1 code implementation • 20 Jul 2022 • Siyuan Dong, Gilbert Hangel, Eric Z. Chen, Shanhui Sun, Wolfgang Bogner, Georg Widhalm, Chenyu You, John A. Onofrey, Robin de Graaf, James S. Duncan
Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI.
1 code implementation • 10 Jun 2022 • Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Jiazhen Zhang, Albert J. Sinusas, John A. Onofrey, Chi Liu
In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived u-maps.
1 code implementation • International Conference on Medical Image Computing and Computer-Assisted Intervention 2021 • Ricardo A. Gonzales, Jérôme Lamy, Felicia Seemann, Einar Heiberg, John A. Onofrey, Dana C. Peters
Tracking the tricuspid valve (TV) in magnetic resonance imaging (MRI) long-axis cine images has the potential to aid in the evaluation of right ventricular dysfunction, which is common in congenital heart disease and pulmonary hypertension.
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.