41 citations · 44 across the 5 of their papers we have counts for
5 papers
Super Phantoms: advanced models for testing medical imaging technologies
Srirang Manohar, Ioannis Sechopoulos, Mark A. Anastasio +3
Phantoms are test objects used for initial testing and optimization of medical imaging techniques, but these rarely capture the complex properties of the tissue. Here we introduce…
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…
Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis
Koen Michielsen, Nikita Moriakov, Jonas Teuwen +1
Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learning-based meth…
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
Jonas Teuwen, Nikita Moriakov, Christian Fedon +7
The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital…
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Nikita Moriakov, Koen Michielsen, Jonas Adler +3
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular r…