1 code implementation • 29 Apr 2023 • K. Aditya Mohan, Jean-Baptiste Forien, Venkatesh Sridhar, Jefferson A. Cuadra, Dilworth Parkinson
The dominant approaches to 3D reconstruction using XPCT relies on the use of phase-retrieval algorithms that make one or more limiting approximations for the experimental configuration and material properties.
no code implementations • 27 Feb 2023 • Wenrui Li, Venkatesh Sridhar, K. Aditya Mohan, Saransh Singh, Jean-Baptiste Forien, Xin Liu, Gregery T. Buzzard, Charles A. Bouman
As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities.
no code implementations • ICCV 2023 • Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.
no code implementations • ICCV 2021 • Albert W. Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya
However, if the scene is moving too fast, then the sampling occurs along a limited view and is difficult to reconstruct due to spatiotemporal ambiguities.
no code implementations • 16 Apr 2021 • S. V. Venkatakrishnan, K. Aditya Mohan, Amir Koushyar Ziabari, Charles A. Bouman
In the first part, we will focus on model-based image reconstruction algorithms that formulate the inversion as solving a high-dimensional optimization problem involving a data-fidelity term and a regularization term.
no code implementations • 22 Dec 2020 • Alan D. Kaplan, Qi Cheng, K. Aditya Mohan, Lindsay D. Nelson, Sonia Jain, Harvey Levin, Abel Torres-Espin, Austin Chou, J. Russell Huie, Adam R. Ferguson, Michael McCrea, Joseph Giacino, Shivshankar Sundaram, Amy J. Markowitz, Geoffrey T. Manley
Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery.
1 code implementation • 29 Oct 2020 • K. Aditya Mohan, Alan D. Kaplan
AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to compress the information contained within each partition.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley
Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle M. Champley
Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved.
2 code implementations • 10 May 2019 • K. Aditya Mohan, Robert M. Panas, Jefferson A. Cuadra
Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast.
no code implementations • CVPR 2018 • Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle Champley, Timo Bremer
The classical techniques require measuring projections, called sinograms, from a full 180$^\circ$ view of the object.