3 citations · 4 across the 3 of their papers we have counts for
3 papers
physics.med-ph2023★ 1 cited
Observer study-based evaluation of TGAN architecture used to generate oncological PET images
Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu +9
The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack…
eess.IV2023★ 3 cited
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images
Shadab Ahamed, Arman Rahmim
Automated segmentation of cancerous lesions in PET/CT images is a vital initial task for quantitative analysis. However, it is often challenging to train deep learning-based segmen…
physics.med-ph2023
Lu SPECT Imaging in the Presence of Y: Does Y Degrade Image Quantification? A Simulation Study
Cassandra Miller, Carlos Uribe, Xinchi Hou +2
This work aims to investigate the accuracy of quantitative SPECT imaging of Lu in the presence of Y, which occurs in dual-isotope radiopharmaceutical therapy (RPT) i…