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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
difference 2.3 ± 0.1%
difference < 1%
0.6%
Artificial Intelligence in Adaptive Radiation Therapy
0.27%
± 0.79%
difference
< 1% for both photon
N/A
and proton plans
difference
< 1% (proton plan)
Gamma passing rate: >
+
95% at (1%, 1 mm)
for photon plan,
(Continued)
> 90% at (2%, 2 mm)
for
proton plan
0.5% (proton plan)
MAE (HU): 47.2 ± 11.0 Mean DVH metrics
validation/18% testing
(brain)
MAE (HU): 55.7 ± 9.4
Brain: 24, leave-one-out cross
Brain: T1w
validation
Pelvis: T2w
50.8 ± 15.5 (pelvis)
validation
Pelvis: 20, leave-one-out cross
MAE (HU): 34.1 ± 7.5 PTV V95% difference <
14, 25/11
MAE (HU): 72.87 ± 18.16 Mean DVH metrics
validation
MAE (HU): 51.32 ± 16.91 Mean DVH metrics
validation
MAE (HU): (82, 147)
testing
Gupta et al [74] U-Net 3 T in-phase Dixon T1w Brain: 47 training/13 testing MAE (HU): 17.6 ± 3.4 Mean target dose
Largent et al [28] GAN 3 T T2w Pelvis: 39, training/testing: 25/14, 25/
Kazemifar et al [85] GAN 1.5 T post-gadolinium T1w Brain: 77, 70% training/12%
Lei et al [39] CycleGAN
Liu et al [89] U-Net 1.5 T T1w Brain: 30 training/10 testing MAE (HU): 75 ± 23 PTV V95% difference
Liu et al [40, 42] CycleGAN 3 T/1.5 T T1w Liver: 21, leave-one-out cross
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Liu et al [42] CycleGAN 1.5 T T2w Pelvis: 17, leave-one-out cross
Neppl et al [22] U-Net 1.5 T T1w Brain: 57 training/28 validation/4
CycleGAN 1.5 T T1w Brain: 50 MAE (HU): 54.55 ± 6.81 PTV D95 difference <
[90]
Olberg et al [44] GAN 0.35 T T1w Breast: 48 training/12 testing MAE (HU): 16.1 ± 3.5 PTV D95 difference <1%Shafai-Erfani et al
Key findings in
Key findings in image
dosimetry
quality
Artificial Intelligence in Adaptive Radiation Therapy
difference in
< 1%
difference
< 1%
MAE (HU): 75.7 ± 14.6 N/A
target = 1.3%
MAE (HU): 48.5 ± 6 Maximum dose
Table 8.3. (Continued )
Site, and # of patients in training/
testing
Author, year Network MR parameters
Wang et al [91] U-Net 1.5 T T2w Head and neck: 23 training/10 testing MAE (HU): 131 ± 24 N/A
Florkow et al [82] U-Net 3 T T1w Dixon Pelvis: 27, 3-fold cross validation MAE (HU): (33, 40) N/A
Koike et al [92] GAN T1w + T2w + FLAIR Brain: 15 MAE (HU): 108.1 ± 24.0 DVH metrics difference
Head and neck: 30 training/15 testing MAE (HU): 69.98 ± 12.02 Mean average dose
Qi et al [81] GAN T1w + T2w + contrast-
enhanced T1w + contrast-
enhanced T1w Dixon water
testing from one scanner
validation
Pelvis: 11 training from two scanner/8
Head and neck: 32, 8-fold cross
contrast T1w + T2w
scanners
Numbers in parentheses indicate minimum and maximum values.
N/A: not available, i.e. not explicitly indicated in the publication.
AE: Autoencoder.
*
Tie et al [83] GAN 1.5 T pre-contrast T1w + post-
Brou Boni et al [93] GAN 1.5 T and 3 T T2w from three
+
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Artificial Intelligence in Adaptive Radiation Therapy
Table 8.4. Summary of studies on MR-based synthetic CT for PET attenuation correction. (Adapted from [1].
CC BY 4.0.)
