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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge
Elizabeth McKenzie, Anand P. Santhanam, Dan Ruan, et al.
Medical Physics (2019) Vol. 47, Iss. 3, pp. 1094-1104
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

A review on medical imaging synthesis using deep learning and its clinical applications
Tonghe Wang, Yang Lei, Yabo Fu, et al.
Journal of Applied Clinical Medical Physics (2020) Vol. 22, Iss. 1, pp. 11-36
Open Access | Times Cited: 210
Deep learning based synthetic‐CT generation in radiotherapy and PET: A review
Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino, et al.
Medical Physics (2021) Vol. 48, Iss. 11, pp. 6537-6566
Open Access | Times Cited: 73
Machine learning applications in radiation oncology
Matthew Field, Nicholas Hardcastle, Michael Jameson, et al.
Physics and Imaging in Radiation Oncology (2021) Vol. 19, pp. 13-24
Open Access | Times Cited: 62
Plant Genotype to Phenotype Prediction Using Machine Learning
Monica F. Danilevicz, Mitchell Gill, Robyn Anderson, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 56
A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy
Moiz Khan Sherwani, Shyam Gopalakrishnan
Frontiers in Radiology (2024) Vol. 4
Open Access | Times Cited: 9
Review and recommendations on deformable image registration uncertainties for radiotherapy applications
Lena Nenoff, Florian Amstutz, Martina Murr, et al.
Physics in Medicine and Biology (2023) Vol. 68, Iss. 24, pp. 24TR01-24TR01
Open Access | Times Cited: 21
A review on AI-based medical image computing in head and neck surgery
Jiangchang Xu, Bolun Zeng, Jan Egger, et al.
Physics in Medicine and Biology (2022) Vol. 67, Iss. 17, pp. 17TR01-17TR01
Closed Access | Times Cited: 27
Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: methods, applications and limitations
Dildar Hussain, Mohammed A. Al‐masni, Muhammad Aslam, et al.
Journal of X-Ray Science and Technology (2024) Vol. 32, Iss. 4, pp. 857-911
Closed Access | Times Cited: 5
A transformer-based hierarchical registration framework for multimodality deformable image registration
Yao Zhao, Xinru Chen, Brigid A. McDonald, et al.
Computerized Medical Imaging and Graphics (2023) Vol. 108, pp. 102286-102286
Closed Access | Times Cited: 11
Synthetic CT generation using Zero TE MR for head-and-neck radiotherapy
Iris Lauwers, Marta E. Capala, Sandeep Kaushik, et al.
Radiotherapy and Oncology (2025), pp. 110762-110762
Closed Access
Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model
Yi Guo, Jun Chen, Lu Lin, et al.
Radiation Oncology (2025) Vol. 20, Iss. 1
Open Access
A systematic review of deep learning techniques for generating synthetic CT images from MRI data
Isaac Kwesi Acquah, Issahaku Shirazu, Samuel Nii Adu Tagoe
Polish Journal of Medical Physics And Engineering (2025) Vol. 31, Iss. 1, pp. 20-38
Open Access
Artificial Intelligence for Image Registration in Radiation Oncology
Jonas Teuwen, Zeno A.R. Gouw, Jan‐Jakob Sonke
Seminars in Radiation Oncology (2022) Vol. 32, Iss. 4, pp. 330-342
Closed Access | Times Cited: 16
Machine Learning for Medical Image Translation: A Systematic Review
Jake McNaughton, Justin Fernandez, Samantha J. Holdsworth, et al.
Bioengineering (2023) Vol. 10, Iss. 9, pp. 1078-1078
Open Access | Times Cited: 8
Abnormal default mode network could be a potential prognostic marker in patients with disorders of consciousness
Jun Zhang, Enpeng Zhang, Cong Yuan, et al.
Clinical Neurology and Neurosurgery (2022) Vol. 218, pp. 107294-107294
Closed Access | Times Cited: 13
United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration
Liming Zhong, Pinyu Huang, Hai Shu, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 231, pp. 107391-107391
Closed Access | Times Cited: 5
Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method
Ji Zhu, Xinyuan Chen, Yuxiang Liu, et al.
Radiation Oncology (2023) Vol. 18, Iss. 1
Open Access | Times Cited: 5
Contrast-agnostic deep learning–based registration pipeline: Validation in spinal cord multimodal MRI data
E. Beal, Julien Cohen‐Adad
Aperture Neuro (2023) Vol. 3, Iss. 2023, pp. 1-21
Open Access | Times Cited: 5
Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma
Siqi Yuan, Xinyuan Chen, Yuxiang Liu, et al.
Radiation Oncology (2023) Vol. 18, Iss. 1
Open Access | Times Cited: 5
Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review
Tonghe Wang, Yang Lei, Yabo Fu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 13
A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma
Xinyuan Chen, Bining Yang, Jingwen Li, et al.
Physics in Medicine and Biology (2021) Vol. 66, Iss. 22, pp. 224001-224001
Open Access | Times Cited: 11
Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments
Zhen Zhang, Chun Li, Wenhui Wang, et al.
The Innovation (2023) Vol. 5, Iss. 1, pp. 100539-100539
Open Access | Times Cited: 4
Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis
Yixuan Huang, Xiaoxuan Zhang, Yicheng Hu, et al.
Computerized Medical Imaging and Graphics (2024) Vol. 114, pp. 102365-102365
Closed Access | Times Cited: 1
A unified generation‐registration framework for improved MR‐based CT synthesis in proton therapy
Xia Li, Renato Bellotti, Barbara Bachtiary, et al.
Medical Physics (2024) Vol. 51, Iss. 11, pp. 8302-8316
Open Access | Times Cited: 1
Weakly-Supervised Biomechanically-Constrained CT/MRI Registration of the Spine
Bailiang Jian, Mohammad Farid Azampour, Francesca De Benetti, et al.
Lecture notes in computer science (2022), pp. 227-236
Open Access | Times Cited: 7
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