Search Results for author:
Found 7 papers, 3 papers with code
Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging.
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains.
Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis
This dissertation is devoted to provide advanced nonconvex nonsmooth variational models of (Magnetic Resonance Image) MRI reconstruction, efficient learnable image reconstruction algorithms and parameter training algorithms that improve the accuracy and robustness of the optimization-based deep learning methods for compressed sensing MRI reconstruction and synthesis.
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis
In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs.
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset
In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network.
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities
We cast the reconstruction network as a structured discrete-time optimal control system, resulting in an optimal control formulation of parameter training where the parameters of the objective function play the role of control variables.
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data.
