Search Results for author: Charles A. Bouman

Found 38 papers, 2 papers with code

Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects

no code implementations25 Jun 2024 Maliha Hossain, Charles A. Bouman, Brendt Wohlberg

Flash X-ray computed tomography (CT) is an important imaging modality for characterization of high-speed dynamic events, such as Kolsky bar impact experiments for the study of mechanical properties of materials subjected to impulsive forces.

Computed Tomography (CT)

Pixel-weighted Multi-pose Fusion for Metal Artifact Reduction in X-ray Computed Tomography

no code implementations25 Jun 2024 Diyu Yang, Craig A. J. Kemp, Soumendu Majee, Gregery T. Buzzard, Charles A. Bouman

X-ray computed tomography (CT) reconstructs the internal morphology of a three dimensional object from a collection of projection images, most commonly using a single rotation axis.

Computed Tomography (CT) Metal Artifact Reduction +1

CLAMP: Majorized Plug-and-Play for Coherent 3D LIDAR Imaging

no code implementations19 Jun 2024 Tony G. Allen, David J. Rabb, Gregery T. Buzzard, Charles A. Bouman

CLAMP introduces an FFT-based method to account for the effects of the aperture and uses majorization of the forward model for computational efficiency.

Computational Efficiency Image Reconstruction

MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data

no code implementations1 Feb 2024 Natalie M. Jadue, Madhuri Nagare, Jonathan S. Maltz, Gregery T. Buzzard, Charles A. Bouman

Current commercial and prototype clinical photon counting CT systems utilize PCD-CT reconstruction methods that either reconstruct from each spectral bin separately, or first create an estimate of a material sinogram using a specified set of basis materials and then reconstruct from these material sinograms.

Computed Tomography (CT)

Texture Matching GAN for CT Image Enhancement

no code implementations20 Dec 2023 Madhuri Nagare, Gregery T. Buzzard, Charles A. Bouman

However, naive application of DNN-based methods can result in image texture that is undesirable in clinical applications.

Computed Tomography (CT) Generative Adversarial Network +1

Design of Novel Loss Functions for Deep Learning in X-ray CT

no code implementations23 Sep 2023 Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare, Charles A. Bouman, Roman Melnyk, Jie Tang, Brian Nett

Particularly in a field such as X-ray CT, where radiologists' subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively.

Computed Tomography (CT)

Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography

no code implementations23 Sep 2023 Obaidullah Rahman, Ken D. Sauer, Charles A. Bouman, Roman Melnyk, Brian Nett

But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction.

Generative Plug and Play: Posterior Sampling for Inverse Problems

1 code implementation12 Jun 2023 Charles A. Bouman, Gregery T. Buzzard

In this paper, we introduce Generative Plug-and-Play (GPnP), a generalization of PnP to sample from the posterior distribution.

Image Denoising

Dynamic DH-MBIR for Phase-Error Estimation from Streaming Digital-Holography Data

no code implementations5 May 2023 Ali G. Sheikh, Casey J. Pellizzari, Sherman J. Kisner, Gregery T. Buzzard, Charles A. Bouman

These phase error estimates must be computed with very low latency to keep pace with changing atmospheric parameters, which requires that phase errors be estimated in a single shot of DH data.

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

Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography

no code implementations27 Feb 2023 Mohammad Samin Nur Chowdhury, Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman

The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain.

TRINIDI: Time-of-Flight Resonance Imaging with Neutrons for Isotopic Density Inference

no code implementations24 Feb 2023 Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman

We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel.

Ringing Artifact Reduction Method for Ultrasound Reconstruction Using Multi-Agent Consensus Equilibrium

no code implementations9 Feb 2023 Abdulrahman M. Alanazi, Singanallur Venkatakrishnan, Gregery T. Buzzard, Charles A. Bouman

Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data.

Image Reconstruction

An Edge Alignment-based Orientation Selection Method for Neutron Tomography

no code implementations1 Dec 2022 Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Mohammad S. N. Chowdhury, Yuxuan Zhang, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman

Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences.

Diversity

Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium

no code implementations15 Sep 2022 Diyu Yang, Craig A. J. Kemp, Gregery T. Buzzard, Charles A. Bouman

In this paper, we present Multi-Pose Fusion, a novel algorithm that performs a joint tomographic reconstruction from CT scans acquired from multiple poses of a single object, where each pose has a distinct rotation axis.

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging

no code implementations31 Mar 2022 Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard, Brendt Wohlberg

Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.

