1 code implementation • 8 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.
no code implementations • 8 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.
no code implementations • 18 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.
no code implementations • 2 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.
1 code implementation • 25 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.
no code implementations • 3 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.
1 code implementation • 20 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.
no code implementations • 27 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.
1 code implementation • 21 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.
no code implementations • 14 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.
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.
no code implementations • 2 May 2023 • Haonan Zhang, Yuhan Zhang, Qing Wu, Jiangjie Wu, Zhiming Zhen, Feng Shi, Jianmin Yuan, Hongjiang Wei, Chen Liu, Yuyao Zhang
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
no code implementations • 31 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.
no code implementations • 23 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.
no code implementations • 19 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.
no code implementations • 14 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.
no code implementations • 14 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.
1 code implementation • 12 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.
1 code implementation • 27 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.
no code implementations • 29 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.
1 code implementation • 21 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.
no code implementations • 15 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.