Site, and # of patients in
Author, year NetworkMRparameters
training/testing Key findings in PET quality
Gong et al [84] U-Net Dixon and
ZTE
Jang et al [18] U-Net 3 T UTE Brain: 30 pre-training/6
Leynes et al [24] U-Net 3 T Dixon and
ZTE
Liu et al [17] U-Net 1.5 T T1w Brain: 30 training/10
Spuhler et al [27] U-Net 1.5 T T1w Brain: 44 training/11
Torrado-Carvajal
et al [23]
Blanc-Durand et al
[79]
Ladefoged et al [80] U-Net UTE Brain: 79 (pediatric), 4-
Arabi et al [94] GAN 3 T T1w Brain: 40, 2-fold cross
U-Net Dixon-VIBE Pelvis: 28 pairs from 19
U-Net ZTE Brain: 23 training/47
Brain: 14, leave-two-out Absolute bias < 3% among 8
training/8 testing
Pelvis: 26, 10 training/16
testing
testing
validation/11 testing
patients, 4-fold cross
validation
testing
fold cross validation
validation
VOIs
Bias (%): −0.8 ± 0.8–1.1 ±
1.3 among 23 VOIs
RMSE (%): 2.68 among 30
bone lesions, 4.07 among
60 soft-tissue lesions
Bias (%): −3.2 ± 1.3–0.4 ±
0.8
Global bias (%): −0.49 ± 1.7
for 11C-WAY-100 635–
1.52 ± 0.73 for 11CDASB
Bias (%): 0.27 ± 2.59 for fat
−0.03 ± 2.98 for soft tissue
−0.95 ± 5.09 for bone
Bias (%): −1.8 ± 1.9–1.7 ±
2.6 among 70 VOIs
Bias (%):
Absolute bias < 4% among
−0.2–0.5 in 95%
CI
63 VOIs
In the majority of the studies, the MAE of the synthetic CT within the patient’s
body typically falls within the range of 40–70 HU. Some reported results even
approach the uncertainties observed in standard CT simulation. Specifically, several
studies highlight MAEs for soft tissue that are less than 40 HU [21, 28, 30, 72–75],
demonstrating relatively accurate intensity mapping in this region. However, due to
the indistinguishable contrast of bone or air on MR images, the MAE for these
tissues tends to exceed 100 HU, indicating higher discrepancies. Misalignment
between CT and MR images in patient datasets emerges as a common source of
error. This misalignment, particularly on bone structures, not only contributes to
intensity mapping errors during training but also results in an overestimation of
error during evaluation. This is because the error from misalignment registers as
synthetic error in the assessment metrics. Notably, two studies reported significantly
higher MAE for the rectum (∼70 HU) compared to other soft tissues [28, 76]. This
discrepancy may be attributed to mismatches in CT and MR imaging, potentially
arising from variable filling of the rectum. Considering that the number of bone
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Artificial Intelligence in Adaptive Radiation Therapy
pixels is considerably fewer than those of soft tissue, the training process may tend to
map pixels to the low HU region during the prediction stage. Potential solutions to
address these challenges could include assigning higher loss weights on bone
structures or incorporating bone-only images during the training process [21].
In multiple studies, learning-based methods consistently outperform conventional
methods, showcasing superior accuracy in generating synthetic CTs [16, 29, 73, 76].
This highlights the advantage of adopting a data-driven approach over traditional
model-based methods. For instance, synthetic CTs generated by atlas-based
methods were observed to be more susceptible to noise and registration errors,
resulting in significantly greater MAE compared to learning-based methods. Despite
the advantages of learning-based methods, there are limitations to consider. The
performance of these methods can be unpredictable when applied to datasets that
significantly differ from the training sets. Such differences may stem from unusual or
abnormal anatomy, or images with degraded quality due to severe artifacts and
noise. In contrast, atlas-based methods generate a weighted average of templates
derived from prior knowledge. This characteristic makes them less prone to failure
in handling unexpected or unusual cases, contributing to their robustness in
scenarios with significant variations in image quality [76].
