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

7 papers

cs.CV2025

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography

Aniket Pramanik, Obaidullah Rahman, Singanallur V. Venkatakrishnan +1

Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials an…

eess.IV2025

A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction

Aniket Pramanik, Singanallur V. Venkatakrishnan, Obaidullah Rahman +1

Industrial X-ray cone-beam CT (XCT) scanners are widely used for scientific imaging and non-destructive characterization. Industrial CBCT scanners use large detectors containing mi…

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…