13 citations · 47 across the 12 of their papers we have counts for
12 papers
CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model
Shudong Li, Xiao Jiang, Matthew Tivnan +3
Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a s…
A Joint Processing Strategy for Image Quality Improvement in 3D Digital Subtraction Angiography
Xiaoxuan Zhang, Xiao Jiang, Matthew Tivnan +2
Three-dimensional digital subtraction angiography (3D-DSA) is a widely adopted technique for clinical evaluation of contrast-enhanced vasculatures. The distribution of a contrast a…
Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation
Matthew Tivnan, Jacopo Teneggi, Tzu-Cheng Lee +6
Score-based stochastic denoising models have recently been demonstrated as powerful machine learning tools for conditional and unconditional image generation. The existing methods…
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman +1
Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks. While they provide high-qual…
High-Sensitivity Iodine Imaging by Combining Spectral CT Technologies
Matthew Tivnan, Grace Gang, Wenchao Cao +3
Spectral CT offers enhanced material discrimination over single-energy systems and enables quantitative estimation of basis material density images. Water/iodine decomposition in c…
Manifold Reconstruction of Differences: A Model-Based Iterative Statistical Estimation Algorithm with a Data-Driven Prior
Matthew Tivnan, J. Webster Stayman
Manifold learning using deep neural networks been shown to be an effective tool for building sophisticated prior image models that can be applied to noise reduction in low-dose CT.…
Multi-Contrast CT Imaging with a Prototype Spatial-Spectral Filter
Matthew Tivnan, Wenying Wang, J. Webster Stayman
Spectral CT has great potential for a variety of clinical applications due to the improved material discrimination with respect to conventional CT. Many clinical and preclinical sp…
Combining Spectral CT Acquisition Methods for High-Sensitivity Material Decomposition
Matthew Tivnan, Wenying Wang, Grace Gang +3
Quantitative estimation of contrast agent concentration is made possible by spectral CT and material decomposition. There are several approaches to modulate the sensitivity of the…
Optimized Spatial-Spectral CT for Multi-Material Decomposition
Matthew Tivnan, Wenying Wang, Steven Tilley +2
Spectral CT is an emerging modality that uses a data acquisition scheme with varied spectral responses to provide enhanced material discrimination in addition to the structural inf…
Design of Spatial-Spectral Filters for CT Material Decomposition
Matthew Tivnan, Wenying Wang, Grace Gang +1
Spectral CT has shown promise for high-sensitivity quantitative imaging and material decomposition. This work presents a new device called a spatial-spectral filter (SSF) which con…
A Preconditioned Algorithm for Model-Based Iterative CT Reconstruction and Material Decomposition from Spectral CT Data
Matthew Tivnan, Wenying Wang, J. Webster Stayman
Model-based material decomposition is a statisticaliterative reconstruction framework where basis material densityimages are estimated directly from spectral CT data. This methodus…
Physical Modeling and Performance of Spatial-Spectral Filters for CT Material Decomposition
Matthew Tivnan, Steven Tilley, J. Webster Stayman
Material decomposition for imaging multiple contrast agents in a single acquisition has been made possible by spectral CT: a modality which incorporates multiple photon energy spec…