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
studies to conduct spine SBRT using spinal fixation hardware with Varian triggered
imaging [88].
4.4.3 4D-CBCT
Four-dimensional CBCT (4D-CBCT) was developed to address respiratory motion
artifacts from CBCT. Under a 4D acquisition mode, the CBCT sorts projections
into different respiratory phase bins according to the patient’s breathing cycle. Just
like 4D-CT, it provides a set of phase-resolved volumetric images and minimizes
motion blurring artifacts [89]. 4D-CBCT images can also be aligned with 4D-CT
images to correct set-up errors or monitor motion prior to treatment. The
application of 4D-CBCT in the clinic, however, is limited due to its long acquisition
time. The total acquisition time for an Elekta XVI system is around 4 min. Recent
developments in deep learning-driven reconstruction in 4D-CBCT have the potential to expedite the deployment of 4D-CBCT in clinics [90].
4.4.4 Cine MRI
The rapid acquisition of 2D cine MRI presents a valuable opportunity for real-time
target position verification in MRgRT settings. Utilizing single or orthogonal 2D
cine MRI images at a frequency of approximately 5 frames per second (fps) within
modern MR-linac systems enables dynamic motion monitoring [91, 92]. Real-time
cine MRI serves multiple purposes, including motion tracking [92, 93] and beam
gating techniques [94, 95] to mitigate uncertainties stemming from intrafraction
motion. Motion tracking algorithms utilize image registration to continuously assess
target location, allowing for real-time adjustments of multi-leaf collimator (MLC)
leaf positions to accommodate changes in the target position [96 ]. Beam gating relies
on comparing real-time 2D cine MRI with pre-treatment reference MRI to calculate
target contour shifts, gating off the beam when the target exceeds predefined
boundaries [97]. Additionally, investigations into dynamic slice selection or multislice cine acquisition techniques aim to extend real-time motion monitoring to
multiple targets or OARs [98, 99]. Moreover, the availability of cine MRI facilitates
the evaluation of the relationship between tumors and surrogate markers used for
motion management, such as the skin surface. Retrospective studies have raised
concerns regarding the weak correlation between vertical belly motion and longitudinal tumor motion [100].
4.4.5 4D-MRI
Respiratory-correlated four-dimensional MRI (RC-4D-MRI) serves as a valuable
tool for characterizing respiratory motion throughout the thorax and abdomen,
offering improved visualization of OARs and enhanced flexibility in image orientation compared to 4D-CT [101]. Various strategies are employed to gather
volumetric data in RC-4D-MRI. 2D retrospective algorithms capture signals
repeatedly from the same volume and subsequently sort them according to the
respiratory cycle [102, 103]. However, retrospective algorithms are susceptible to
unstable respiratory motion, leading to missing slices. In contrast, 2D prospective
4-12
Artificial Intelligence in Adaptive Radiation Therapy
algorithms acquire slices based on real-time respiratory signals, thus avoiding
missing slices and enhancing acquisition efficiency [104, 105]. Recently, algorithms
utilizing 3D read-outs have emerged, offering improved signal-to-noise ratio (SNR),
enhanced geometric correction, motion averaging effects, and increased flexibility in
image dimensions [106–108]. Unlike RC-4D-MRI, which employs respiratory phase
as the fourth dimension, time-resolved four-dimensional MRI (TR-4D-MRI) does
not assume periodic respiratory motion, representing real-time data in the fourth
dimension. Consequently, TR-4D-MRI offers advantages in assessing irregular
respiratory motion or joint organ motion resulting from cardiac or digestive motion
[109]. 4D-MRI serves to provide valuable reference images for treatment planning
and the derivation of target and OAR motion models. A common motion management strategy involves delineating the internal target volume (ITV) by propagating
gross tumor volume contours across different phases to encompass the dynamic
target motion range [110].
4.4.6 Surface imaging
VisionRT is a surface imaging system for patient motion monitoring and tracking.
The system uses a pair of 3D cameras to project a speckled light pattern onto the
patient surface. The distortions and changes in the pattern are used for 3D
reconstruction. VisionRT’s software then constructs a 3D surface model by
rendering points into wireframe. This surface was then rigidly registered to a
reference surface in six degree of freedom (6DOF) to account for translational
and rotational shifts [111]. The reference surface can be the recorded surface of the
patient or derived from the body contours from CT volumetric data. The VisionRT
system can then display the 6DOF shift within the region-of-interest in real time to
help monitor or align the patient surface. These shifts can also be used to trigger
beam hold during treatment when the shift exceeded the preset threshold.
