1 Real-time ART with AI
- •7.3.2 Dose escalation and functional adaption with AI
- •7.4 Summary
- •References
- •8.1 Introduction
- •8.2 Synthetic CT: deep learning methods
- •8.2.1 Conventional methods
- •8.2.2 U-Net
- •8.2.3 Generative adversarial networks
- •8.2.4 Denoising diffusion probabilistic model
- •8.3 Synthetic CT from CBCT
- •8.3.1 Noise and artifact reduction
- •8.3.2 Online dose calculation
- •8.3.3 Online image segmentation
- •8.4 Synthetic CT from MRI
- •8.4.1 Synthetic image accuracy
- •8.4.2 Dose calculation in MR-only radiation therapy
- •8.4.3 PET attenuation correction
- •8.4.4 Image registration
- •8.5 Discussion and outlook
- •8.6 Summary
- •References
- •9.1 AI-based image registration and segmentation for ART
- •9.1.1 Adaptive radiation therapy
- •9.2 Artificial intelligence
- •9.2.1 What is machine learning?
- •9.2.2 What is deep learning?
- •9.3 Deep learning: the basic components
- •9.3.1 Convolutional neural networks: looking at the picture
- •9.3.2 Pooling layers: keeping what matters most
- •9.3.3 Fully connected (dense) layers: bringing it all together
- •9.3.4 Activations
- •9.3.5 Loss: driving the model
- •9.3.6 Auto-encoders: remove the noise
- •9.3.7 Supervised versus unsupervised learning
- •9.3.8 Pre-trained convolutional neural networks
- •9.4 Image registration: bringing two images together
- •9.4.1 Registration similarity metrics
- •9.4.2 Types of registrations
- •9.5 AI-based image registration
- •9.5.1 Supervised learning
- •9.5.2 Unsupervised learning
- •9.5.3 Registration in ART
- •9.5.4 Commonalities in architectures
- •9.6 Image segmentation
- •9.6.1 Introduction: coloring by the numbers
- •9.6.2 Segmentation networks
- •9.6.3 Best practices
- •9.7 Summary
- •10.1 Introduction
- •10.1.1 Overview of chapter content
- •10.2 The landscape of AI-assisted dose prediction
- •10.2.1 Traditional machine learning for dose prediction
- •10.2.2 Deep learning-based dose prediction
- •10.2.3 Challenges in AI-assisted dose prediction
- •10.3 Re-planning workflows powered by AI
- •10.3.1 Deep learning for re-planning pipelines
- •10.4 Future directions of AI-assisted dose prediction and re-planning
- •10.5 Summary
- •11.1 Introduction
- •11.2.1 Imaging-based motion monitoring
- •11.2.2 Delivery system actions
- •11.2.3 Challenges for real-time ART implementation
- •11.3 AI in real-time ART workflows
- •11.3.1 Improving intrafraction motion monitoring through AI
- •11.3.2 Mitigating system latency through AI
- •11.4 AI for ART delivery: future directions
- •11.4.1 Management of non-respiratory motion
- •11.4.2 Training AI models with small or unpaired datasets
- •11.4.4 Biology-guided ART delivery
- •11.5 Summary
- •References
- •12.1 Introduction
- •12.2 Patient QA
- •12.2.1 Pre-planning QA
- •12.2.2 Pre-treatment plan QA
- •12.2.3 On-treatment QA
- •12.3 Treatment delivery systems and instruments
- •12.3.1 Machine commissioning
- •12.3.2 Machine QA
- •12.3.3 Dosimetry tool QA
- •12.4 Summary
- •References
- •13.1 Data resources for response modeling in radiotherapy
- •13.1.1 Clinical data
- •13.1.2 Imaging (radiomics)
- •13.1.3 Treatment planning (dosiomics)
- •13.1.4 Multiomics
- •13.2 Radiotherapy treatment outcome modeling
- •13.2.1 TCP/NTCP in radiotherapy
- •13.2.2 Clinical outcomes versus PROs
- •13.2.3 Machine learning response prediction
- •13.2.4 Explainability of ML response models
- •13.2.5 Sample use cases
- •13.3 AI response-based adaptive radiotherapy
- •13.3.1 Requirements and challenges
- •13.3.2 Prediction versus treatment optimization
- •13.3.3 Sample use cases
- •13.4 Challenges and recommendations
- •13.5 Summary
- •Acknowledgments
- •References
- •14.1 Overview of challenges in AI-driven ART
- •14.2 Data challenges
- •14.2.1 Data availability
- •14.2.2 Data quality
- •14.2.3 Data privacy
- •14.3 Technical challenges
- •14.3.2 Model robustness and generalizability
- •14.3.3 Model explainability and interpretability
- •14.4 Challenges associated with online and real-time workflows
- •14.4.1 Image quality
- •14.4.2 Dose calculation
- •14.4.3 Real-time ART
- •14.5 Operational challenges
- •14.5.1 Clinical validation
- •14.5.3 Staff training
