14 citations · 20 across the 16 of their papers we have counts for
17 papers
ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
Yixuan Jia, Siyi Chen, Yida Pan +9
Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and clim…
MCLR: Improving Conditional Modeling via Inter-Class Likelihood-Ratio Maximization and Unifying Classifier-Free Guidance with Alignment Objectives
Xiang Li, Yixuan Jia, Xiao Li +3
Diffusion models achieve strong generative performance but often rely on classifier-free guidance (CFG), an inference-time intervention that modifies the sampling trajectory. Since…
Scan-Adaptive Dynamic MRI Undersampling Using a Dictionary of Efficiently Learned Patterns
Siddhant Gautam, Angqi Li, Prachi P. Agarwal +4
Cardiac MRI is limited by long acquisition times, which can lead to patient discomfort and motion artifacts. We aim to accelerate Cartesian dynamic cardiac MRI by learning efficien…
Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction
Taewon Yang, Jason Hu, Jeffrey A. Fessler +1
Diffusion models learn strong image priors that can be leveraged to solve inverse problems like medical image reconstruction. However, for real-world applications such as 3D Comput…
A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers
Tao Hong, Umberto Villa, Jeffrey A. Fessler
Model-based reconstruction plays a key role in compressed sensing (CS) MRI, as it incorporates effective image regularizers to improve the quality of reconstruction. The Plug-and-P…
ALPCAHUS: Subspace Clustering for Heteroscedastic Data
Javier Salazar Cavazos, Jeffrey A Fessler, Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend PCA to the union of subspace (UoS) sett…
Smooth optimization using global and local low-rank regularizers
Rodrigo A. Lobos, Javier Salazar Cavazos, Raj Rao Nadakuditi +1
Many inverse problems and signal processing problems involve low-rank regularizers based on the nuclear norm. Commonly, proximal gradient methods (PGM) are adopted to solve this ty…
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction
Hongze Yu, Jeffrey A. Fessler, Yun Jiang
Deep Learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often genera…
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Siyi Chen, Yixuan Jia, Qing Qu +2
Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman, particle) presuppose full know…
Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction
Tao Hong, Zhaoyi Xu, Jason Hu +1
Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, nonsmooth, and sometimes…
Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problems
Jason Hu, Bowen Song, Jeffrey A. Fessler +1
Diffusion models have achieved excellent success in solving inverse problems due to their ability to learn strong image priors, but existing approaches require a large training dat…
Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction
Xiaojian Xu, Marc Klasky, Michael T. McCann +2
Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inerti…
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views
Zongyu Li, Yixuan Jia, Xiaojian Xu +3
Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised l…
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
Bowen Song, Jason Hu, Zhaoxu Luo +2
Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT). Due to the demanding m…
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
Jason Hu, Bowen Song, Xiaojian Xu +2
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and r…
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction
Tao Hong, Xiaojian Xu, Jason Hu +1
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of…
Streaming Heteroscedastic Probabilistic PCA with Missing Data
Kyle Gilman, David Hong, Jeffrey A. Fessler +1
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data ar…