20 citations · 33 across the 16 of their papers we have counts for
17 papers
Microstructural Generative Inverse Models for Additive Manufacturing Digital Twins
Pablo Luna Falcon, Jason Mayeur, Amirkoushyar Ziabari +2
Metal additive manufacturing (AM) offers substantial advantages to the fabrication of complex structural components, but its widespread adoption is slowed due to complexities in th…
Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman +1
Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time for each projection. Reconst…
Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
Timofey Efimov, Singanallur Venkatakrishnan, Maliha Hossain +2
Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to colle…
Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction
Haley Duba-Sullivan, Aniket Pramanik, Venkatakrishnan Singanallur +1
Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures, with applications ranging from medical to indu…
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…
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…
Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing
Anika Tabassum, Amirkoushyar Ziabari
Industrial X-ray computed tomography (XCT) is a powerful tool for non-destructive characterization of materials and manufactured components. XCT commonly accompanied by advanced im…
2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts
Haley Duba-Sullivan, Obaidullah Rahman, Singanallur Venkatakrishnan +1
X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite it…
Edge Projection-Based Adaptive View Selection for Cone-Beam CT
Jingsong Lin, Singanallur Venkatakrishnan, Gregery Buzzard +2
Industrial cone-beam X-ray computed tomography (CT) scans of additively manufactured components produce a 3D reconstruction from projection measurements acquired at multiple predet…
Deep learning based workflow for accelerated industrial X-ray Computed Tomography
Obaidullah Rahman, Singanallur V. Venkatakrishnan, Luke Scime +5
X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal…
YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy
Amirkoushyar Ziabari, Derek C. Rose, Abbas Shirinifard +1
Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stackin…
Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches
S. V. Venkatakrishnan, K. Aditya Mohan, Amir Koushyar Ziabari +1
Multi-scale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. S…
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…
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…
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…
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…
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…