- •14.5.4 User experiences
- •14.5.5 Quality management program
- •14.5.6 Financial challenges
- •14.6 Ethical, regulatory, and legal challenges
- •14.6.1 Ethical issues
- •14.6.2 Regulatory and legal issues
- •14.7 Summary
- •References
- •15.1 Clinical considerations for CT-based offline ART
- •15.1.1 Patient and site selection
- •15.1.2 Re-simulation
- •15.1.3 Re-planning
- •15.1.4 Plan summation and evaluation
- •15.1.6 Limitations and future directions
- •15.2 Clinical considerations for CBCT/CT-based online ART
- •15.2.2 Patient and site selection
- •15.2.3 Simulation
- •15.2.4 Pre-planning review
- •15.2.5 Reference planning
- •15.2.9 Limitations and future directions
- •15.3 Summary
- •References
- •16.1 Introduction
- •16.2 Overview of MRI-guided ART systems
- •16.3 MRI-guided ART workflow
- •16.4 AI applications for MRI-guided ART
- •16.4.1 Synthetic CT generation
- •References
- •16.4.2 Auto-segmentation
- •16.4.3 Image registration
- •16.4.4 Others
- •16.4.5 Future AI development and implementation
- •16.5 Summary
- •17.1 Functional PET-guided ART
- •17.1.1 PET-based functional imaging overview
- •17.1.2 From anatomy to function: the power of PET in radiation therapy
- •17.1.5 Conclusions and future prospects
- •17.2 Functional MRI-guided ART
- •17.2.1 From anatomy to function: the power of functional MRI in radiation therapy
- •17.2.4 Conclusion and future prospects
- •17.3 Summary
- •References
- •18.1 Proton ART
- •18.1.1 Clinical context and necessity
- •18.1.2 Patient populations
- •18.1.4 Rationale for AI in proton ART
- •18.2 AI in proton ART
- •18.2.1 Imaging
- •18.2.2 Deformable and rigid registration
- •18.2.3 Contour propagation
- •18.2.4 Dose calculations
- •18.2.5 Plan optimization
- •18.2.6 Other developments
- •18.3 Implementation of adaptive proton therapy
- •18.4 Summary
- •References
- •19.1 Designing clinical trials with AI
- •19.1.1 The essential role of clinical trials
- •19.1.2 Trial protocols and methodologies
- •19.1.3 AI-driven clinical trial design and execution
- •19.1.4 Incorporation of digital twins (DTs) in clinical trials
- •19.2 Implementation of AI in ongoing clinical trials
- •19.2.1 Integration with existing clinical trial frameworks
- •19.2.2 Quality assurance, compliance, and standardization
- •19.3 Case studies of AI in adaptive radiotherapy trials
- •19.3.1 Overview of guidance for advanced radiotherapy in clinical trials
- •19.3.2 AI in the radiotherapy clinical trial quality assurance processes
- •19.4 Ethical and regulatory considerations
- •19.4.1 Patient consent and data privacy
- •19.4.2 Bias, fairness, and transparency
- •19.4.3 Regulatory guidelines and compliance
- •19.5 Future directions and challenges
- •19.5.1 Emerging technologies and techniques
- •19.5.2 Alternative strategies
- •19.6 Conclusion
- •19.7 Summary
- •References
- •20.1 Risk management
- •20.1.1 Prospective risk assessments
- •20.1.2 Root cause analysis

Artificial Intelligence in Adaptive Radiation Therapy
CBCT shows anatomic change but can also be determined from computed
tomography (CT), positron emission tomography (PET), or MRI. Online adaptive
therapy is generally based on CBCT or MRI, with PET being a recent addition. In
principle, the use of MRI or PET can provide important functional information
about changes in the tumor, in addition to anatomic changes. However, adaptation
that shrinks the target volume based on either anatomic or functional imaging risks
undertreatment of the disease.
4.2 Imaging for treatment planning
Tumor delineation stands as a pivotal process in radiotherapy, influencing treatment
accuracy and patient outcomes. Medical imaging plays a crucial role in this regard
by facilitating the precise localization of tumor targets for radiation delivery.
Historically, planar x-ray imaging served as the primary modality for this purpose.
However, the advent of CT revolutionized the field by enabling three-dimensional
visualization of both the tumor and surrounding normal tissues within the patient
anatomy. CT integration into radiotherapy treatment planning ushered in a new era
of three-dimensional dose optimization and enhanced patient positioning accuracy.
