Research graph
References from Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy. Local targets link to admitted publications; unresolved targets remain external evidence.
Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system
10.1016/j.ijrobp.2003.12.024 · 2004 · External reference
Identifying patients who may benefit from adaptive radiotherapy: does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help?
10.1016/j.radonc.2015.05.018 · 2015 · External reference
Head and neck cancer adaptive radiation therapy (ART): conceptual considerations for the informed clinician
10.1016/j.semradonc.2019.02.008 · 2019 · External reference
Adaptive radiotherapy for anatomical changes
10.1016/j.semradonc.2019.02.007 · 2019 · External reference
kV cone-beam CT-based IGRT: a clinical review
10.1007/s00066-011-2236-4 · 2011 · External reference
A review of dose calculation approaches with cone-beam CT in photon and proton therapy
10.1016/j.ejmp.2020.06.017 · 2020 · External reference
Feasibility of CBCT-based dose calculation: comparative analysis of HU adjustment techniques
10.1016/j.radonc.2012.06.007 · 2012 · External reference
Comparing cone-beam CT intensity correction methods for dose recalculation in adaptive photon and proton therapy for head and neck cancer
10.3109/0284186x.2015.1061206 · 2015 · External reference
Evaluating the impact of cone-beam CT scatter mitigation strategies on radiotherapy dose calculation accuracy
10.1016/j.phro.2019.04.001 · 2019 · External reference
Artefacts in CBCT: a review
10.1259/dmfr/30642039 · 2011 · External reference
Varian ethos online adaptive radiotherapy for prostate cancer: early results of contouring accuracy, treatment plan quality, and treatment time
10.1002/acm2.13479 · 2022 · External reference
First clinical experiences with a high-field 1.5 T MR-linac
10.1080/0284186x.2019.1627417 · 2019 · External reference
Synthetic CT image generation from CBCT: a systematic review
10.1109/trpms.2025.3533749 · 2025 · External reference
Deep learning based synthetic-CT generation in radiotherapy and PET: a review
10.1002/mp.15150 · 2021 · External reference
Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images
10.1118/1.3483260 · 2010 · External reference
Proton dose calculation on scatter-corrected CBCT image: feasibility study for adaptive proton therapy
10.1118/1.4923179 · 2015 · External reference
First clinical investigation of cone beam computed tomography and deformable registration for adaptive proton therapy for lung cancer
10.1016/j.ijrobp.2016.01.055 · 2016 · External reference
ScatterNet: a convolutional neural network for cone-beam CT intensity correction
10.1002/mp.13175 · 2018 · External reference
Paired CycleGAN-based image correction for quantitative cone-beam CT
10.1002/mp.13656 · 2019 · External reference
Generating synthesized CT from cone-beam CT using CycleGAN for adaptive radiotherapy
10.1088/1361-6560/ab22f9 · 2019 · External reference
CBCT correction using a cycle-consistent GAN and unpaired training to enable photon and proton dose calculation
10.1088/1361-6560/ab4d8c · 2019 · External reference
Unresolved reference
2015 · External reference
A single neural network for cone-beam CT-based radiotherapy of head-and-neck, lung and breast cancer
10.1016/j.phro.2020.04.002 · 2020 · External reference
Clinical suitability of deep learning-based synthetic CTs for adaptive proton therapy of lung cancer
10.1002/mp.15333 · 2021 · External reference
Evaluation of a CycleGAN-based CBCT-to-CT conversion algorithm for adaptive radiotherapy
10.1016/j.ejmp.2020.11.007 · 2020 · External reference
Synthetic CT generation from CBCT images via deep learning
10.1002/mp.13978 · 2020 · External reference
CBCT-based synthetic CT generation using deep-attention CycleGAN for pancreatic adaptive radiotherapy
10.1002/mp.14121 · 2020 · External reference
Improving cone-beam CT quality using a cycle-residual connection with a dilated convolution–consistent GAN
10.1088/1361-6560/ac7b0a · 2022 · External reference
CBCT-to-CT synthesis for cervical cancer adaptive radiotherapy via a U-net-based model trained with a hybrid dataset
10.3390/cancers15225479 · 2023 · External reference
CBCT-to-CT translation using registration-based GANs in head-and-neck cancer patients
10.3390/cancers15072017 · 2023 · External reference
CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma
10.1038/s41598-023-33472-w · 2023 · External reference
Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations
