activity
20182025
most cited2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation

6 citations · 10 across the 6 of their papers we have counts for

collaborators

5 papers

eess.IV2021

Model-based Reconstruction for Enhanced X-ray CT of Tri-structural Isotropic (TRISO) Particles

Singanallur V. Venkatakrishnan, Amirkoushyar Ziabari, Philip Bingham +1

Tri-Structural Isotropic (TRISO) fuel particles are a key component of next generation nuclear fuels. Using X-ray computed tomography (CT) to characterize TRISO particles is challe…

cs.LG2019

PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design

Maryam Parsa, Aayush Ankit, Amirkoushyar Ziabari +1

The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimi…

cs.CV2019

X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5D Deep Learning MBIR

Amirkoushyar Ziabari, Michael Kirka, Vincent Paquit +2

In this paper, we present a deep learning algorithm to rapidly obtain high quality CT reconstructions for AM parts. In particular, we propose to use CAD models of the parts that ar…

eess.IV20186 cited

2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation

Amirkoushyar Ziabari, Dong Hye Ye, Somesh Srivastava +3

While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown to have better image quality than Filtered Back Projection (FBP), its use has been limited by its high…

eess.IV20184 cited

Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT

Amirkoushyar Ziabari, Dong Hye Ye, Lin Fu +4

With the recent introduction of CT scanners with large cone angles, wide coverage detectors now provide a desirable scanning platform for cardiac CT that allows whole heart imaging…