Y
Yong Yang
Researcher at Stanford University
Publications - 64
Citations - 2235
Yong Yang is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 21, co-authored 50 publications receiving 1990 citations. Previous affiliations of Yong Yang include Peking Union Medical College.
Papers
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Journal ArticleDOI
Overview of image-guided radiation therapy.
Lei Xing,B. Thorndyke,Eduard Schreibmann,Yong Yang,Tian Fang Li,Gwe-Ya Kim,Gary Luxton,Albert C. Koong +7 more
TL;DR: The purpose of this article is to summarize recent advancements in IGRT and discussed various practical issues related to the implementation of the new imaging techniques available to radiation oncology community.
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Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation*
TL;DR: The CBCT can be employed directly for dose calculation for a disease site such as the prostate, where there is little motion artefact and a large discrepancy between the original treatment plan and the CBCT (or mCBCT)-based calculation is noted.
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Model-based image reconstruction for four-dimensional PET
TL;DR: A method to enhance the performance of 4D PET by developing a new technique of4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm is proposed.
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Four-dimensional cone-beam computed tomography using an on-board imager.
Tianfang Li,Lei Xing,Peter Munro,C. McGuinness,Ming Chao,Yong Yang,Billy W. Loo,Albert C. Koong +7 more
TL;DR: This work quantitatively study the influence of organ motion on CBCT imaging and investigates a strategy to acquire high quality phase-resolved [four-dimensional (4D)] CBCT images based on phase binning of the CBCT projection data.
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Optimization of radiotherapy dose-time fractionation with consideration of tumor specific biology
Yong Yang,Lei Xing +1 more
TL;DR: The results showed that, for fast proliferating tumors, the optimum overall time is similar to the assumed kickoff time T(k) and almost independent of interval patterns, and the proposed technique provides a useful tool to systematically optimize radiotherapy for fast and slow proliferation tumors and sheds important insight into the complex problem of dose-time fractionation.