The diverse datasets, training approaches, and testing strategies employed across
these studies make the direct comparison of results challenging, precluding the
determination of a single best methodology for all applications. However, some
studies have conducted comparisons with competing methods using the same
datasets, shedding light on relative advantages and limitations. For example, in a
study involving fifteen brain cancer patients, a GAN-based method demonstrated
better preservation of detail and closer similarity to real CT with less noise when
compared to an autoencoder-based method [30]. The GAN-based synthetic CT
exhibited higher accuracy at the bone–air interface and in determining fine
structures, with approximately 10HU less error by MAE. Another study comparing
U-Net and GAN with different loss functions on 39 patients with prostate cancer
revealed quantitative results indicating that U-Net methods had significantly higher
MAE than their GAN counterparts. Interestingly, the perceptual loss in both U-Net
and GAN did not contribute to reducing MAE or provide benefits for dose
calculation accuracy [28]. A comparison between CycleGAN and GAN-based
methods on patients with brain and prostate cancer demonstrated a significant
improvement in MAE with CycleGAN. CycleGAN also exhibited better visual
results in terms of fine structural detail and contrast. Notably, CycleGAN results
were less sensitive to local mismatches in the training CT/MR pairs, resulting in less
blurry bone boundaries compared to GAN results [39]. Similar comparison results
were reported in a study comparing CycleGAN and GAN on liver stereotactic body
radiation therapy (SBRT) cases. While dosimetry comparison showed minimal
difference, attributed to the insensitivity of volumetric modulated arc therapy
(VMAT) plans to HU inaccuracy, CycleGAN exhibited improved MAE and visual
results over GAN [40].
Among the reviewed studies, various MR sequences have been employed for
synthetic CT generation, with the choice often dictated by their availability.
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Artificial Intelligence in Adaptive Radiation Therapy
The optimal sequence yielding the best performance has not been conclusively
determined. T1-weighted and T2-weighted sequences, being two of the most
common general diagnostic MR sequences, are widely used due to their availability.
These sequences enable models to be trained on relatively large datasets containing
co-registered CT and T1- or T2-weighted MR images. T2-weighted images may be
preferable to T1-weighted ones due to their intrinsically superior geometric accuracy
within regions of significant anatomic variability, such as the nasal cavity, and
reduced chemical shift artifacts at fat and tissue boundaries. However, both T1- and
T2-weighted MR images lack contrast for air and bone, which can impede the
extraction of features corresponding to these structures in learning-based methods.
The two-point Dixon sequence, capable of separating water and fat, has been
utilized in commercial PET/MR applications for segmentation [77, 78]. However, its
limitation lies in poor bone contrast, resulting in the misclassification of bone as fat.
To enhance bone contrast and facilitate feature extraction in learning-based
methods, ultrashort echo time (UTE) and/or zero echo time (ZTE) MR sequences
have been employed recently to generate positive image contrast from bone [17].
While studies by Ladefoged et al and Blanc-Durand et al demonstrated the
feasibility of UTE and ZTE MR sequences using U-Net in PET/MR attenuation
correction, respectively [79, 80], a direct comparison with conventional MR
sequences under the same deep learning network is lacking. Therefore, the
advantage of these specialized sequences has not been conclusively validated.
Moreover, compared with conventional T1- or T2-weighted MR images, UTE/
ZTE MR images may have limited diagnostic value for soft tissue and longer
acquisition times. This may potentially reduce their clinical utility, particularly in
poorly tolerated, long-duration exams such as whole-body PET/MR.
Several studies have explored the use of multiple MR images with varying
contrasts as training inputs to enhance the overall predictive power and accuracy of
synthetic CT generation. Qi et al proposed a four-channel input comprising T1, T2,
contrast-enhanced T1, and contrast-enhanced T1 Dixon water images. The results
from the four-channel input demonstrated lower mean absolute error (MAE)
compared to results from fewer channels, highlighting the potential benefits of
incorporating diverse contrast information [81]. Florkow et al investigated singleand multi-channel inputs using magnitude MR images and Dixon-reconstructed
water and fat images obtained from a single T1 multi-echo gradient-echo acquisition
[82]. Their findings indicated that multi-channel input can improve synthetic CT
generation over single-channel input, with the Dixon sequence input outperforming
other configurations. Tie et al employed T2 and pre- and post-contrast T1 MR
images in a multi-channel, multi-path architecture, demonstrating additional
improvement over multi-channel single-path and single-channel results [83].
Combining UTE or ZTE sequences with Dixon sequences, which provide contrast
for bone against air and fat against soft tissue, respectively, has been considered an
attractive combination [ 24, 84]. Leynes et al showed that synthetic CT using both
ZTE and Dixon MR sequences has less error than using Dixon alone, showcasing
the potential benefits of combining these contrast sources [24]. While the resulting
improvement in image quality has been validated, the necessity of performing
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Artificial Intelligence in Adaptive Radiation Therapy
additional MR sequences for synthetic CT generation requires further study in
specific applications to justify the associated costs and acquisition time.