Despite these advancements, CT exhibits limitations in tissue contrast and lacks
functional insights.
The emergence of linear accelerators (linacs) and advanced dose delivery
techniques underscores the importance of achieving higher precision in radiotherapy. The accuracy of manual tumor delineation on CT now dictates the level
of treatment precision attainable. Consequently, there is a growing demand for more
refined tumor definition techniques to optimize patient outcomes. Integrating
complementary imaging modalities such as MRI and PET offers a promising
avenue for enhancing tumor delineation. MRI excels in providing superior soft
tissue contrast, thereby facilitating more accurate tumor delineation compared to
CT. PET imaging, on the other hand, offers valuable metabolic and functional
information critical for tumor grading and delineation. By combining the strengths
of CT with these modalities, clinicians can access a comprehensive dataset that
improves tumor definition and, ultimately, treatment efficacy in radiotherapy.
4.2.1 CT simulation
CT provides volumetric imaging for both diagnostic imaging and radiation
oncology. CT uses a gantry-mounted x-ray source and detectors to record x-ray
transmission of an object. The x-ray source consists of a cathode and an anode.
Electrons are produced by the cathode and then accelerated towards a rotating
tungsten anode to create x-ray via Bremsstrahlung. The detectors are usually made
of a scintillating material, converting x-ray to visible light, which is then collected by
a photodiode. During image acquisition, the x-ray and detectors rotate simultaneously. The raw x-ray projection data form a sinogram. The CT image is
reconstructed from the sinogram using filtered-back-projection (FBP) techniques.
The output of FBP is the linear attenuation coefficient (μ) for each voxel. CT images
are displayed with Hounsfield units (HU):
4-2

Artificial Intelligence in Adaptive Radiation Therapy
=×
U 1000 .
mm
−
material water
m
water
CT is commonly used in radiation oncology for patient simulation. It provides
planning CT volumes for patient positioning and dose calculation. The CT
simulator has a flat couch to mimic the treatment couch of a linac so that the
patient pose is similar during treatment. They also have lasers to help reproduce the
patient position when aligning for treatment. Unlike CTs in diagnostic imaging, HU
accuracy is crucial for CT simulators. The HU is converted to electron density and
serves as the basis for dose calculation. The electron density derived from the CT
simulator is the gold standard for dose calculation. Therefore, an accurate
calibration curve is required during the commissioning of the scanner to ensure
accurate dose calculation.
Technological advances in CT have been applied to radiation oncology. Coolens
et al implemented a 320-slice volumetric scanner for CT simulation [7]. By using
volume scanning instead of helical scanning, the scanner can capture the entire
treatment site within a single gantry rotation. The scan time is thus reduced, and
motion artifacts are minimized. Most centers, however, still use the larger bore
helical scanners. Dual-energy CT (DECT) was investigated as a candidate for
simulation in proton therapy. By capturing CT images under two energies, DECT
can differentiate x-ray attenuation change in density or chemical composition [8]. It
can then calculate stopping power ratio and improve proton radiotherapy planning
by reducing range uncertainties. Currently, the high level of noise still hinders the
wider adoption of DECT in proton therapy simulation [9]. More recently, clinical
deployment of photon counting CT (PCCT) also provides opportunities in the
improvement of radiation simulation. The photon counting detector can resolve
energy by counting photons in selected energy bins. It has the potential to directly
calculate the stopping power ratio using the Bethe–Bloch equation. The scanner
achieved stopping power ratio estimates that are comparable to DECT scanners
[10]. PCCT is also capable of generating quantitative contrast enhanced CT scans. It
can leverage the photon counting detector’s energy resolution to provide increased
tissue and contrast agent discrimination. This capability helps PCCT generate
virtual non-contrast electron density and stopping power [11]. Patients can potentially avoid a non-contrast scan as well as its dose during simulation.
4.2.2 4D-CT
Four-dimensional CT (4D-CT) is often used for motion management during patient
simulation. Patient breathing motion can often introduce uncertainty and blurring in
simulation CT scans. 4D-CT captures multiple CT volumes at different phases of a
patient’s breathing motion. This acquisition protocol oversamples at every position
of interest in the superior–inferior direction by using the axial cine mode. Multiple
CT images are reconstructed per slice and each of them represents a different
anatomical state during a respiratory cycle. These CT images are sorted based on the
phase of breathing measured by a breathing monitoring device such as respiratory
gating system or abdominal belt [12]. The entire 4D-CT volume captures the patient
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Artificial Intelligence in Adaptive Radiation Therapy
motion throughout the entire breathing cycle. Maximum intensity projection and
average CT are often compiled from the 4D-CT volume to account for patient
motion. Clinicians can then define the treatment target based on the tumor and
organs-at-risk (OARs) motion throughout the treatment.