4.5 Imaging for treatment assessment
Treatment assessment plays a crucial role in evaluating the efficacy of interventions
and guiding clinical decision-making for healthcare providers. Medical imaging
techniques enable comprehensive, three-dimensional, and non-invasive assessment
of tumors throughout the body. Traditionally, changes in tumor size observed in
anatomical imaging have served as the primary criterion for treatment response
assessment, as outlined in guidelines such as those established by the World Health
Organization (WHO) and the response evaluation criteria in solid tumors (RECIST)
[112–114]. However, tumor size changes in many cases are slow to manifest and
occasionally challenging to discern, potentially leading to delays in therapeutic
decision-making [115]. In contrast, functional imaging methodologies offer the
capability to detect changes in biological and metabolic activities within tumors,
providing insights into cellular-level changes and facilitating earlier tumor
assessments.
4-13
Artificial Intelligence in Adaptive Radiation Therapy
4.5.1 Contrasted CT
Contrast-enhanced CT serves as a prominent modality for evaluating treatment
response in clinical practice. Upon intravenous administration of iodinated contrast
agents, CT signals undergo enhancement due to increased attenuation, directly
proportional to the concentration of the administered agents. This enhancement
pattern is influenced by factors including blood flow, capillary permeability, diffusion
rate, and the volume of the extravascular extracellular space. Dynamic contrast
enhanced CT (DCE-CT) involves acquiring a temporal series of images during
contrast agent administration, enabling assessment of tumor vascular support [116].
The inherent quantitative relationship between signal enhancement and contrast
concentration facilitates precise perfusion assessment, as a notable advantage over
DCE-MRI [117]. Moreover, DCE-CT demonstrates high reproducibility. Changes in
vascular volume observed in DCE-CT can serve as valuable biomarkers for treatment
response assessment, as microvascular damage constitutes a critical mechanism
underlying tumor response to radiation therapy [118]. Numerous studies have
documented reductions in parameters such as blood flow, blood volume, mean transit
time, and permeability–surface-area product following radiochemotherapy in patients
with diverse malignancies, underscoring the utility of DCE-CT in monitoring treatment response and guiding therapeutic decisions [119–125].
4.5.2 PET/CT
As depicted in section 4.2.2, PET/CT enables the monitoring of biological and
metabolic functions within the body using diverse tracers. Given that tumor response
is linked to tumor biology and microenvironmental characteristics, PET/CT emerges
as a potent modality for assessing early tumor response following radiotherapy,
thereby facilitating personalized treatment management [126]. FDG-PET is widely
utilized in the treatment assessment of progressive tumors with high FDG avidity,
including lymphoma [127–129], breast cancer [130–132], non-small cell lung cancer
[133–135], esophageal cancer [136–138], colorectal cancer [139, 140], and various
other malignancies [141–145]. The post-treatment SUV
hasbeenreportedtobe
max
predictive of complete response and patient survival [146–148]. Studies have shown
that FDG-PET achieves higher sensitivity and specificity compared to DCE-CT in
lymphoma and ovarian malignancy [149–151]. Moreover, PET/CT can be employed
to image cell proliferation using
18
F-FLT. Unlike FDG, FLT is exclusively taken up
by actively dividing cells. Changes in FLT uptake serve as indicators of cellular
response to treatment even before visible alterations in tumor volume manifest,
providing early decision support for patients [152, 153].
4.5.3 Functional MRI
Pathophysiological and microstructural changes in metabolic tissue profile, tissue
blood perfusion, microvessel permeability, and water mobility can be evaluated
using functional MRI techniques such as DCE-MRI, DWI, and magnetic resonance
spectroscopy (MRS) [154]. Similar to DCE-CT, DCE-MRI enables the assessment
4-14
Artificial Intelligence in Adaptive Radiation Therapy
of perfusion and permeability for studying tumor vascular physiology. Estimated
DCE-MRI parameters have been correlated with treatment response in various
cancers including brain tumor, head and neck cancer, breast cancer, rectal cancer,
and prostate cancer [155–159]. DWI is sensitive to the Brownian motion of
microscopic water molecules. Tumor necrosis in response to therapy typically
exhibits decreased cellularity, resulting in reduced signal on DWI and increased
apparent diffusion coefficient (ADC) [160]. Changes in diffusion signal and ADC
have proven valuable in predicting tumor response in numerous studies [161–163].