In the reviewed studies, CT and MR images in the training datasets were acquired
separately on different machines, necessitating image registration between the CT
and MR images to create CT-MR pairs for training. The registration error is
generally minimal at the level of the brain but may be more significant within the
pelvis, owing to variable bladder and rectum filling, and in the abdomen, due to
variations introduced by respiratory motion and peristalsis. Methods such as U-Net
and GAN-based approaches can be susceptible to registration errors, particularly
when utilizing a pixel-to-pixel loss function. These errors can be exacerbated by
physiological motion, making accurate registration challenging. To address this
issue, Kazemifar et al proposed a potential solution using mutual information as the
loss function in the GAN generator. This approach aims to bypass the registration
step during training, potentially mitigating the impact of registration errors on the
performance of the model [85]. CycleGAN-based methods, developed for unpaired
image-to-image translation, exhibit greater robustness to registration errors. This is
attributed to the role of the cycle consistency loss, which enforces structural
consistency between the original and cycle-generated images. For instance, in the
context of synthetic CT generation from MR images, the cycle consistency loss
ensures that a cycle MRI generated from synthetic CT remains similar to the
original MRI. This characteristic makes CycleGAN-based methods more resilient to
registration errors, contributing to their effectiveness in scenarios where accurate
image registration is challenging [19, 33, 35, 86].
8.4.2 Dose calculation in MR-only radiation therapy
In studies with applications in radiation therapy, many have evaluated the
dosimetric accuracy of synthetic CT by calculating the radiation treatment dose
from the original treatment plan and comparing it against ground truth CT
simulation imaging. It has been observed that the dose difference is approximately
1%, which is relatively small compared to typical total dose delivery uncertainties
over an entire treatment course (5%). For reference, in the bulk-density assignment
method, Lee et al observed that the differences between the dose of CRT plans on
bulk-density, when compared to CT, were less than 2% [3]. Similarly, Jonsson et al
reported a comparable result, noting that the maximum difference in monitor units
(MU) required to reach the prescribed dose was 1.6% [4]. The improvement in
dosimetric accuracy provided by deep learning-based methods in radiation therapy,
when compared to image accuracy, is relatively small and may lack clinical
relevance [73, 76]. One potential reason for this phenomenon is that dose calculation
on photon plans tends to be forgiving to image inaccuracy, particularly within
homogeneous regions such as the brain. In VMAT, the contribution to dosimetric
error from random image inaccuracy also tends to cancel out within an arc.
However, the small dosimetric improvement observed may be of significance in
scenarios such as stereotactic radiosurgery (SRS) and stereotactic body radiation
therapy (SBRT), where small volumes are treated to very high doses. In such cases,
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Artificial Intelligence in Adaptive Radiation Therapy
significant dosimetric errors may arise from otherwise negligible errors in CT
synthesis, particularly in the region surrounding the target volume [95]. These
findings underscore the importance of considering the clinical context and the
specific treatment scenario when assessing the impact of synthetic CT accuracy on
dosimetry in radiation therapy applications.
Studies have also assessed the use of synthetic CT in the context of proton therapy
for various cancers, including prostate, liver, and brain cancer [41, 42, 90]. Proton
beams, unlike photon beams, exhibit a sharp dose gradient (Bragg peak) at the distal
end of the beam, allowing for highly conformal dose delivery to the target by
superimposing proton beams from several angles. Any inaccuracies in HU along the
beam path on the planning CT can lead to a shift in the highly conformal high-dose
area. This shift may result in the tumor being substantially under-dosed or the
organs at risk being over-dosed [96]. In studies such as the one by Liu et al most of
the dose differences resulting from the use of synthetic CT were observed at the distal
end of the proton beam [42]. Liu et al reported that the largest and mean absolute
range differences were 0.56 and 0.19 cm among their 21 liver cancer patients, and
0.75 and 0.23 cm among 17 prostate cancer patients, respectively [41, 42]. These
findings emphasize the critical importance of accurate synthetic CT generation in
proton therapy, where precision in dose delivery is crucial due to the unique
characteristics of proton beams.
In addition to dosimetric accuracy for treatment planning, the evaluation of
synthetic CT imaging must also consider geometric fidelity for treatment set-up.
