no code implementations • NAACL 2022 • Luyao Shi, Tanveer Syeda-Mahmood, Tyler Baldwin
However, these methods still lack semantic understanding of the underlying clinical conditions as well as ruled out findings, resulting in poor precision during retrieval.
no code implementations • 14 Feb 2024 • Xueqi Guo, Luyao Shi, Xiongchao Chen, Qiong Liu, Bo Zhou, Huidong Xie, Yi-Hwa Liu, Richard Palyo, Edward J. Miller, Albert J. Sinusas, Lawrence H. Staib, Bruce Spottiswoode, Chi Liu, Nicha C. Dvornek
Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases.
1 code implementation • 23 Aug 2023 • Xueqi Guo, Luyao Shi, Xiongchao Chen, Bo Zhou, Qiong Liu, Huidong Xie, Yi-Hwa Liu, Richard Palyo, Edward J. Miller, Albert J. Sinusas, Bruce Spottiswoode, Chi Liu, Nicha C. Dvornek
The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable.
no code implementations • 24 Jun 2022 • Huidong Xie, Zhao Liu, Luyao Shi, Kathleen Greco, Xiongchao Chen, Bo Zhou, Attila Feher, John C. Stendahl, Nabil Boutagy, Tassos C. Kyriakides, Ge Wang, Albert J. Sinusas, Chi Liu
In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation.
no code implementations • MIDL 2019 • Luyao Shi, Deepta Rajan, Shafiq Abedin, Manikanta Srikar Yellapragada, David Beymer, Ehsan Dehghan
In addition to the classification loss, an attention loss was added during training to help the network focus attention on PE.
no code implementations • 3 Sep 2019 • Luyao Shi, John A. Onofrey, Enette Mae Revilla, Takuya Toyonaga, David Menard, Jo-seph Ankrah, Richard E. Carson, Chi Liu, Yihuan Lu
Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map ($\mu$-CNN) from $\lambda$-MLAA and $\mu$-MLAA, in which an image-domain loss (IM-loss) function between the $\mu$-CNN and the ground truth $\mu$-CT was used.