MRS offers insight into the metabolic composition of tissue, including metabolism,
membrane turnover, necrosis, energy homeostasis, and proliferation [154]. Tumor
response is often associated with increased signals from lactate, lipid, or choline
concentrations [164–168].
4.6 Summary
Imaging is playing an increasingly important role in radiation oncology, from pretreatment tumor staging, treatment planning, target delineation, to in-treatment imageguidance and post-treatment response monitoring. Traditionally, CT plays the most
important role in the pre-treatment stage. A well calibrated CT image provides accurate
electron density for dose calculation and clear anatomical information for treatment
target and OAR delineation in modern treatment planning. In recent years, MR has
played a more and more important role in pre-treatment, given the sophisticated
technological advancements in synthetic CT creation [169, 170] and crispy soft tissue
visualization.In-treatment guidance, from traditionalportal imaging to CBCT and CTon-rail, and most recent MR guidance(MR-linac) and biologicalguidance(PET-linac),
the technological advancementhas enabledhigh-precision radiationoncology withrealtime adaptive planning. In post-treatment, functional MRI and PET have been widely
used as an effective non-invasivetooltomonitor treatment response [171]. Theyare often
usedas informative toolsto decide whether furtherinvasive operation is neededor not to
achieve optimal cancer management. Finally, it is also worth mentioning that artificial
intelligence (AI) has greatly reshaped the future of imaging in radiation oncology. AI
technologies have been widely used in image reconstruction, post-reconstruction image
processing, treatment planning, treatment guidance, and predictive modeling for treatment outcome monitoring [172]. In future research, a deeper integration of AI
technologies with all aspects of imaging in radiation oncology should be a focus.
Particularly, the exploration of pre-treatment and in-treatment quantitative imaging
biomarkers for treatment outcome prediction, patient stratification, and personalized
treatment would be an important research area.
References
[1] Yan D, Vicini F, Wong J and Martinez A 1997 Adaptive radiation therapy Phys. Med.
Biol.
42 123–32
[2] Martinez A A et al 2001 Improvement in dose escalation using the process of adaptive
radiotherapy combined with three-dimensional conformal or intensity-modulated beams
for prostate cancer Int. J. Radiat. Oncol.
50 1226–34
4-15
Artificial Intelligence in Adaptive Radiation Therapy
[3] Nijkamp J et al 2008 Adaptive radiotherapy for prostate cancer using kilovoltage cone-
beam computed tomography: first clinical results Int. J. Radiat. Oncol.
[4] Geets X et al 2007 Adaptive biological image-guided IMRT with anatomic and functional
imaging in pharyngo-laryngeal tumors: impact on target volume delineation and dose
distribution using helical tomotherapy Radiother. Oncol.
[5] Mutic S and Dempsey J F 2014 The ViewRay system: magnetic resonance-guided and
controlled radiotherapy Semin. Radiat. Oncol.
[6] Andersson L et al 2020 PO-1939: training of RTT on CBCT online adaptive radiotherapy—
first step finding the future ‘adapters’ Radiother. Oncol.
[7] Coolens C et al 2009 Implementation and characterization of a 320-slice volumetric CT
scanner for simulation in radiation oncology Med. Phys.
[8] Zhu J and Penfold S N 2016 Dosimetric comparison of stopping power calibration with
dual-energy CT and single-energy CT in proton therapy treatment planning Med. Phys.
2845–54
[9] Bär E, Lalonde A, Royle G, Lu H-M and Bouchard H 2017 The potential of dual-energy
CT to reduce proton beam range uncertainties Med. Phys.
[10] Hu G et al 2022 Assessment of quantitative information for radiation therapy at a first-
generation clinical photon-counting computed tomography scanner Front. Oncol.
970299
[11] Simard M, Lapointe A, Lalonde A, Bahig H and Bouchard H 2019 The potential of
photon-counting CT for quantitative contrast-enhanced imaging in radiotherapy Phys.
