158 citations · 366 across the 16 of their papers we have counts for
16 papers
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging
Berk Iskender, Sushan Nakarmi, Nitin Daphalapurkar +2
Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only…
Supervised Reconstruction for Silhouette Tomography
Evan Bell, Michael T. McCann, Marc Klasky
In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette…
Isotopic gamma lines for identification of shielding materials
Oleg Korobkin, Marc L. Klasky, Ajeeta Khatiwada +1
Identifying the constituting materials of concealed objects is crucial in a wide range of sectors, such as medical imaging, geophysics, nonproliferation, national security investig…
Score-based Diffusion Models for Bayesian Image Reconstruction
Michael T. McCann, Hyungjin Chung, Jong Chul Ye +1
This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can…
RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging
Berk Iskender, Marc L. Klasky, Yoram Bresler
Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography,…
Material Identification From Radiographs Without Energy Resolution
Michael T. McCann, Elena Guardincerri, Samuel M. Gonzales +3
We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, includ…
Machine Learning technique for isotopic determination of radioisotopes using HPGe -ray spectra
Ajeeta Khatiwada, Marc Klasky, Marcie Lombardi +2
-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditio…
Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
Hyungjin Chung, Dohoon Ryu, Michael T. McCann +2
Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility. They ha…
Diffusion Posterior Sampling for General Noisy Inverse Problems
Hyungjin Chung, Jeongsol Kim, Michael T. Mccann +2
Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative…
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
Berk Iskender, Marc L. Klasky, Yoram Bresler
In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly…
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Anish Lahiri, Marc Klasky, Jeffrey A. Fessler +1
Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the num…
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Maliha Hossain, Balasubramanya T. Nadiga, Oleg Korobkin +7
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics. This technique has been used in num…
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography
Zhishen Huang, Marc Klasky, Trevor Wilcox +1
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reco…
Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT
Alexander N. Sietsema, Michael T. McCann, Marc L. Klasky +1
In this work, we compare one-step and two-step approaches for X-ray computed tomography (CT) scatter correction and density reconstruction. X-ray CT is an important imaging techniq…
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
Michael T. McCann, Marc L. Klasky, Jennifer L. Schei +1
We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a train…
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
Xuehang Zheng, Saiprasad Ravishankar, Yong Long +2
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computation…