10.1088/1361-6560/aaf496 · 2019 · External reference
A more effective CT synthesizer using transformers for CBCT-guided adaptive radiotherapy
2022 · External reference
Synthetic CT generation based on CBCT using improved vision transformer CycleGAN
10.1038/s41598-024-61492-7 · 2024 · External reference
An image is worth 16×16 words: transformers for image recognition at scale
2021 · External reference
CBCT-based synthetic CT image generation using conditional denoising diffusion probabilistic model
10.1002/mp.16704 · 2024 · External reference
Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report
2024 · External reference
Assessment of CBCT-based synthetic CT generation accuracy for adaptive radiotherapy planning
2022 · External reference
UNet 3+: A full-scale connected UNet for medical image segmentation
2020 · External reference
Unresolved reference
2016 · External reference
Unresolved reference
2018 · External reference
Concurrent spatial and channel squeeze & excitation in fully convolutional networks
2018 · External reference
Fusing residual dense and attention mechanisms in generative adversarial networks for super-resolution of medical images
10.21037/qims-2025-3 · 2025 · External reference
Leveraging GAN-based CBCT-to-CT translation models for enhanced image quality and accurate photon and proton dose calculation in adaptive radiotherapy
2024 · External reference
Image-to-image translation with conditional adversarial networks
2017 · External reference
Bridging 2D and 3D segmentation networks for computationally efficient volumetric medical image segmentation: an empirical study of 2.5D solutions
10.1016/j.compmedimag.2022.102088 · 2022 · External reference
Unresolved reference
2016 · External reference
Generative adversarial nets
2014 · External reference
Perceptual losses for real-time style transfer and super-resolution
2016 · External reference
Image quality assessment: from error visibility to structural similarity
10.1109/tip.2003.819861 · 2004 · External reference
Unresolved reference
2016 · External reference
Enhancing medical image segmentation based on loss functions integration
10.21608/sej.2025.357874.1073 · 2025 · External reference
Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study
10.2196/26151 · 2021 · External reference
Generating synthetic images from CBCT using self-attention residual U-net for head-and-neck radiotherapy
2023 · External reference
Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer
10.3857/roj.2023.00584 · 2024 · External reference
Cone-beam CT-based synthetic CT generation using deep learning for dose calculation in nasopharyngeal carcinoma radiotherapy
10.1177/15330338211062415 · 2021 · External reference
Generating synthetic CT from low-dose CBCT using GANs for adaptive radiotherapy
10.1186/s13014-021-01928-w · 2021 · External reference
Cone-beam CT-based validation of neural network–generated synthetic CTs for radiotherapy in the head region
10.1002/mp.14987 · 2021 · External reference
Comparison of supervised and unsupervised approaches for the generation of synthetic CT from cone-beam CT
10.3390/diagnostics11081435 · 2021 · External reference
Comparison and evaluation of deep learning models for synthetic CT generation from CBCT in nasopharyngeal cancer adaptive proton therapy
10.1002/mp.16777 · 2023 · External reference
Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients
10.1088/2057-1976/acea27 · 2023 · External reference
Tolerance limits and methodologies for IMRT measurement-based verification QA: recommendations of AAPM task group no. 218
10.1002/mp.12810 · 2018 · External reference
Evaluation of novel AI-based extended field-of-view CT reconstructions
10.1002/mp.14937 · 2021 · External reference
Daily kV cone-beam CT and deformable registration as a method for studying dose accumulation in adaptive radiotherapy of head and neck cancer
10.3109/0284186x.2010.500304 · 2010 · External reference
Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM radiation therapy committee task group no. 132
10.1002/mp.12256 · 2017 · External reference
Review and recommendations on deformable image registration uncertainties for radiotherapy applications
10.1088/1361-6560/ad0d8a · 2023 · External reference
Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head-and-neck patients
10.1088/1361-6560/abb1d6 · 2020 · External reference
Practical clinical workflows for online and offline adaptive radiation therapy
10.1016/j.semradonc.2019.02.004 · 2019 · External reference
