1 citations · 1 across the 2 of their papers we have counts for
2 papers
eess.IV2022★ 1 cited
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
Teaghan O'Briain, Carlos Uribe, Kwang Moo Yi +3
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the op…
cs.CV2019
Reducing the Human Effort in Developing PET-CT Registration
Teaghan O'Briain, Kyong Hwan Jin, Hongyoon Choi +3
We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits. Current methods for registration rely on correspond…