Med. Biol.
[12] Rietzel E, Pan T and Chen G T Y 2005 Four-dimensional computed tomography: image
formation and clinical protocol Med. Phys.
[13] Senan S and De Ruysscher D 2005 Critical review of PET-CT for radiotherapy planning in
lung cancer Crit. Rev. Oncol. Hematol.
[14] Fonti R, Conson M and Del Vecchio S 2019 PET/CT in radiation oncology Semin. Oncol.
46 202–9
[15] Jelercic S and Rajer M 2015 The role of PET-CT in radiotherapy planning of solid tumours
Radiol. Oncol.
[16] De Ruysscher D et al 2005 Selective mediastinal node irradiation based on FDG-PET scan
data in patients with non-small-cell lung cancer: a prospective clinical study Int. J. Radiat.
Oncol.
[17] Steenbakkers R J H M et al 2006 Reduction of observer variation using matched CT-PET
for lung cancer delineation: a three-dimensional analysis Int. J. Radiat. Oncol.
[18] Nestle U, Kremp S and Grosu A-L 2006 Practical integration of [18F]-FDG-PET and PET-
CT in the planning of radiotherapy for non-small cell lung cancer (NSCLC): the technical
basis, ICRU-target volumes, problems, perspectives Radiother. Oncol.
[19] Daisne J-F et al 2004 Tumor volume in pharyngolaryngeal squamous cell carcinoma:
comparison at CT, MR imaging, and FDG PET and validation with surgical specimen
Radiology
[20] Leong T et al 2006 A prospective study to evaluate the impact of FDG-PET on CT-based
radiotherapy treatment planning for oesophageal cancer Radiother. Oncol.
[21] Kidd E A et al 2010 Clinical outcomes of definitive intensity-modulated radiation therapy
with fluorodeoxyglucose–positron emission tomography simulation in patients with locally
advanced cervical cancer Int. J. Radiat. Oncol.
64 115020
49 1–9
62 988–94
233 93–100
24 196–9
32 874–89
56 345–51
77 1085–91
85 105–15
152 S1079
36 5120–7
44 2332–44
70 75–82
43
12
64 435–48
81 209–25
78 254–61
4-16
Artificial Intelligence in Adaptive Radiation Therapy
[22] Schreurs L M A et al 2010 Impact of 18-fluorodeoxyglucose positron emission tomography
on computed tomography defined target volumes in radiation treatment planning of
esophageal cancer: reduction in geographic misses with equal inter-observer variability:
PET/CT improves esophageal target definition Dis. Esoph.
23 493–501
[23] Bradley J et al 2004 Impact of FDG-PET on radiation therapy volume delineation in non-
small-cell lung cancer Int. J. Radiat. Oncol.
[24] Aerts H J W L et al 2009 Identification of residual metabolic-active areas within individual
NSCLC tumours using a pre-radiotherapy
Oncol.
91 386–92
59 78–86
18
fluorodeoxyglucose-PET-CT scan Radiother.
[25] Rajendran J G et al 2006 Tumor hypoxia imaging with [F-18] fluoromisonidazole positron
emission tomography in head and neck cancer Clin. Cancer Res.
[26] Iommelli F et al 2014 Monitoring reversal of MET-mediated resistance to EGFR tyrosine
kinase inhibitors in non-small cell lung cancer using 3′-deoxy-3′-[
positron emission tomography Clin. Cancer Res.
20 4806–15
12 5435–41
18
F]-fluorothymidine
[27] McKay M J, Taubman K L, Foroudi F, Lee S T and Scott A M 2018 Molecular imaging
using PET/CT for radiation therapy planning for adult cancers: current status and
expanding applications Int. J. Radiat. Oncol.
102 783–91
[28] Soret M, Bacharach S L and Buvat I 2007 Partial-volume effect in PET tumor imaging
J. Nucl. Med.
48 932–45
[29] Specht L and Berthelsen A K 2018 PET/CT in radiation therapy planning Semin. Nucl.
Med.
48 67–75
[30] Firouzian A, Kelly M D and Declerck J M 2014 Insight on automated lesion delineation
methods for PET data EJNMMI Res.