However, studies specifically focusing on synthetic CT positioning accuracy are
limited. Fu et al conducted patient alignment testing by rigidly aligning synthetic CT
and real CT to the CBCT acquired during the delivery of the first fraction of a
fractionated radiotherapy treatment course [21]. The average translation vector
distance and absolute Euler angle difference between the two alignments were found
to be less than 0.6 mm and 0.5°, respectively. Gupta et al performed a similar study
and reported that the translation difference was less than 0.7 mm in one direction
[74]. Although studies have addressed alignment with CBCT, the alignment between
the digitally reconstructed radiograph (DRR) derived from the synthetic CT and onboard kilovolt (kV) imaging of the patient is also clinically important. However, no
studies on DRR alignment accuracy were found in the reviewed literature. It is
worth noting that the geometric accuracy of synthetic CT is influenced not only by
the synthetic methods employed but also by the geometric distortion on MR images
caused by magnetic field inhomogeneity, as well as subject-induced susceptibility
and chemical shift. Therefore, methods to mitigate MR distortion are crucial for
improving synthetic CT accuracy in patient positioning, contributing to the overall
success of radiotherapy treatment set-up.
8.4.3 PET attenuation correction
In studies focused on PET attenuation correction, the evaluation has primarily
centered around the bias introduced in PET quantification due to synthetic CT
errors. While it is challenging to define a specific error tolerance that significantly
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Artificial Intelligence in Adaptive Radiation Therapy
impacts clinical decision-making, a general consensus is that quantitative errors of
10% or less typically do not have a substantial impact on decisions in diagnostic
imaging [15]. A thresholding on MR images of UTE sequences resulted into an
average error of 5% in brain PET images [12]. Kops and Herzog demonstrated that
the segmentation-based and the registration-based methods proposed by them have
similar performance in PET image reconstruction [14]. A thresholding on MR
images of UTE sequences resulted in an average error of 5% in brain PET images
[12]. Kops and Herzog demonstrated that the segmentation-based and the registration-based methods proposed by them have similar performance in PET image
reconstruction [14]. Most of the proposed deep learning methods in the reviewed
studies met this criterion based on the average relative bias reported. However, it is
essential to note that due to variation among study subjects, the bias in some
volumes-of-interest (VOIs) may exceed 10% for certain patients [24, 79]. This
emphasizes the importance of considering both the mean and standard deviation
of the bias when interpreting results, as proposed methods may exhibit poor local
performance affecting specific patients. Reporting alternative results that list or plot
all data points, or at least their range, could provide a more comprehensive
understanding of the proposed methods’ performance.
Bone accuracy on synthetic CT is crucial for PET attenuation correction since
bone has the highest capacity for attenuation due to its high density and atomic
number. Unlike applications in radiation therapy, the bias and geometric accuracy
of bone on synthetic CT are more frequently evaluated for PET attenuation
correction. Several studies have demonstrated that improved accuracy of bone
representation in CT synthesis leads to more globally accurate PET [23, 79, 84, 94].
In the reviewed studies, PET attenuation correction by conventional CT synthesis
methods exhibited an average bias of about 5% among selected VOIs. In contrast,
learning-based methods reduced the bias to around 2%, highlighting the significant
improvements achieved in PET accuracy with more accurate synthetic CT images
generated by these methods [17, 18, 23, 24, 84].
8.4.4 Image registration
In addition to its applications in radiation treatment planning and PET attenuation
correction, MR-based CT synthesis has demonstrated promise in facilitating intermodality image registration. Direct registration between CT and MR images is
challenging due to disparate image contrast, and this challenge is further amplified in
deformable registration, where significant geometric distortion is allowed.
McKenzie et al proposed a CycleGAN-based method to synthesize CT images,
utilizing the synthetic CT to replace MR imaging in MR-CT registration in the head
and neck [97]. By doing so, they transformed an inter-modality registration problem
into an intra-modality one. As summarized in table 8.5, their findings revealed that,
using the same deformable registration algorithm, the average landmark error
decreased from 9.8 ± 3.1 mm in direct MR-CT registration to 6.0 ± 2.1 mm when
using synthetic CT as a bridge. Similar positive results were reported in the inverse
CT-MR registration task.
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Artificial Intelligence in Adaptive Radiation Therapy
Table 8.5. Summary of study on MR-based synthetic CT for registration. (Adapted from [1]. CC BY 4.0.)
