65 citations · 76 across the 3 of their papers we have counts for
3 papers
eess.IV2018★ 1 cited
Deep Learning Angiography (DLA): Three-dimensional C-arm Cone Beam CT Angiography Using Deep Learning
Juan C. Montoya, Yinsheng Li, Charles Strother +1
Background and Purpose: Our purpose was to develop a deep learning angiography (DLA) method to generate 3D cerebral angiograms from a single contrast-enhanced acquisition. Material…
physics.med-ph2015★ 10 cited
A patient-specific scatter artifacts correction method
Wei Zhao, Stephen Brunner, Kai Niu +3
This paper provides a fast and patient-specific scatter artifact correction method for cone-beam computed tomography (CBCT) used in image-guided interventional procedures. Due to i…
physics.med-ph2010★ 65 cited
Anomalous scaling law for noise variance and spatial resolution in differential phase contrast computed tomography
Guang-Hong Chen, Joseph Zambelli, Zhihua Qi +1
In conventional absorption based x-ray computed tomography (CT), the noise variance in reconstructed CT images scales with spatial resolution following an inverse cubic relationshi…