11 citations · 28 across the 5 of their papers we have counts for
5 papers
Physics-driven Deep Learning for PET/MRI
Abhejit Rajagopal, Andrew P. Leynes, Nicholas Dwork +3
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which ha…
Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstruction
Andrew P. Leynes, Nikhil Deveshwar, Srikantan S. Nagarajan +1
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising tech…
Development of Conditional Random Field Insert for UNet-based Zonal Prostate Segmentation on T2-Weighted MRI
Peng Cao, Susan M. Noworolski, Olga Starobinets +6
Purpose: A conventional 2D UNet convolutional neural network (CNN) architecture may result in ill-defined boundaries in segmentation output. Several studies imposed stronger constr…
Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning pseudo-CT and Maximum Likelihood Estimation of Activity and Attenuation
Andrew P. Leynes, Sangtae P. Ahn, Kristen A. Wangerin +4
A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) and uncer…
Simultaneous Segmentation and Relaxometry for MRI through Multitask Learning
Peng Cao, Jing Liu, Shuyu Tang +4
Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversi…