@article{3a1b58547c194479a681f9e31a5010ee,
title = "Artificial intelligence can overcome challenges in brachytherapy treatment planning",
author = "Xun Jia and Cunha, {J. Adam M.} and Yi Rong",
note = "Funding Information: Dr. Xun Jia is Professor and Associate Vice Chair of Medical Physics Research at the Department of Radiation Oncology, University of Texas Southwestern Medical Center (UTSW). He received his master's degree in applied mathematics in 2007 and Ph.D. degree in physics in 2009, both from the University of California Los Angeles. After his postdoctoral training in medical physics from the Department of Radiation Physics and Applied Sciences, University of California San Diego, he became a faculty in the same department in 2011. He moved to UTSW in 2013. Over the years, Dr. Jia has conducted productive research on cone beam CT reconstruction, GPU‐based Monte Carlo radiation transport simulation, deep learning for image processing and RT treatment planning, and development of a preclinical small animal radiation research platform. He has published ∼150 peer‐reviewed manuscripts. His research has been funded by NIH, the State of Texas, industrial, and charitable funding agencies. Dr. Jia currently serves as an Executive Editorial board member of Physics in Medicine and Biology. He is the recipient of John Laughlin Young Scientist Award of American Association of Physicists in Medicine in 2017. ",
year = "2022",
month = jan,
doi = "10.1002/acm2.13504",
language = "English (US)",
volume = "23",
journal = "Journal of applied clinical medical physics",
issn = "1526-9914",
publisher = "American Institute of Physics Publising LLC",
number = "1",
}