36 citations · 88 across the 21 of their papers we have counts for
21 papers
Volumetric Material Decomposition Using Spectral Diffusion Posterior Sampling with a Compressed Polychromatic Forward Model
Xiao Jiang, Grace J. Gang, J. Webster Stayman
We have previously introduced Spectral Diffusion Posterior Sampling (Spectral DPS) as a framework for accurate one-step material decomposition by integrating analytic spectral syst…
CTorch: PyTorch-Compatible GPU-Accelerated Auto-Differentiable Projector Toolbox for Computed Tomography
Xiao Jiang, Grace J. Gang, J. Webster Stayman
This work introduces CTorch, a PyTorch-compatible, GPU-accelerated, and auto-differentiable projector toolbox designed to handle various CT geometries with configurable projector a…
Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography
Jacopo Teneggi, J Webster Stayman, Jeremias Sulam
Uncertainty quantification is necessary for developers, physicians, and regulatory agencies to build trust in machine learning predictors and improve patient care. Beyond measuring…
Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling
Xiao Jiang, Grace J. Gang, J. Webster Stayman
Many spectral CT applications require accurate material decomposition. Existing material decomposition algorithms are often susceptible to significant noise magnification or, in th…
Deep Learning CT Image Restoration using System Blur and Noise Models
Yijie Yuan, Grace J. Gang, J. Webster Stayman
The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities like computed tomograp…
Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model
Xiao Jiang, Shudong Li, Peiqing Teng +2
Diffusion Posterior Sampling(DPS) methodology is a novel framework that permits nonlinear CT reconstruction by integrating a diffusion prior and an analytic physical system model,…
CT Material Decomposition using Spectral Diffusion Posterior Sampling
Xiao Jiang, Grace J. Gang, J. Webster Stayman
In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements. This approach c…
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…
Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging
Steven Tilley, Matthew Jacobson, Qian Cao +4
We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in fl…
High-Fidelity Modeling of Shift-Variant Focal-Spot Blur for High-Resolution CT
Steven Tilley, Wojciech Zbijewski, J. Webster Stayman
Dedicated application-specific CT systems are popular solutions to high-resolution clinical needs. Some applications, such as mammography and extremities imaging, require spatial r…