4.2.3 PET/CT
PET serves as a valuable tool for investigating the biodistribution of radio-labeled
tracers, thereby enabling crucial metabolic and functional insights based on the
biochemical pathways of these tracers. Among the commonly utilized tracers,
fluorine-18 fludeoxyglucose (
18
F-FDG) stands out as a glucose analog that undergoes cellular uptake and subsequent intracellular entrapment. Through this mechanism, FDG-PET identifies metabolically active tissue within the body. PET/CT
imaging combines anatomical and metabolic information, with CT images providing anatomical reference points and aiding in attenuation correction for PET data.
The integration of PET and CT functionalities within a hybrid device mitigates the
risk of misalignment associated with patient repositioning [13]. PET/CT plays a
pivotal role in identifying primary tumors and associated regional lymph nodes
based on the increased uptake of
18
F-FDG [14]. Its utility becomes particularly
evident in scenarios where tumor volumes are poorly defined or when dose
escalation is warranted, necessitating precise delineation of tumor volumes and
distinct boundaries from surrounding tissues.
Studies have underscored the efficacy of PET/CT in tumor delineation for various
cancers, including lung, head and neck, esophageal, and cervical malignancies [15].
In lung cancer, PET/CT facilitates more accurate lymph node staging [16], reduces
inter-observer variability during tumor delineation [17], and refines the delineation
of tumor borders in conjunction with atelectasis [18]. Notably, PET/CT-derived
gross tumor volumes (GTVs) have demonstrated superior accuracy when comparing
with surgical specimens in a head and neck cancer study [19]. Prospective
investigations in esophageal and cervical cancers have corroborated the positive
impact of PET/CT on tumor delineation and the significant reduction in late
radiation toxicity [20–22]. The evolution of PET/CT-guided radiotherapy has gained
interest in dose painting strategies targeting specific tumor sub-volumes based on
biological imaging [23]. Escalated doses can be prescribed to radio-resistant tumor
regions to enhance local tumor control [24].
Despite the widespread use of
18
F-FDG, other PET tracers such as fluorine-18
fluoromisonidazole (FMISO) and fluorine-18 fluoroazomycin arabinoside (FAZA)
provide non-invasive and quantitative assessments of tumor hypoxia—acritical
determinant of radiation treatment resistance. Hypoxia volumes measured via
FMISO-PET serve as predictive factors for patient survival in head and neck cancer
[25]. Targeting hypoxic tumor volumes with escalated doses represents a promising
strategy to improve tumor control. Additionally, the active cellular proliferation
characteristic of tumors can be assessed using fluorine-18 fluorothymidine (
a thymidine analogue that monitors thymidine kinase activity—a surrogate marker
for cell proliferation [26]. Given its low uptake in inflammatory tissues,
18
F-FLT),
18
F-FLT is
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Artificial Intelligence in Adaptive Radiation Therapy
preferred in highly inflammatory cancers such as head and neck cancer, where falsepositive results from FDG-PET could lead to the enlargement of GTVs [27].
While PET imaging aids in avoiding tumor misses and minimizing unnecessary
radiation exposure to healthy tissues, its spatial resolution remains a limitation
compared to modern CT scanners [28]. The spatial resolution of current PET
scanners typically ranges from 5 to 8 mm, which may result in the oversight of small
lesions, particularly in cases of significant patient motion [29]. Furthermore, there is
a risk of including non-malignant FDG-avid tissue within the target volume,
potentially increasing the likelihood of long-term complications. The delineation
method employed significantly impacts the quality assessment of GTVs. While
thresholding based on a percentage of the maximum tumor standardized uptake
value (SUV
) is a common approach [30], alternative methods such as contrast-
max
based [31], gradient-based [32], and statistically stochastic algorithms [33] are also
utilized, with their efficacy dependent on imaging parameters and tumor characteristics [30].
4.2.4 MRI
Despite its established role in diagnostic imaging, MRI has emerged as a valuable
complement to CT imaging in radiotherapy treatment planning. MRI offers several
advantages over CT, notably superior soft tissue contrast and its intrinsic threedimensional imaging capability. However, limitations include the absence of
electron density information, geometric distortion, and a restricted field-of-view
(FOV). Nonetheless, advancements in MRI technology have facilitated its integration into the radiotherapy workflow. Initially, disparities in patient positioning
between diagnostic MR scans and radiotherapy simulation CT scans posed
challenges due to the differences between curved cushion-lined MR systems with
flat-bed couches in CT scanners. Subsequent availability of commercial MRIsimulators featuring flat-bed couches has mitigated this issue. Nevertheless, image
co-registration remains essential to align the two modalities within the same
coordinate system. Image registration introduces systematic geometrical uncertainties of 2–3 mm throughout the treatment process, potentially compromising tumor
control [34 ]. The integration of MRI into treatment planning has yielded significant
improvements in target delineation quality and reduced inter-observer variability,
particularly in tumors of the brain, head and neck, and pelvis [35].