4 69
[31] Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch C-M and Nestle U 2008 A contrast-
oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient
data Eur. J. Nucl. Med. Mol. Imaging
35 1989–99
[32] Geets X, Lee J A, Bol A, Lonneux M and Grégoire V 2007 A gradient-based method for
segmenting FDG-PET images: methodology and validation Eur. J. Nucl. Med. Mol.
Imaging
34 1427–38
[33] Hatt M, Cheze Le Rest C, Turzo A, Roux C and Visvikis D 2009 A fuzzy locally adaptive
Bayesian segmentation approach for volume determination in PET IEEE Trans. Med.
Imaging
28 881–93
[34] Owrangi A M, Greer P B and Glide-Hurst C K 2018 MRI-only treatment planning:
benefits and challenges Phys. Med. Biol.
63 05TR01
[35] Devic S 2012 MRI simulation for radiotherapy treatment planning Med. Phys. 39 6701
[36] Edmund J M and Nyholm T 2017 A review of substitute CT generation for MRI-only
radiation therapy Radiat. Oncol.
12 28
[37] Johnstone E et al 2018 Systematic review of synthetic computed tomography generation
methodologies for use in magnetic resonance imaging-only radiation therapy Int. J. Radiat.
Oncol.
100 199–217
[38] Reichert M, Ai T, Morelli J N, Nittka M, Attenberger U and Runge V M 2015 Metal
artefact reduction in MRI at both 1.5 and 3.0 T using slice encoding for metal artefact
correction and view angle tilting Br. J. Radiol.
88 20140601
[39] Koch K M et al 2011 Imaging near metal with a MAVRIC-SEMAC hybrid Magn. Reson.
Med.
65 71–82
4-17
Artificial Intelligence in Adaptive Radiation Therapy
[40] Maikusa N et al 2013 Improved volumetric measurement of brain structure with a
distortion correction procedure using an ADNI phantom Med. Phys.
[41] Price R G, Knight R A, Hwang K, Bayram E, Nejad-Davarani S P and Glide-Hurst C K
2017 Optimization of a novel large field of view distortion phantom for MR-only treatment
planning J. Appl. Clin. Med. Phys.
[42] Grande F D, Guggenberger R and Fritz J 2021 Rapid musculoskeletal MRI in 2021: value
and optimized use of widely accessible techniques Am. J. Roentgenol.
[43] Delfaut E M, Beltran J, Johnson G, Rousseau J, Marchandise X and Cotten A 1999 Fat
suppression in MR imaging: techniques and pitfalls RadioGraphics
[44] Van Houdt P J, Li S, Yang Y and Van Der Heide U A 2024 Quantitative MRI on MR-
linacs: towards biological image-guided adaptive radiotherapy Semin. Radiat. Oncol.
107–19
[45] Rouvière O et al 2004 Recurrent prostate cancer after external beam radiotherapy: value of
contrast-enhanced dynamic MRI in localizing intraprostatic tumor—correlation with
biopsy findings Urology
[46] Na H B, Song I C and Hyeon T 2009 Inorganic nanoparticles for MRI contrast agents Adv.
Mater.
21 2133–48
[47] Xiao Y-D, Paudel R, Liu J, Ma C, Zhang Z-S and Zhou S-K 2016 MRI contrast agents:
classification and application (review) Int. J. Mol. Med.
[48] Han K et al 2016 A prospective study of DWI, DCE-MRI and FDG PET imaging for
target delineation in brachytherapy for cervical cancer Radiother. Oncol.
[49] Skinner J T, Moots P L, Ayers G D and Quarles C C 2016 On the use of DSC-MRI for
measuring vascular permeability Am. J. Neuroradiol.
[50] Lagendijk J J W, Raaymakers B W, Van Den Berg C A T, Moerland M A, Philippens M E
and Van Vulpen M 2014 MR guidance in radiotherapy Phys. Med. Biol.
[51] Metcalfe P et al 2013 The potential for an enhanced role for MRI in radiation-therapy
treatment planning Technol. Cancer Res. Treat.
[52] Schmainda K M et al 2018 Multisite concordance of DSC-MRI analysis for brain tumors:
results of a national cancer institute quantitative imaging network collaborative project
Am. J. Neuroradiol.