Site, and # of patients in
Author, year NetworkMRparameters
McKenzie et al [97] CycleGAN 0.35 T Head and neck: 25, 5-fold
training/testing
cross validation
Key findings in registration
accuracy
Landmark error (mm):
6.0 ± 2.1 (MR-to-CT)
6.6 ± 2.0 (CT-to-MR)
8.5 Discussion and outlook
Recent years have seen a surge in the utilization of deep learning within the realm of
medical imaging. Cutting-edge networks and techniques borrowed from computer
vision have been adapted to cater to specific clinical tasks in radiology and radiation
oncology. This chapter reviews the emerging and active field of CT synthesis, with
most of the studies covered being published within the last three years. With ongoing
advancements in both artificial intelligence and computing hardware, it is anticipated that more advanced learning-based methods will further enhance the clinical
workflow with novel applications. While the reviewed literature showcases the
success of deep learning-based image synthesis in various applications, there are still
some open questions that need addressing in future studies.
The incorporation of novel network architectures, including transformers and
diffusion models, holds great promise for advancing the field of CT synthesis. The
transformer architecture, renowned for its success in natural language processing
and image recognition, may offer enhanced capabilities in capturing long-range
dependencies and contextual information within medical images. The attention
mechanism in transformers enables the model to focus on relevant image regions,
potentially improving the synthesis accuracy, particularly in complex anatomical
structures. Similarly, the diffusion model, such as the DDPM, has emerged as a
powerful tool for deep generative tasks. Its unique two-stage approach involving
noise addition and subsequent denoising offers stability during training, making it
less susceptible to issues such as mode collapse and hyperparameter sensitivity. As
research in this area progresses, the application of diffusion models could contribute
to more robust and accurate CT synthesis, addressing challenges faced by current
deep learning-based methods.
The selection between 2D and 3D models for CT synthesis is a pivotal decision
that hinges on the specific demands and constraints of the application. 2D models
exhibit advantages in computational efficiency and training data availability,
making them suitable for scenarios with limited resources and large datasets.
However, challenges arise in their ability to capture 3D context and potential slice
discontinuities. On the other hand, 3D models inherently provide spatial context and
more homogeneous synthesis but demand greater computational resources and
extensive training data. Fu et al compared the performance of 2D and 3D models
using the same U-Net implementation, finding that 3D-generated synthetic CT
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exhibited smaller MAE and more accurate bone regions [21]. Hybrid approaches,
combining 3D patches or multiple adjacent slices, offer a compromise [98]. The
ongoing development of techniques that optimize both 2D and 3D models may
provide a balanced solution, ensuring that the choice aligns with the unique
requirements of medical imaging tasks, such as CT synthesis, where understanding
volumetric relationships is critical for accurate clinical applications.
The reviewed studies underscore the superiority of learning-based methods over
conventional approaches in terms of performance and clinical utility. Learningbased methods consistently surpass conventional ones by producing synthetic
images that closely resemble real images and exhibit superior quantitative metrics.
While the training process for learning-based methods demands hours to days, the
application of a trained model to new patients enables the rapid generation of
synthetic images within seconds to minutes. In contrast, conventional methods
display a broad spectrum of run times due to diverse methodologies, with iterative
approaches such as compressed sensing (CS) proving less favorable due to
substantial time and computational resource requirements.
While learning-based methods have demonstrated clear advantages, it is crucial
to acknowledge the potential unpredictability of their performance when dealing
with input images during production that significantly differ from the training
images. Many reviewed studies tend to exclude unusual cases, but the clinical reality
may present scenarios that deviate from the norm. Instances such as hip prostheses,
causing severe artifacts on both CT and MR images, could impact the application of
learning-based methods, and understanding such effects is essential. Unusual cases,
ranging from medical implants introducing artifacts to challenges posed by obesity
and anatomic deformities, may arise in various imaging modalities, warranting
further investigation to ensure the robustness and reliability of learning-based
models in diverse clinical scenarios.
Before integrating learning-based models into the clinical workflow, addressing
several challenges is paramount. To accommodate potentially unpredictable synthetic
images arising from non-compliance with imaging protocols in the training data or
unexpected anatomic variations, the implementation of additional quality assurance
(QA) steps becomes essential in clinical practice. QA procedures would be designed to
routinely assess or verify the consistency of model performance, either through
periodic checks or after upgrades, involving re-training the network with additional
patient datasets. This approach ensures the reliability of synthetic image quality across
a range of cases in diverse clinical scenarios.
8.6 Summary
In recent years, the increasing integration of deep learning into medical imaging has
been notable. Borrowing from computer vision, advanced techniques of AI are now
being tailored for clinical use in radiology and radiation oncology. Adaptive
radiation therapy is an emerging concept that involves complex imaging operations.
AI with its superior ability in image style transferring can facilitate the adaptive
radiation therapy workflow by synthesizing CT images from CBCT or/and MRI
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