As a multi-parametric modality, MR systems offer both anatomical and functional information through diverse techniques and sequences. The concept of MRonly treatment planning has recently gained traction [34]. While MRI inherently
lacks direct electron density information, various methods have been proposed to
generate synthetic CT images based on MRI data [36, 37]. However, challenges such
as image distortion and artifacts have hindered the widespread adoption of MRI as
a primary imaging modality in radiotherapy planning. Despite these challenges,
ongoing research aims to address these limitations and further optimize the
integration of MRI into radiotherapy planning workflows [38–41].
4-5

Artificial Intelligence in Adaptive Radiation Therapy
4.2.4.1 Anatomical MRI
MRI relies on the detection of nuclear magnetic moments, primarily originating
from hydrogen nuclei (protons) that are predominantly found in water and lipids
within the body. These protons undergo precession at the Larmor frequency
(ω
= γB0) when subjected to a strong main magnetic field (B0), where γ represents
0
the gyromagnetic ratio. MR images can be acquired to visualize the distribution of
protons in the body, commonly referred to as proton density images. However,
proton density images typically exhibit limited tissue contrast, although they prove
valuable in diagnosing conditions such as edema and inflammatory diseases [42].
The most frequently employed anatomical MRI sequences include T1-weighted
(T1w) and T2-weighted (T2w) scans, which rely on the manipulation of longitudinal
(T1) and transverse (T2) relaxation times. These relaxation times are influenced by
molecular motion and interactions within tissues, resulting in varying signal
characteristics across different tissue types. Contrast in MRI is achieved by
weighting signals based on T1 and T2 relaxation times. T1w images are generated
using short echo time (TE) and relaxation time (TR), whereas T2w images are
produced using relatively longer TE and TR parameters. Various techniques, such
as fat saturation, inversion-recovery imaging, and opposed-phase imaging, can be
employed to suppress fat signal, enhancing tissue contrast [43]. T1w and T2w images
find extensive utility in MR-guided adaptive radiotherapy, facilitating improved
visualization and delineation of target volumes [44].
4.2.4.2 Post-contrasted MRI
Paramagnetic or superparamagnetic agents capable of significantly altering the
relaxation times (T1 and/or T2) of nearby protons serve as crucial MRI contrast
agents. Among the most employed agents is gadolinium-based contrast agent, which
effectively shortens T1 relaxation times. Tissues enhanced with gadolinium-based
agents exhibit increased signals in T1w images. Studies indicate that gadoliniumcontrasted MRI yields better concordance with biopsy findings compared to T2w
MRI [45]. Another prevalent contrast agent is superparamagnetic iron oxide (SPIO)
particles, which attenuate T2 signals in tissues where they accumulate, and are
commonly employed in imaging of kidneys and spleens [46]. Additionally, manganese-based and iron platinum-based agents are currently under investigation [47].
Dynamic contrast enhanced MRI (DCE-MRI) involves the acquisition of T1w
images before and after the administration of contrast agents. DCE-MRI enables
the measurement of multiple parameters related to tissue perfusion and microvascular status. Integration of DCE-MRI into tumor delineation protocols has been
shown to reduce inter-observer variability [48]. Dynamic susceptibility contrast MRI
(DSC-MRI) captures local magnetic inhomogeneities between extra- and intravascular volumes, generating attenuated T2 signals due to the presence of contrast
agents within the vasculature [49]. Moreover, blood oxygen level-dependent
(BOLD) imaging exploits differences in magnetic susceptibility between oxyhemoglobin and deoxyhemoglobin, while diffusion-weighted imaging (DWI) quantifies
molecular diffusion properties within tissues. These modalities have demonstrated
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Artificial Intelligence in Adaptive Radiation Therapy
efficacy in delineating tumor boundaries [50–54]. However, considerable variation
exists in the consistency of target volumes delineated using these protocols [55, 56].
4.3 Imaging for treatment guidance
4.3.1 Portal imaging
Modern image-guided radiation therapy (IGRT) systems are typically equipped with
two imaging panels, the electric portal imaging device (EPID) and kV imaging panel.
