Search Results for author: Hongjiang Wei

Found 14 papers, 5 papers with code

IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI

1 code implementation21 Nov 2023 Ruimin Feng, Qing Wu, Jie Feng, Huajun She, Chunlei Liu, Yuyao Zhang, Hongjiang Wei

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.

MRI Reconstruction Specificity

JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model

no code implementations14 Oct 2023 Lixuan Chen, Xuanyu Tian, Jiangjie Wu, Ruimin Feng, Guoyan Lao, Yuyao Zhang, Hongjiang Wei

In this work, we propose to jointly estimate the motion parameters and coil sensitivity maps for under-sampled MRI reconstruction, referred to as JSMoCo.

MRI Reconstruction

Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction

1 code implementation NeurIPS 2023 Qing Wu, Lixuan Chen, Ce Wang, Hongjiang Wei, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang

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.

Metal Artifact Reduction

Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction

no code implementations31 Dec 2022 Jie Feng, Ruimin Feng, Qing Wu, Zhiyong Zhang, Yuyao Zhang, Hongjiang Wei

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.

MRI Reconstruction

Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field

no code implementations23 Oct 2022 Qing Wu, Xin Li, Hongjiang Wei, Jingyi Yu, Yuyao Zhang

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

no code implementations19 Oct 2022 Ruimin Feng, Qing Wu, Yuyao Zhang, Hongjiang Wei

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.

MRI Reconstruction Specificity

Continuous longitudinal fetus brain atlas construction via implicit neural representation

no code implementations14 Sep 2022 Lixuan Chen, Jiangjie Wu, Qing Wu, Hongjiang Wei, Yuyao Zhang

Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate.


Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image

no code implementations14 Sep 2022 Xuanyu Tian, Qing Wu, Hongjiang Wei, Yuyao Zhang

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.

Image Denoising

Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography

1 code implementation12 Sep 2022 Qing Wu, Ruimin Feng, Hongjiang Wei, Jingyi Yu, Yuyao Zhang

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.

Image Reconstruction

An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation

1 code implementation27 Oct 2021 Qing Wu, Yuwei Li, Yawen Sun, Yan Zhou, Hongjiang Wei, Jingyi Yu, Yuyao Zhang

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.

Image Reconstruction Image Super-Resolution

IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation

no code implementations29 Jun 2021 Qing Wu, Yuwei Li, Lan Xu, Ruiming Feng, Hongjiang Wei, Qing Yang, Boliang Yu, Xiaozhao Liu, Jingyi Yu, Yuyao Zhang

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.

Anatomy Image Reconstruction +1

MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping

1 code implementation21 Jan 2021 Ruimin Feng, Jiayi Zhao, He Wang, Baofeng Yang, Jie Feng, Yuting Shi, Ming Zhang, Chunlei Liu, Yuyao Zhang, Jie Zhuang, Hongjiang Wei

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

no code implementations15 May 2019 Hongjiang Wei, Steven Cao, Yuyao Zhang, Xiaojun Guan, Fuhua Yan, Kristen W. Yeom, Chunlei Liu

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.

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