Search Results for author: K. Aditya Mohan

Found 11 papers, 3 papers with code

Maximum Likelihood based Phase-Retrieval using Fresnel Propagation Forward Models with Optional Constraints

1 code implementation29 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.

3D Reconstruction Retrieval

X-ray Spectral Estimation using Dictionary Learning

no code implementations27 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.

Computed Tomography (CT) Dictionary Learning

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

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.

Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields

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.

Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches

no code implementations16 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.

Computed Tomography (CT) Image Reconstruction

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

no code implementations22 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.

AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning

1 code implementation29 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.

Feature Importance Representation Learning

Improving Limited Angle CT Reconstruction with a Robust GAN Prior

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.

SABER: A Systems Approach to Blur Estimation and Reduction in X-ray Imaging

2 code implementations10 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.

Deblurring

Cannot find the paper you are looking for? You can Submit a new open access paper.