The EPID is used to capture 2D-MV planar images. It is typically an amorphoussilicon (a-Si) flat-panel imaging device mounted on a robotic arm directly under the
linac head. The Varian Truebeam equipped with the latest aS1200 panel has a 40 × 40
2
active imaging area and a pixel size of 0.0336 cm. The robotic arm allows a source
cm
to EPID distance from 95 to 180 cm. Improvements have been made on the panel so
that it can handle a flattening filter free (FFF) dose rate without saturation at any
source to detector distance [57]. Elekta linacs are equipped with iViewGT EPID. The
active imaging area is 41 × 41 cm
× 1024 diodes with a pitch of 400 μm[58]. Both the Varian and Elekta IGRT systems
are also capable of capturing kV planar images. The Varian On-Board Imager (OBI)
has two robotic arms that are mounted perpendicular to the radiation beam. One
holds the x-ray source and the other holds the a-Si flat-panel detector. The active
imaging area is 40 × 30 cm
imager distance from 100 to 182.5 cm depending on the imaging protocol. The Elekta
X-ray Volume Imaging (XVI) system has a similar layout with two robotic arms. The
active imaging is 42.5 × 42.5 cm
systems are comparable [58, 59]. Both kV and MV planar imaging techniques are
quick to acquire and deliver less dose to the patient. While soft tissue might be difficult
to visualize on planar images, bony structures or fiducials are visible. This makes
planar x-ray imaging ideal for treatment set-up verification.
2
. The image matrix is created from an array of 1024
2
for the OBI system. The user can adjust the source to
2
. The high contrast spatial resolutions of the two
4.3.2 CBCT
4.3.2.1 kV-CBCT
For modern IGRT systems, gantry-mounted kV imaging is an important component. Elekta’s XVI and Varian’s OBI provide imaging at the same isocenter of the
linear accelerator. With kV-CBCT, volumetric imaging is available for therapists to
align patient to the reference CT simulation image before each fraction. This allows
the localization of patient anatomy at each treatment fraction and improves the
accuracy of treatment delivery.
The developments in flat-panel detectors made gantry-mounted kV-CBCT
possible. The a-Si flat-panel detector used in gantry-mounted kV imaging systems
is made up of a CsI plastic scintillator which converts the x-ray photons into visible
light which is then detected by a-Si thin film transistor (TFT) and read out as an
electrical signal [60]. This indirect detector is ideal for the orthogonal configuration
proposed by Jaffray et al [61] due to its compact size and potential for high
resolution imaging. Cone beam reconstruction was applied to reconstruct a
volumetric image from the flat panel. Compared to multi-slice detectors in fan
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Artificial Intelligence in Adaptive Radiation Therapy
beam CTs, the a-Si flat panel has a lower x-ray cross section, higher noise, longer
readout, and longer time to reset. These properties are important factors to consider
when it is used as image guidance for adaptive radiation therapy. In addition, the
on-board CBCT’s FOV is limited by the size of the flat-panel detector. Cone beam
reconstruction only requires a 180° rotation under full-fan mode to acquire a
sonogram for a volumetric image. A half fan technique was proposed to increase the
FOV to up to 50 cm. The detector and the kV source are offset in this configuration.
A 360° rotation is required to acquire two separate sonograms that are stitched
together for a full view of the patient.
Accurate characterization of the HU is crucial for CBCT used in adaptive therapy
settings. Calibration of the flat panel is thus essential for an adaptive program. The
pixel array of the flat-panel detector is made of groups of a-Si units. Each pixel has
different calibration curve. Dark field calibration is used to characterize the dark
signal of the pixel. Flood field calibration is used to characterize the slope of the
calibration curve. Individual calibration curves are applied to each pixel so that
the response of the flat panel remains uniform despite variances within readout
electronics. However, cone beam geometry introduces non-uniformity in HU characterization for CBCT. The x-ray scatter is more pronounced at the center of the
CBCT image for large FOV scans. This leads to cupping artifacts and introduces nonuniformity into the volumetric image. CBCT is also susceptible to motion artifacts.
Since the on-board imager takes around 60s to complete the acquisition, CBCT is
more susceptible to motion artifacts compared to fan beam CT. Metal artifacts in
CBCT are also more pronounced due to the lack of metal artifact reduction common
in diagnostic CT. These artifacts contribute to uncertainties in HU characterization
and should be noted for CBCT-based adaptive therapy programs.
4.3.2.2 Hypersight kV-CBCT
The new Hypersight kV-CBCT system integrated in the Halcyon/Ethos ring style
linac was introduced recently. The new system applied iterative CBCT image
reconstruction (iCBCT) techniques to reconstruct the volumetric image.