[53] Van Der Heide U A, Houweling A C, Groenendaal G, Beets-Tan R G H and Lambin P
2012 Functional MRI for radiotherapy dose painting Magn. Reson. Imaging
[54] Lagendijk J J W, Raaymakers B W and Van Vulpen M 2014 The magnetic resonance
imaging–linac system Semin. Radiat. Oncol.
[55] Dalah E, Moraru I, Paulson E, Erickson B and Li X A 2014 Variability of target and
normal structure delineation using multimodality imaging for radiation therapy of
pancreatic cancer Int. J. Radiat. Oncol.
[56] Groenendaal G et al 2010 Simultaneous MRI diffusion and perfusion imaging for tumor
delineation in prostate cancer patients Radiother. Oncol.
[57] Miri N, Keller P, Zwan B J and Greer P 2016 EPID-based dosimetry to verify IMRT
planar dose distribution for the aS1200 EPID and FFF beams J. Appl. Clin. Med. Phys.
292–304
[58] Stanley D N, Rasmussen K, Kirby N, Papanikolaou N and Gutiérrez A N 2018 An
evaluation of the stability of image quality parameters of Elekta x-ray volume imager and
iViewGT imaging systems J. Appl. Clin. Med. Phys.
63 922–7
39 1008–16
18 51–61
38 1319–26
37 80–7
12 429–46
24 207–9
89 633–40
95 185–90
19 64–70
40 062303
216 704–17
19 373–82
34
120 519–25
59 R349–69
30 1216–23
17
4-18
Artificial Intelligence in Adaptive Radiation Therapy
[59] Stanley D N, Papanikolaou N and Gutiérrez A N 2015 An evaluation of the stability of
image-quality parameters of varian on-board imaging (OBI) and EPID imaging systems
J. Appl. Clin. Med. Phys.
[60] Kroening P 2012 Reducing dose while maintaining image quality for cone beam computed
tomography BSc Thesis Houghton College, NY
[61] Jaffray D A, Drake D G, Moreau M, Martinez A A and Wong J W 1999 A radiographic
and tomographic imaging system integrated into a medical linear accelerator for localization of bone and soft-tissue targets Int. J. Radiat. Oncol. Biol. Phys.
[62] Wang A et al 2018 Acuros CTS: a fast, linear Boltzmann transport equation solver for
computed tomography scatter—part II: system modeling, scatter correction, and optimization Med. Phys.
[63] Bogowicz M et al 2024 Evaluation of a cone-beam computed tomography system calibrated
for accurate radiotherapy dose calculation Phys. Imaging. Radiat. Oncol.
[64] Ma C-M C and Paskalev K 2006 In-room CT techniques for image-guided radiation
therapy Med. Dosim.
[65] Kuriyama K et al 2003 A new irradiation unit constructed of self-moving gantry-CT and
linac Int. J. Radiat. Oncol.
[66] Court L, Rosen I, Mohan R and Dong L 2003 Evaluation of mechanical precision and
alignment uncertainties for an integrated CT/LINAC system Med. Phys.
[67] Knight K, Touma N, Zhu L, Duchesne G and Cox J 2009 Implementation of daily image-
guided radiation therapy using an in-room CT scanner for prostate cancer isocentre
localization J. Med. Imaging Radiat. Oncol.
[68] Peng C, Ahunbay E, Chen G, Anderson S, Lawton C and Li X A 2011
Characterizing interfraction variations and their dosimetric effects in prostate cancer
radiotherapy Int. J. Radiat. Oncol.
[69] Kumabe A et al 2015 Three-dimensional conformal arc radiotherapy using a C-arm linear
accelerator with a computed tomography on-rail system for prostate cancer: clinical
outcomes Radiat. Oncol.
[70] Ikushima H et al 2011 Daily alignment results of in-room computed tomography-guided
stereotactic body radiation therapy for lung cancer Int. J. Radiat. Oncol.
[71] Yang Z et al 2019 Effect of setup and inter-fraction anatomical changes on the accumulated
dose in CT-guided breath-hold intensity modulated proton therapy of liver malignancies
Radiother. Oncol.
[72] Jiang D et al 2023 Total marrow lymphoid irradiation IMRT treatment using a novel CT-
linac Eur. J. Med. Res.
