Search Results for author:
Found 14 papers, 5 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.
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
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.
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
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.
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image
Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods.
Continuous longitudinal fetus brain atlas construction via implicit neural representation
Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate.
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.
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images.
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
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.
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM.
