Search Results for author:
Found 6 papers, 3 papers with code
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms.
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model
In this work, we propose to jointly estimate the motion parameters and coil sensitivity maps for under-sampled MRI reconstruction, referred to as JSMoCo.
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation
This function was parameterized by a neural network and learned directly from the measured k-space itself without additional fully sampled high-quality training data.
Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy that pushes the tomography image reconstruction quality over supervised deep learning CT reconstruction works.
MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping
However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label.