Compared to the filter back projection-based Feldkamp–Davis–Kress (FDK)
algorithm, iCBCT exhibited increased accuracy in CT number accuracy and reduced
noise. In addition, a larger kV imaging panel (86 × 43 cm
2
versus 43 × 43 cm2) made
of cesium iodide scintillator (higher efficiency) was added. With the larger panel, the
imager operates in full-fan mode. The detector with a high frame rate also enabled a
faster scan time down to 5.9 s. A new anti-scatter grid was introduced. A more
robust scatter correction algorithm incorporating Acuros CTS based scatter model
and Monte Carlo based hardware scatter was incorporated in the iCBCT software
[62]. It also incorporated an extended FOV (eFOV) up to 70 cm and metal artifact
reduction (MAR).
The new CBCT addressed many existing concerns with CBCT regarding scatter
correction, metal artifacts, and motion artifacts for image quality. For adaptive
therapy purposes, the CBCT system generated encouraging results. The new imaging
system achieved more realistic CT numbers under the CBCT mode. In a recent study
by Bogowicz et al [63], the CBCT-based planning dose distributions showed an
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Artificial Intelligence in Adaptive Radiation Therapy
agreement above 97% and 93% for gamma analysis with criteria of 3%/1 mm and 2%/
1 mm, respectively, compared with the simulation CT dose distribution. All
dose–volume histogram (DVH) differences between CT and CBCT were below 2%.
The CBCT images provided by the Hypersight kV-CBCT system would be
suitable for online adaptive radiotherapy workflow.
4.3.3 CT-on-rail and CT-linac
In modern radiotherapy, the minimization of treatment margins holds significant
importance as these margins directly correlate with excessive tissue toxicity and
impose constraints on the dose escalation necessary for improved tumor control.
Enhancing the accuracy of daily patient set-up and tumor localization represents a
pivotal approach to address this challenge. The integration of in-room CT scanners
has significantly contributed to the implementation of imaging-guided techniques
[64]. Siemens introduced the PRIMATOM system, featuring a fixed couch and
diagnostic CT scanners mounted on rails, commonly referred to as ‘CT-on-rail’
systems. In this set-up, the linac gantry and CT gantry can be positioned on opposite
ends of the treatment couch. Pre-treatment 3D CT localization of the tumor is
achieved by rotating the couch 180°. The reported positional accuracy of the couch
in CT-on-rail systems is within 0.4 mm in all three directions [65]. Systematic
investigations have been conducted to assess the mechanical uncertainties of CT-onrail systems [66]. Given the superior image quality and the ability to visualize realtime anatomy while the patient is immobilized, CT-on-rail systems have found
extensive application in clinical studies, particularly in prostate cancer treatment.
Various institutions have reported the feasibility and dosimetric advantages of CTon-rail for daily prostate alignment [67–69]. Moreover, the system has demonstrated
utility in other treatment sites characterized by significant target position variability,
such as the lungs and liver [70, 71].
Another innovative solution incorporating in-room CT scanning is the CT-linac
system, which integrates a C-arm linear accelerator with a CT scanner coaxially
attached behind the linear accelerator. Unlike the CT-on-rail approach, the CTlinac system eliminates the need for 180 ° couch rotation, thereby reducing the
uncertainties associated with rotation. Investigations have demonstrated that the
CT-linac system offers clinically acceptable plan quality and dose delivery efficiency
[72–74].
4.3.4 MR-linac
The MR-linac represents one of the latest developments of image-guided adaptive
radiotherapy. It utilizes real-time on-board MRI during treatment sessions to guide
and adapt the delivery of radiation. A unique advantage of MR-guided radiotherapy (MRgRT) is the superior visualization of tumors and soft tissues provided
by the real-time MRI [75]. The crispy target visualization and online adaptive
platform offered by MR-linac enables precise targeting while minimizing radiation
exposure to healthy tissues, leading to improved therapeutic outcomes. In addition,
MRgRT has the advantage of real-time imaging while the treatment beam is on,
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Artificial Intelligence in Adaptive Radiation Therapy
enabling real-time target motion monitoring and tracking, further improving the
precision of radiation treatment. The currently most popular MR-linac systems are
summarized below. Details of the MRgRT can be found in chapter 16.
4.3.4.1 Low field MR-linac
The ViewRay MRIdian system is a representative of low field MR-linac systems.
