K
K. Aditya Mohan
Researcher at Lawrence Livermore National Laboratory
Publications - 34
Citations - 380
K. Aditya Mohan is an academic researcher from Lawrence Livermore National Laboratory. The author has contributed to research in topics: Iterative reconstruction & Engineering. The author has an hindex of 8, co-authored 27 publications receiving 253 citations. Previous affiliations of K. Aditya Mohan include Purdue University.
Papers
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Journal ArticleDOI
TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views
K. Aditya Mohan,Singanallur Venkatakrishnan,John W. Gibbs,Emine Begum Gulsoy,Xianghui Xiao,Marc De Graef,Peter W. Voorhees,Charles A. Bouman +7 more
TL;DR: Recon reconstructions of both simulated and real X-ray synchrotron data are presented, which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.
Proceedings ArticleDOI
Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Rushil Anirudh,Hyo Jin Kim,Jayaraman J. Thiagarajan,K. Aditya Mohan,Kyle Champley,Timo Bremer +5 more
TL;DR: In this paper, a system of 1D and 2D convolutional neural networks is proposed to predict the reconstruction of a sinogram from a limited angle view of the object, and then use the x-ray transform on this prediction to obtain a completed sinogram.
Proceedings ArticleDOI
Model-based iterative reconstruction for synchrotron X-ray tomography
K. Aditya Mohan,Singanallur Venkatakrishnan,Lawrence F. Drummy,Jeff Simmons,Dilworth Y. Parkinson,Charles A. Bouman +5 more
TL;DR: A model-based iterative reconstruction (MBIR) algorithm for synchrotron X-ray tomography that can automatically handle the non-idealities as a part of the reconstruction and is superior to the analytical reconstructions effectively suppressing noise as well as other artifacts.
Journal ArticleDOI
Method for system-independent material characterization from spectral X-ray CT
TL;DR: In this article, a method for material characterization using Spectral X-ray Computed tomography (SCT) is proposed, which takes advantage of recently developed MultiX ME 100 photon counting detectors to simultaneously measure the energy dependence of a material's linear attenuation coefficient (LAC).
Journal ArticleDOI
3D reconstruction of the magnetic vector potential using model based iterative reconstruction.
TL;DR: In this article, a model-based iterative reconstruction (MBIR) algorithm is proposed to reconstruct the magnetic vector potential of magnetic nanoparticles from their corresponding LTEM images.