[73] Yu L, Zhao J, Zhang Z, Wang J and Hu W 2021 Commissioning of and preliminary
experience with a new fully integrated computed tomography Linac J. Appl. Clin. Med.
Phys.
22 208–23
[74] Sun W et al 2024 The performance of a new type accelerator uRT-linac 506c evaluated by a
quality assurance automation system J. Appl. Clin. Med. Phys.
[75] Keall P J et al 2022 Integrated MRI-guided radiotherapy—opportunities and challenges
Nat. Rev. Clin. Oncol.
[76] Ng J et al 2023 MRI-LINAC: a transformative technology in radiation oncology Front.
Oncol.
13 1117874
[77] Huang C-Y, Yang B, Lam W W, Geng H, Cheung K Y and Yu S K 2023 Magnetic fi eld
induced dose effects in radiation therapy using MR-linacs Med. Phys.
134 101–9
16 87–98
45 773–89
45 1914–25
29 100566
31 30–9
55 428–35
30 1198–210
53 132–8
79 909–14
10 208
79 473–80
28 463
25 e14226
19 458–70
50 3623–36
4-19
Artificial Intelligence in Adaptive Radiation Therapy
[78] Raaymakers B W et al 2009 Integrating a 1.5 T MRI scanner with a 6 MV accelerator:
proof of concept Phys. Med. Biol.
[79] Roberts D A et al 2021 Machine QA for the Elekta Unity system: a report from the Elekta
MR-linac consortium Med. Phys.
[80] Jassar H et al 2023 Real-time motion monitoring using orthogonal cine MRI during MR-
guided adaptive radiation therapy for abdominal tumors on 1.5T MR-linac Med. Phys.
3103–16
[81] Grimbergen et al 2023 Gating and intrafraction drift correction on a 1.5 T MR-linac:
clinical dosimetric benefits for upper abdominal tumors Radiother. Oncol. J. Eur. Soc. Ther.
Radiol. Oncol.
[82] Raaijmakers A J E, Raaymakers B W and Lagendijk J J W 2008 Magnetic-field-induced
dose effects in MR-guided radiotherapy systems: dependence on the magnetic field strength
Phys. Med. Biol.
[83] Oderinde O M, Shirvani S M, Olcott P D, Kuduvalli G, Mazin S and Larkin D 2021 The
technical design and concept of a PET/CT linac for biology-guided radiotherapy Clin.
Transl. Radiat. Oncol.
[84] Surucu M et al 2021 Comparison of a first-in-class LINAC-integrated PET system and a
diagnostic PET/CT scanner Int. J. Radiat. Oncol.
[85] Lee N et al 2016 Strategy of using intratreatment hypoxia imaging to selectively and safely
guide radiation dose de-escalation concurrent with chemotherapy for locoregionally
advanced human papillomavirus-related oropharyngeal carcinoma Int. J. Radiat. Oncol.
96 9–17
[86] Kong F-M et al 2017 Effect of midtreatment PET/CT-adapted radiation therapy with
concurrent chemotherapy in patients with locally advanced non-small-cell lung cancer: a
phase 2 clinical trial JAMA Oncol.
[87] Koo J et al 2021 Triggered kV imaging during spine SBRT for intrafraction motion
management Technol. Cancer Res. Treat.
[88] Cetnar A J et al 2023 Implementation of triggered kilovoltage imaging for stereotactic
radiotherapy of the spine for patients with spinal fixation hardware Phys. Imaging. Radiat.
Oncol.
25 100422
[89] Jia X, Tian Z, Lou Y, Sonke J and Jiang S B 2012 Four-dimensional cone beam CT
reconstruction and enhancement using a temporal nonlocal means method Med. Phys.
5592–602
[90] Zhang Y, Huang X and Wang J 2019 Advanced 4-dimensional cone-beam computed
tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning Vis. Comput. Ind. Biomed. Art.
[91] Sawant A et al 2014 Investigating the feasibility of rapid MRI for image-guided motion
management in lung cancer radiotherapy BioMed. Res. Int.
[92] Keiper T D et al 2020 Feasibility of real-time motion tracking using cine MRI during MR-
guided radiation therapy for abdominal targets Med. Phys.
[93] Jassar H et al 2023 Real-time motion monitoring using orthogonal cine MRI during MR-
guided adaptive radiation therapy for abdominal tumors on 1.5T MR-linac Med. Phys.
