21 citations · 39 across the 32 of their papers we have counts for
29 papers · 1 filter
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…
Dynamic MRI Reconstruction Via Dual Deep Priors and Low-Rank Plus Sparse Modeling
Yongliang Sun, Siddhant Gautam, Chaoyan Huang +3
Dynamic MRI reconstruction from undersampled measurements is a challenging inverse problem that requires preserving both spatial reconstruction quality and temporal consistency acr…
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…
Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
Evan Bell, Shijun Liang, Ismail Alkhouri +1
Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstr…
NERD: Network-Regularized Diffusion Sampling For 3D Computed Tomography
Shijun Liang, Ismail Alkhouri, Qing Qu +2
Numerous diffusion model (DM)-based methods have been proposed for solving inverse imaging problems. Among these, a recent line of work has demonstrated strong performance by formu…
Learning Scan-Adaptive MRI Undersampling Patterns with Pre-Optimized Mask Supervision
Aryan Dhar, Siddhant Gautam, Saiprasad Ravishankar
Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolut…