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Artificial Intelligence in Adaptive Radiation Therapy
successful applications in radiotherapy, such as generating ‘synthetic CT’ from MRI
for radiation treatment planning without paired MRI and CT images for model
training [91–93]. In computer vision, generative models have demonstrated superior
performance in modeling and synthesizing human motions [94, 95]. For motion
monitoring during ART delivery, generative models may facilitate the learning of
motion priors from population image datasets. To focus on modeling motioninduced anatomical variations, targets and OAR volumes need to be aligned to the
same coordinate system through group image registration. The large variances in
individual anatomical configurations and imaging positions, however, will challenge
the accuracy of group image registration, thus potentially degrading the model
quality. Effective learning of motion priors without well-aligned training samples
has the potential of simplifying training data collection and preprocessing, improving model accuracy, and guiding ART delivery with high-quality motion information, especially for non-cyclic motions, such as organ fillings, where patient-specific
motion modeling may be less effective.
11.4.3 Uncertainty quantification for AI models
While AI models have demonstrated significant potential in supporting ART
delivery with improved treatment precision, reliable clinical implementation cannot
be achieved without a method to quantify the uncertainty of model outputs.
Although ad hoc model validation can be done by comparing model outputs to
the ground truth, such ground truth is only available after the delivery of the
radiation dose. To ensure patient safety and give clinicians confidence in trusting
model outputs, instantaneous feedback on model quality is important for AI models
developed for ART delivery.
Confidence intervals have been established for certain statistical machine learning
models as a quantitative measure of model output quality. Ginn et al investigated
confidence interval estimation for a kernel regression model developed for respiratory motion prediction [96]. The estimator consists of three components, including
the goodness of model fit, the robustness of prediction quantified through leave-oneout cross-validation, and the velocity of the target. The estimator was evaluated for
20 abdominal subjects imaged with 2D cine MRI for respiratory motion monitoring.
An increase of gating accuracy of –2% was reported by overriding the gating
decision when the confidence interval estimator suggested a prediction error.
Gaussian models have also been utilized to infer target position or motion fields,
from surrogate markers or sparse imaging data. The uncertainty was then quantified
through the covariance matrix of the linear Gaussian system [97, 98]. As illustrated
in figure 11.11, changes of motion patterns can be detected and unreliable motion
estimation can be rejected when the associated estimation uncertainty exceeds a
certain threshold.
Bayesian networks have been investigated to output both prediction values and
prediction uncertainties. Instead of predicting deterministic values, Bayesian networks predict probabilistic distributions based on network weights and inputs, thus
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 11.11. Unreliable motion estimations were rejected based on the estimation uncertainty when motion
pattern changes, as indicated by the changing patterns in the center of mass-coordinates. (Reproduced from
[
98]. CC BY 4.0.)
permitting uncertainty quantification based on the probabilistic distribution. Given
a set of initial clinical information, the network outputs the probability of obtaining
certain radiotherapy parameters. A low probability therefore corresponds to
potential errors in the radiotherapy plan. Law et al applied a Bayesian network to
quantify the uncertainty of synthetic CT generated from MRI for radiation treatment planning [99]. The network outputs were computed using Monte Carlo
integration. The synthetic CT values were estimated as the expected values of 100
simulation outputs, and the associated estimation uncertainty was characterized by
the Monte Carlo simulation output spread. Uncertainty estimation for image
reconstruction has also been investigated using a Markov chain Monte Carlo
method, where both the image voxel values, and their associated uncertainties,
were calculated from under-sampled MRI data [ 100]. Future work on uncertainty
quantification for motion modeling, in particular for high-dimensional motion
modeling such as DVF estimation and prediction, will facilitate clinical translation
of AI models to support ART delivery.
11.4.4 Biology-guided ART delivery
Biological imaging modalities such as positron emission tomography (PET) provide
tumor functional information that may be used to guide radiotherapy plan and
delivery. Several clinical trials are investigating the benefits of biology-guided ART
planning, where images providing biological information of the target [101–103] are
acquired in the middle of a treatment course for offline plan adaptation. Target
motion monitoring using PET has also been investigated through phantom studies
[104]. The method combines external surrogate signals with PET signals to
reconstruct gated PET images. The target centroid was estimated from the
segmented target volume on the gated PET images. The author reported an
averaged centroid position estimation error of 1.6 mm for both 1D and 3D motion
patterns. The error decreased with time as more coincidence events were accumulated and converged in approximately 90 s.
