Score-based diffusion models as principled priors for inverse imaging
Priors are essential for reconstructing images from noisy and/or incomplete measurements.
The choice of the prior determines both the quality and uncertainty of recovered images. We …
The choice of the prior determines both the quality and uncertainty of recovered images. We …
Learning to optimize: A primer and a benchmark
Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …
develop optimization methods, aiming at reducing the laborious iterations of hand …
Data-and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended
scanning times often compromise patient comfort and image quality, especially in …
scanning times often compromise patient comfort and image quality, especially in …
Alternating learning approach for variational networks and undersampling pattern in parallel MRI applications
This work proposes an alternating learning approach to learn the sampling pattern (SP) and
the parameters of variational networks (VN) in accelerated parallel magnetic resonance …
the parameters of variational networks (VN) in accelerated parallel magnetic resonance …
Learning task-specific strategies for accelerated MRI
Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual
information from subsampled measurements for diagnostic tasks. Traditional CS-MRI …
information from subsampled measurements for diagnostic tasks. Traditional CS-MRI …
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended
scanning times often compromise patient comfort and image quality, especially in …
scanning times often compromise patient comfort and image quality, especially in …
Symbolic learning to optimize: Towards interpretability and scalability
Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and
accelerating the optimization procedure for complicated tasks. Existing L2O models …
accelerating the optimization procedure for complicated tasks. Existing L2O models …
Segmentation-aware MRI subsampling for efficient cardiac MRI reconstruction with reinforcement learning
R Xu, I Oksuz - Image and Vision Computing, 2024 - Elsevier
Abstract Magnetic Resonance Imaging (MRI) scans, though highly detailed and non-
invasive, take significantly longer than Computed Tomography (CT) scans and are sensitive …
invasive, take significantly longer than Computed Tomography (CT) scans and are sensitive …
Probabilistic Bayesian optimal experimental design using conditional normalizing flows
Bayesian optimal experimental design (OED) seeks to conduct the most informative
experiment under budget constraints to update the prior knowledge of a system to its …
experiment under budget constraints to update the prior knowledge of a system to its …
Adaptive model‐based Magnetic Resonance
I Beracha, A Seginer, A Tal - Magnetic Resonance in Medicine, 2023 - Wiley Online Library
Purpose Conventional sequences are static in nature, fixing measurement parameters in
advance in anticipation of a wide range of expected tissue parameter values. We set out to …
advance in anticipation of a wide range of expected tissue parameter values. We set out to …