Search Results for author: Hongjiang Wei

Found 22 papers, 8 papers with code

Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

1 code implementation8 Feb 2025 Xuanyu Tian, Lixuan Chen, Qing Wu, Chenhe Du, Jingjing Shi, Hongjiang Wei, Yuyao Zhang

Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems.

CT Reconstruction

Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT

no code implementations8 Dec 2024 Qing Wu, Hongjiang Wei, Jingyi Yu, Yuyao Zhang

Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, severely degrading imaging quality and reliability.

GESH-Net: Graph-Enhanced Spherical Harmonic Convolutional Networks for Cortical Surface Registration

no code implementations18 Oct 2024 Ruoyu Zhang, Lihui Wang, Kun Tang, Jingwen Xu, Hongjiang Wei

The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data.

Deep Learning Graph Attention +2

Coordinate-Based Neural Representation Enabling Zero-Shot Learning for 3D Multiparametric Quantitative MRI

no code implementations2 Oct 2024 Guoyan Lao, Ruimin Feng, Haikun Qi, Zhenfeng Lv, Qiangqiang Liu, Chunlei Liu, Yuyao Zhang, Hongjiang Wei

Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice.

Zero-Shot Learning

Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation

1 code implementation25 Sep 2024 Qing Wu, Chenhe Du, Xuanyu Tian, Jingyi Yu, Yuyao Zhang, Hongjiang Wei

Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion.

Model Optimization

Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models

no code implementations3 Jul 2024 Jiayue Chu, Chenhe Du, Xiyue Lin, Yuyao Zhang, Hongjiang Wei

The posterior sampling of diffusion models based on the real measurement data holds significant promise of improved reconstruction accuracy.

Zero-Shot Image Denoising for High-Resolution Electron Microscopy

1 code implementation20 Jun 2024 Xuanyu Tian, Zhuoya Dong, Xiyue Lin, Yue Gao, Hongjiang Wei, Yanhang Ma, Jingyi Yu, Yuyao Zhang

Noise2SR trains the network with paired noisy images of different resolutions, which is conducted via SR strategy.

Data Augmentation Image Denoising +2

DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

no code implementations27 Apr 2024 Chenhe Du, Xiyue Lin, Qing Wu, Xuanyu Tian, Ying Su, Zhe Luo, Rui Zheng, Yang Chen, Hongjiang Wei, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang

Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions.

Computed Tomography (CT) CT Reconstruction +2

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 NeRF

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.

NeRF

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

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

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.

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.

CT 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.

Decoder Image Reconstruction +1

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.

Deep Learning SSIM

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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