Sparse-View CT Reconstruction using Recurrent Stacked Back Projection

no code implementations9 Dec 2021 Wenrui Li, Gregery T. Buzzard, Charles A. Bouman

Sparse-view CT reconstruction is important in a wide range of applications due to limitations on cost, acquisition time, or dosage.

Projected Multi-Agent Consensus Equilibrium for Ptychographic Image Reconstruction

no code implementations28 Nov 2021 Qiuchen Zhai, Brendt Wohlberg, Gregery T. Buzzard, Charles A. Bouman

Ptychography is a computational imaging technique using multiple, overlapping, coherently illuminated snapshots to achieve nanometer resolution by solving a nonlinear phase-field recovery problem.

Image Reconstruction

CodEx: A Modular Framework for Joint Temporal De-blurring and Tomographic Reconstruction

no code implementations11 Nov 2021 Soumendu Majee, Selin Aslan, Doga Gursoy, Charles A. Bouman

The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm.

Computed Tomography (CT) Deblurring +1

Hyperspectral Neutron CT with Material Decomposition

no code implementations6 Oct 2021 Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman

Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material.

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

Ultra-Sparse View Reconstruction for Flash X-Ray Imaging using Consensus Equilibrium

no code implementations29 Mar 2021 Maliha Hossain, Shane C. Paulson, Hangjie Liao, Weinong W. Chen, Charles A. Bouman

A growing number of applications require the reconstructionof 3D objects from a very small number of views.

Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction

no code implementations1 Aug 2020 Soumendu Majee, Thilo Balke, Craig A. J. Kemp, Gregery T. Buzzard, Charles A. Bouman

In this paper, we present multi-slice fusion, a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers.

4D reconstruction Denoising

Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography

no code implementations26 Jan 2020 Xiao Wang, Robert D. MacDougall, Peng Chen, Charles A. Bouman, Simon K. Warfield

Our algorithm uses precise physics models to reconstruct from the native cone-beam geometry and interleaved dual source helical trajectory of a DS-FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture.

Computed Tomography (CT)

4D X-Ray CT Reconstruction using Multi-Slice Fusion

no code implementations15 Jun 2019 Soumendu Majee, Thilo Balke, Craig A. J. Kemp, Gregery T. Buzzard, Charles A. Bouman

In this paper, we present Multi-Slice Fusion, a novel algorithm for 4D and higher-dimensional reconstruction, based on the fusion of multiple low-dimensional denoisers.

4D reconstruction Denoising +1

Deep Back Projection for Sparse-View CT Reconstruction

no code implementations6 Jul 2018 Dong Hye Ye, Gregery T. Buzzard, Max Ruby, Charles A. Bouman

Filtered back projection (FBP) is a classical method for image reconstruction from sinogram CT data.

Image Reconstruction

SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks

1 code implementation8 Mar 2018 Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak

From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar.

Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling

no code implementations27 Jun 2017 Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage.

Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework

no code implementations9 Jun 2017 Casey J. Pellizzari, Mark F. Spencer, Charles A. Bouman

The estimation of phase errors from digital-holography data is critical for applications such as imaging or wave-front sensing.

Image Reconstruction

Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium

no code implementations24 May 2017 Gregery T. Buzzard, Stanley H. Chan, Suhas Sreehari, Charles A. Bouman

We give examples to illustrate consensus equilibrium and the convergence properties of these algorithms and demonstrate this method on some toy problems and on a denoising example in which we use an array of convolutional neural network denoisers, none of which is tuned to match the noise level in a noisy image but which in consensus can achieve a better result than any of them individually.

Denoising Image Reconstruction

Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

no code implementations CVPR 2017 Yandong Guo, Cheng Lu, Jan P. Allebach, Charles A. Bouman

Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio.

Dictionary Learning Image Compression

A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)

no code implementations14 Mar 2017 G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Michael D. Uchic, Michael A. Groeber, Gregery T. Buzzard, Charles A. Bouman

In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements.

regression

Multi-resolution Data Fusion for Super-Resolution Electron Microscopy

no code implementations28 Nov 2016 Suhas Sreehari, S. V. Venkatakrishnan, Katherine L. Bouman, Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman

Consequently, there is an enormous demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution.

Super-Resolution

A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT

no code implementations12 May 2016 Ruoqiao Zhang, Dong Hye Ye, Debashish Pal, Jean-Baptiste Thibault, Ken D. Sauer, Charles A. Bouman

In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction.

Image Denoising

Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

no code implementations23 Dec 2015 Suhas Sreehari, S. V. Venkatakrishnan, Brendt Wohlberg, Lawrence F. Drummy, Jeffrey P. Simmons, Charles A. Bouman

The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation.

Denoising Electron Tomography

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