Relevant bibliographies by topics / MRI-linear accelerator

Academic literature on the topic 'MRI-linear accelerator'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "MRI-linear accelerator"

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Liney, G. P., B. Whelan, B. Oborn, M. Barton, and P. Keall. "MRI-Linear Accelerator Radiotherapy Systems." Clinical Oncology 30, no. 11 (November 2018): 686–91. http://dx.doi.org/10.1016/j.clon.2018.08.003.

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Crijns, S. P. M., J. G. M. Kok, J. J. W. Lagendijk, and B. W. Raaymakers. "Towards MRI-guided linear accelerator control: gating on an MRI accelerator." Physics in Medicine and Biology 56, no. 15 (July 13, 2011): 4815–25. http://dx.doi.org/10.1088/0031-9155/56/15/012.

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Randall, James W., Nikhil Rammohan, Indra J. Das, and Poonam Yadav. "Towards Accurate and Precise Image-Guided Radiotherapy: Clinical Applications of the MR-Linac." Journal of Clinical Medicine 11, no. 14 (July 13, 2022): 4044. http://dx.doi.org/10.3390/jcm11144044.

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Advances in image-guided radiotherapy have brought about improved oncologic outcomes and reduced toxicity. The next generation of image guidance in the form of magnetic resonance imaging (MRI) will improve visualization of tumors and make radiation treatment adaptation possible. In this review, we discuss the role that MRI plays in radiotherapy, with a focus on the integration of MRI with the linear accelerator. The MR linear accelerator (MR-Linac) will provide real-time imaging, help assess motion management, and provide online adaptive therapy. Potential advantages and the current state of these MR-Linacs are highlighted, with a discussion of six different clinical scenarios, leading into a discussion on the future role of these machines in clinical workflows.
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Graham, J., G. Redler, K. DeLozier, H. H. M. Yu, D. E. Oliver, and S. A. Rosenberg. "Dosimetric Feasibility of HA-WBRT With an MRI-Guided Linear Accelerator." International Journal of Radiation Oncology*Biology*Physics 111, no. 3 (November 2021): e511-e512. http://dx.doi.org/10.1016/j.ijrobp.2021.07.1403.

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Blinde, S., A. S. R. Mohamed, A. Al-Mamgani, K. Newbold, I. Karam, J. R. Robbins, D. Thomson, N. Raaijmakers, C. D. Fuller, and C. Terhaard. "Interobserver Variation in the International MRI Linear Accelerator Oropharyngeal Carcinoma Delineation Study." International Journal of Radiation Oncology*Biology*Physics 100, no. 5 (April 2018): 1362. http://dx.doi.org/10.1016/j.ijrobp.2017.12.143.

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Bol, G. H., S. Hissoiny, J. J. W. Lagendijk, and B. W. Raaymakers. "Fast online Monte Carlo-based IMRT planning for the MRI linear accelerator." Physics in Medicine and Biology 57, no. 5 (February 21, 2012): 1375–85. http://dx.doi.org/10.1088/0031-9155/57/5/1375.

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Wang, Michael H., Anthony Kim, Mark Ruschin, Hendrick Tan, Hany Soliman, Sten Myrehaug, Jay Detsky, et al. "Comparison of Prospectively Generated Glioma Treatment Plans Clinically Delivered on Magnetic Resonance Imaging (MRI)-Linear Accelerator (MR-Linac) Versus Conventional Linac: Predicted and Measured Skin Dose." Technology in Cancer Research & Treatment 21 (January 2022): 153303382211246. http://dx.doi.org/10.1177/15330338221124695.

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Introduction: Magnetic resonance imaging-linear accelerator radiotherapy is an innovative technology that requires special consideration for secondary electron interactions within the magnetic field, which can alter dose deposition at air–tissue interfaces. As part of ongoing quality assurance and quality improvement of new radiotherapy technologies, the purpose of this study was to evaluate skin dose modelled from the treatment planning systems of a magnetic resonance imaging-linear accelerator and a conventional linear accelerator, and then correlate with in vivo measurements of delivered skin dose from each linear accelerator. Methods: In this prospective cohort study, 37 consecutive glioma patients had treatment planning completed and approved prior to radiotherapy initiation using commercial treatment planning systems: a Monte Carlo-based algorithm for magnetic resonance imaging-linear accelerator or a convolution-based algorithm for conventional linear accelerator. In vivo skin dose was measured using an optically stimulated luminescent dosimeter. Results: Monte Carlo-based magnetic resonance imaging-linear accelerator plans and convolution-based conventional linear accelerator plans had similar dosimetric parameters for target volumes and organs-at-risk. However, magnetic resonance imaging-linear accelerator plans had 1.52 Gy higher mean dose to air cavities ( P < .0001) and 1.10 Gy higher mean dose to skin ( P < .0001). In vivo skin dose was 14.5% greater for magnetic resonance imaging-linear accelerator treatments ( P = .0027), and was more accurately predicted by Monte Carlo-based calculation ( ρ = 0.95, P < .0001) versus convolution-based ( ρ = 0.80, P = .0096). Conclusion: This is the first prospective dosimetric comparison of glioma patients clinically treated on both magnetic resonance imaging-linear accelerator and conventional linear accelerator. Our findings suggest that skin doses were significantly greater with magnetic resonance imaging-linear accelerator plans but correlated better with in vivo measurements of actual skin dose from delivered treatments. Future magnetic resonance imaging-linear accelerator planning processes are being designed to account for skin dosimetry and treatment delivery.
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Woodings, Simon J., J. H. Wilfred Vries, Jan M. G. Kok, Sara L. Hackett, Bram Asselen, Johanna J. Bluemink, Helena M. Zijp, et al. "Acceptance procedure for the linear accelerator component of the 1.5 T MRI‐linac." Journal of Applied Clinical Medical Physics 22, no. 8 (July 17, 2021): 45–59. http://dx.doi.org/10.1002/acm2.13068.

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Williams, C. L., E. Huynh, J. Campbell, J. Penney, S. Boyle, I. Usta, E. Neubauer Sugar, et al. "Initial Experience With Online Adaptive Radiotherapy Workflows on an MRI-guided Linear Accelerator." International Journal of Radiation Oncology*Biology*Physics 108, no. 3 (November 2020): e348. http://dx.doi.org/10.1016/j.ijrobp.2020.07.2327.

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Carlone, M., M. Lamey, S. Steciw, B. Burke, and B. Fallone. "TH-C-L100J-02: Study of RF Interference Between a Linear Accelerator and MRI." Medical Physics 34, no. 6Part22 (June 2007): 2621. http://dx.doi.org/10.1118/1.2761640.

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Book chapters on the topic "MRI-linear accelerator"

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Chang, David S., Foster D. Lasley, Indra J. Das, Marc S. Mendonca, and Joseph R. Dynlacht. "MRI-Linear Accelerator (MRL)." In Basic Radiotherapy Physics and Biology, 175–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61899-5_17.

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Conference papers on the topic "MRI-linear accelerator"

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Stanescu, Teo, and David Jaffray. "Development and clinical implementation of a hybrid system consisting of an MRI and medical linear accelerator." In 2017 11th European Conference on Antennas and Propagation (EUCAP). IEEE, 2017. http://dx.doi.org/10.23919/eucap.2017.7928336.

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Gu, Hongyi, Burhaneddin Yaman, Steen Moeller, Il Yong Chun, and Mehmet Akcakaya. "Accelerated MRI with Deep Linear Convolutional Transform Learning." In 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2022. http://dx.doi.org/10.1109/iemcon56893.2022.9946548.

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