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Session: Brachytherapy Technology Horizon [Return to Session]

Brachytherapy Technology Horizon

X Jia1*, W Song2*, A Carlsson Tedgren3*, J Robar4*, (1) The University of Texas Southwestern Medical Ctr, Dallas, TX, (2) Virginia Commonwealth University, Henrico, VA, (3) Linkoping University, Linkoping, ,SE, (4) Nova Scotia Health Authority, Halifax, NS


4:30 PM Deep Learning and Artificial Intelligence for Brachytherapy - X Jia, Presenting Author
4:45 PM Intensity Modulated and Anisotropic Brachytherapy Sources - W Song, Presenting Author
5:00 PM Advancements in Brachytherapy Plan Optimization - Å Carlsson Tedgren, Presenting Author
5:15 PM Personalized Brachytherapy with Integration of 3D Printing Technologies - J Robar, Presenting Author

WE-F-TRACK 5-0 (Wednesday, 7/28/2021) 4:30 PM - 5:30 PM [Eastern Time (GMT-4)]

Brachytherapy is undergoing a dramatic transformation. There are tremendous advancements and automation across imaging, applicators, treatment planning, and QA. The future of brachytherapy will likely be shaped by these emerging technologies and this symposia is to take an in-depth look at what is on its horizon. First, we’ll review the application of deep learning (DL) in brachytherapy. Undoubtedly, DL based approaches has become an effective and handy tools in tackling some of the most time-consuming tasks. Specifically, its role in image processing/registration, applicator/needle digitization, organ segmentation, and plan evaluation and toxicity prediction will be looked at. Second, we’ll review the rise of cleverly designed shielded applicators that enable intensity modulated brachytherapy (IMBT). IMBT utilizes high-density shields to provide at least one additional degree of freedom in the dose delivery process (e.g., directional beam) and can result in marked improvement in plan quality. Third, we’ll review a number of recent advances that improves the brachytherapy treatment planning in different aspects. For example, improved solution algorithms for new treatment technologies (IMBT), catheter placement and total number of catheters to improve treatment plan quality, robust optimization to mitigate the effects from uncertainty, and machine learning to automate the parameter tuning in optimization models. Finally, we’ll review the state of the art in 3D printing and clinical applications as this technology has progressed in an impressive pace in the last few years. There are vast range of materials and printers to consider, and at least, one commercial solution available. It is poised to revolutionize the field and hence is a timely topic.

Learning Objectives:
1. Review of Deep Learning, Automation in Brachytherapy, including QA
2. Review of Intensity Modulated Brachytherapy and Plan Optimization
3. Review of 3D Printing in Brachytherapy and QA

Funding Support, Disclosures, and Conflict of Interest: Varian funding support.



Not Applicable / None Entered.


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