The advent of PET/CT-linac systems has also permitted PET-guided ART delivery.
This technology utilizes CT images for patient set-up and PET detection of outgoing
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 11.12. (a) Layout of the PET arcs in the gantry. (b) Phantom image acquired using the PET imaging
subsystem of the PET-linac system. (Reproduced with permission from [
Authors. Published by the British Institute of Radiology.)
105]. Copyright 2022, 2023 The
tumor emissions for target motion monitoring during treatment delivery [39].
The motion information is used to guide the system to conform the MV radiation
beamlets to the moving target. Preliminary investigations have characterized the
imaging quality and tracking accuracy of the PET/CT-linac system [105, 106]. As
shown in figure 11.12, a phantom study using the PET imaging subsystem demon-
strated comparable spatial resolution and image contrast to diagnostic imaging
systems, while the sensitivity and count rate were lower due to the smaller detector
area. The tracking accuracy was also characterized through phantom studies with
varying levels of PET biodistributions [106]. With good contrast between target and
background PET signals (8:1 in PET biodistributions), the tracked dosimetry showed
a prescription covering the target of 100%, while for the case with poor contrast (2:1 in
PET biodistributions), the prescription covering the target dropped to 88%. In these
low contrast cases, the system also flagged warnings based on the low activity
concentration within the target.
The accuracy of motion monitoring during PET-guided ART delivery critically
depends on PET image quality. To reduce patient motion and imaging dose, it is
desirable to lower the injection dose and shorten the PET scan time. However, the
low injection dose and the short scan time can lead to image quality degradation,
exacerbated by hardware limitations such as smaller detectors that have lower count
rates and sensitivity. AI models have demonstrated promising performance in lowdose PET reconstruction. For diagnostic PET reconstruction, generative adversarial
networks have been explored to enhance the quality of low-dose PET images [107].
The network was trained using population-based datasets containing both low-dose
and high-dose PET images acquired from a patient population. Lim et al integrated
neural network-learned priors of high-dose PET images with a physical model of the
PET imaging system to improve low-dose PET reconstruction [108]. The network
served as a data regularization term during the iterative image reconstruction
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 11.13. Transverse slices from full-dose PET images of different treatment fractions along with the
corresponding low-dose images and the images predicted by the U-Net and the teacher–student U-Net for one
example patient. (Reproduced from [
109]. Courtesy of Cornell University.)
process by penalizing deviations of model estimation from network-learned priors.
The method demonstrated good generalizability across different datasets, where the
network was trained using PET images of a sphere phantom and tested using PET
images of an anthropomorphic torso phantom.
For the image reconstruction of the PET-Linac system, Fu et al proposed a
patient-specific deep learning model that enhances low-dose PET images using highdose PET images of the same patient [109]. The model comprises a teacher U-net
and a student U-net. The training dataset was created by subsampling coincidence
events to generate various low-dose PET images from high-dose acquisitions. The
parameters of the student network were optimized to output high-dose PET from
low-dose inputs, while the parameters of the teacher network remained constant as
the moving-average values of the student network parameters. Consistency loss
between the teacher and student network was also incorporated during network
training. As shown in figure 11.13, the teacher–student model demonstrated
improved image quality compared to a single U-Net model. As more studies are
conducted on PET-linac systems, enhancing PET imaging through AI will enable
the full exploitation of the advantages of biology-guided ART delivery, with the
potential to improve treatment outcomes.
11.5 Summary
While significant advances in adapting treatments have begun to be implemented
clinically, they are inherently limited by the motions that occur during treatment. AI
has demonstrated the potential to improve treatment monitoring. Simpler AI
methods are in clinical use to aid motion tracking, and more advanced methods
have shown the potential to dramatically improve the efficiency and accuracy of
measuring, predicting, and reacting to changes that occur as treatment is being
delivered.
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Artificial Intelligence in Adaptive Radiation Therapy
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