3103–16
[94] Cunningham J M et al 2022 On-line adaptive and real-time intrafraction motion manage-
ment of spine-SBRT on an MR-linac Front. Phys.
189 109932
53 909–23
29 106–12
54 N229–237
48 e67–85
50
111 e515–6
3 1358
20
39
2 23
2014 1–6
47 3554–66
50
10 882564
4-20
Artificial Intelligence in Adaptive Radiation Therapy
[95] Akdag O et al 2022 First experimental exploration of real-time cardiorespiratory motion
management for future stereotactic arrhythmia radioablation treatments on the MR-linac
Phys. Med. Biol.
[96] Sawant A et al 2008 Management of three-dimensional intrafraction motion through real-
time DMLC tracking Med. Phys.
[97] Ehrbar S et al 2022 MR-guided beam gating: residual motion, gating efficiency and dose
reconstruction for stereotactic treatments of the liver and lung Radiother. Oncol.
[98] Mickevicius N J, Chen X, Boyd Z, Lee H J, Ibbott G S and Paulson E S 2018 Simultaneous
motion monitoring and truth-in-delivery analysis imaging framework for MR-guided
radiotherapy Phys. Med. Biol.
[99] Keijnemans K, Borman P T S, Uijtewaal P, Woodhead P L, Raaymakers B W and Fast M F
2022 A hybrid 2D/4D-MRI methodology using simultaneous multislice imaging for radiotherapy guidance Med. Phys.
[100] Mao W, Kim J and Chetty I J 2022 Association between internal organ/liver tumor and
external surface motion from cine MR images on an MRI-linac Front. Oncol.
[101] Stemkens B, Paulson E S and Tijssen R H N 2018 Nuts and bolts of 4D-MRI for
radiotherapy Phys. Med. Biol.
[102] Liu Y et al 2014 Investigation of sagittal image acquisition for 4D-MRI with body area as
respiratory surrogate Med. Phys.
[103] Cai J, Chang Z, Wang Z, Paul Segars W and Yin F 2011 Four-dimensional magnetic
resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study
Med. Phys.
[104] Du D et al 2015 High-quality T2-weighted 4-dimensional magnetic resonance imaging for
radiation therapy applications Int. J. Radiat. Oncol.
[105] Glide-Hurst C K et al 2015 Four dimensional magnetic resonance imaging optimization
and implementation for magnetic resonance imaging simulation Pract. Radiat. Oncol.
433–42
[106] Han F, Zhou Z, Cao M, Yang Y, Sheng K and Hu P 2017 Respiratory motion-resolved,
self-gated 4D-MRI using rotating Cartesian k-space (ROCK) Med. Phys.
[107] Stemkens B et al 2015 Optimizing 4-dimensional magnetic resonance imaging data
sampling for respiratory motion analysis of pancreatic tumors Int. J. Radiat. Oncol.
571–8
[108] Deng Z et al 2017 Improved vessel–tissue contrast and image quality in 3D radial sampling-
based 4D-MRI J. Appl. Clin. Med. Phys.
[109] Li G, Liu Y and Nie X 2019 Respiratory-correlated (RC) vs time-resolved (TR) four-
dimensional magnetic resonance imaging (4DMRI) for radiotherapy of thoracic and
abdominal cancer Front. Oncol.
[110] Paulson E S and Tijssen R H N 2019 Motion management MRI Radiotheraphy ed G Liney
and U Van Der Heide (Cham: Springer International) pp 107–16
[111] Al-Hallaq H A et al 2022 AAPM task group report 302: surface-guided radiotherapy Med.
Phys.
49 e82–e112
[112] Kang H, Lee H Y, Lee K S and Kim J-H 2012 Imaging-based tumor treatment response
evaluation: review of conventional, new, and emerging concepts Korean J. Radiol.
[113] Eisenhauer E A et al 2009 New response evaluation criteria in solid tumours: revised
RECIST guideline (version 1.1) Eur. J. Cancer
67 065003
35 2050–61
174 101–8
63 235014
49 6068–81
12 868076
63 21TR01
41 101902
38 6384–94
92 430–7
44 1359–68
91
18 250–7
9 1024
13 371
45 228–47
5
4-21