20 citations · 33 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…