The ViewRay system integrates a 0.35 tesla low field MRI scanner with a linear
accelerator for MRI-guided treatment. This system is the first MR-linac device used
for clinical MRgRT and received Food and Drug Administration (FDA) approval
in 2017 [5]. The MRIdian system consists of a split-bore superconducting magnet
with a bore diameter of 70 cm perpendicular to a 6 MV FFF linear accelerator
system. The linear accelerator produces coplanar static intensity-modulated radiation therapy (IMRT) fields and delivers dose at 650 MU/min with a 0.5 revolutions
per minute (rpm) gantry rotation. The MRI system uses a balanced steady state free
precession pulse sequence for MR imaging that can be used for treatment planning
and set-up verification. The MRIdian system also allows for real-time imaging
during treatment beam-on, enabling intrafraction motion monitoring during treatment. This feature allows for real-time tumor tracking and automatic beam gating
based on user defined gating boundaries on a sagittal cine image [76]. Another low
field MR-linac system is the MagnetTx Aurora-RT, which is a 0.5 tesla MR-linac
utilizing an open bore in-line design to mitigate the electron return effect [77]. The
Aurora-RT received FDA premarket clearance in 2022 and treated its first patient in
2023.
4.3.4.2 High field MR-linac
The Elekta Unity system (Elekta AB, Stockholm, Sweden) is the first high field MRlinac that integrates a 7 MV FFF linac system and a 1.5 tesla Philips (Philips
Healthcare, Best, the Netherlands) MRI system [78]. The Unity system is designed
as a bore-type machine with a linac system rotating around the MRI system, which
has an inner bore diameter of 70 cm [79]. The radiation beam is perpendicular to the
magnetic field orientation. The treatment couch moves in the longitudinal direction
only; however, during treatment the couch is not designed to move to adjust
treatment isocenter location. Instead, online adaptive planning is utilized to account
for isocenter shifts. The system offers an integrated online adaptive planning
workflow and every fraction treated with Elekta Unity requires an online adaptive
planning. The system also allows for simultaneous MR imagining and treatment
delivery. In its first version, the Unity system offered only tumor motion monitoring
[80]. In late 2023, Elekta released the comprehensive motion management (CMM)
system, allowing for different levels of motion management for better control of
respiratory motion and other motion uncertainty during treatment delivery [81]. It is
also worth noting that due to the high strength of the magnetic field, the beam profile
is inherently off-center and asymmetric, and the electron return effect can cause
electrons to change trajectory to ‘return’ to a higher density material at the interface
of exiting a higher density material into a lower density material [82].
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Artificial Intelligence in Adaptive Radiation Therapy
4.3.5 PET-linac
The advent of PET-linac systems, exemplified by the Reflexion system, has
introduced the concept of biology-guided radiotherapy (BgRT) [83]. Integrating
both kV CT and PET scanners within a ring gantry linear accelerator, the Reflexion
system enables real-time beamlet conformation based on PET signals acquired from
the tumor, with sub-second latency. This swift response capability allows the system
to manage patient motion without the need for motion trackers or breath-holding
techniques. Moreover, PET images generated by the Reflexion system exhibit
comparability to those obtained from traditional PET/CT scanners [84]. PET
images hold significant potential in the realm of biologically adaptive radiation
therapy (BART), which integrates tumor or OAR function into adaptive planning
strategies. Prior trials have demonstrated the advantages of intra-treatment FDG
and hypoxia PET images in facilitating dose escalation and enhancing local tumor
control [85, 86]. The evolution of PET-linac technology is expected to facilitate
clinical trials aimed at investigating the efficacy of daily PET-guided plan
adaptation.
4.4 Imaging for motion management
4.4.1 ExacTrac
Novalis ExacTrac system is an x-ray system designed for stereotactic radiosurgery
(SRS) and stereotactic body radiotherapy (SBRT). The system consists of two
components: a real-time infrared (IR) tracking system and a kV imaging system. The
IR system detects motion in real time using the IR reflecting markers. These markers
can be placed on the reference frame mounted on the treatment couch, or on the
patient’s skin. The camera combined with the IR system confirms patient positioning and monitors patient movement. The x-ray system has two x-ray tubes installed
in the floor and two flat-panel detectors mounted on the ceiling. The system is
mounted obliquely to the mid-sagittal plane of the accelerator. The x-ray system
acquires projection images and localizes the patient by registering bony landmarks
or implanted markers with the corresponding digitally reconstructed radiograph
(DRR) from the planning CT.
4.4.2 Varian triggered imaging
Triggered imaging is a kV imaging technique included in the Varian advanced
imaging package. Triggered imaging uses the OBI of the Varian Truebeam to
monitor high contrast regions during treatment delivery. The high contrast regions
are typically implanted fiducial markers, endogenous patient anatomy (vertebral
body and spinous process), or endogenous orthopedic hardware [87]. The kV images
are typically taken with an intrafraction motion review application at specific gantry
intervals to ensure accurate delivery. The system is capable of auto beam hold
(ABH), pausing the beam delivery when the tracking region falls outside of
tolerance. Varian triggered imaging enables intrafraction IGRT. There have been
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