Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Bai, Ti"'
Autor:
Balagopal, Anjali, Dohopolski, Michael, Kwon, Young Suk, Montalvo, Steven, Morgan, Howard, Bai, Ti, Nguyen, Dan, Liang, Xiao, Zhong, Xinran, Lin, Mu-Han, Desai, Neil, Jiang, Steve
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IP
Externí odkaz:
http://arxiv.org/abs/2302.01493
Autor:
Zhi, Shaohua, Wang, Yinghui, Xiao, Haonan, Bai, Ti, Ge, Hong, Li, Bing, Liu, Chenyang, Li, Wen, Li, Tian, Cai, Jing
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers from low spatial resolution and strong motion artifacts owing to the l
Externí odkaz:
http://arxiv.org/abs/2211.11144
When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and ap
Externí odkaz:
http://arxiv.org/abs/2211.10588
Autor:
Wang, Biling, Dohopolski, Michael, Bai, Ti, Wu, Junjie, Hannan, Raquibul, Desai, Neil, Garant, Aurelie, Yang, Daniel, Nguyen, Dan, Lin, Mu-Han, Timmerman, Robert, Wang, Xinlei, Jiang, Steve
We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its efficacy with changes in clinical landscapes. Our study involved 1328 pros
Externí odkaz:
http://arxiv.org/abs/2210.05673
Autor:
Dohopolski, Michael, Wang, Kai, Wang, Biling, Bai, Ti, Nguyen, Dan, Sher, David, Jiang, Steve, Wang, Jing
Prediction uncertainty estimation has clinical significance as it can potentially quantify prediction reliability. Clinicians may trust 'blackbox' models more if robust reliability information is available, which may lead to more models being adopted
Externí odkaz:
http://arxiv.org/abs/2210.00589
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly du
Externí odkaz:
http://arxiv.org/abs/2206.03413
Autor:
Bai, Ti, Lin, Muhan, Liang, Xiao, Wang, Biling, Dohopolski, Michael, Cai, Bin, Nguyen, Dan, Jiang, Steve
Medical image registration is a fundamental and vital task which will affect the efficacy of many downstream clinical tasks. Deep learning (DL)-based deformable image registration (DIR) methods have been investigated, showing state-of-the-art perform
Externí odkaz:
http://arxiv.org/abs/2203.04295
Autor:
Liang, Xiao, Chun, Jaehee, Morgan, Howard, Bai, Ti, Nguyen, Dan, Park, Justin C., Jiang, Steve
Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images. Propagating expert-drawn contours from the pre-treatment planning
Externí odkaz:
http://arxiv.org/abs/2202.03978
Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting energy properties of the scanned object. Due to the limited photon numbers and the non-ideal detector response of each energy chann
Externí odkaz:
http://arxiv.org/abs/2201.10294
Autor:
Zhao, Hengrui, Liang, Xiao, Meng, Boyu, Dohopolski, Michael, Choi, Byongsu, Cai, Bin, Lin, Mu-Han, Bai, Ti, Nguyen, Dan, Jiang, Steve
Publikováno v:
In Physics and Imaging in Radiation Oncology July 2024 31
