no code implementations • 11 May 2024 • Qing Wu, Xu Guo, Lixuan Chen, Dongming He, Hongjiang Wei, Xudong Wang, S. Kevin Zhou, Yifeng Zhang, Jingyi Yu, Yuyao Zhang
Specifically, we decompose the energy-dependent LACs into energy-independent densities and energy-dependent mass attenuation coefficients (MACs) by fully considering the physical model of X-ray absorption.
no code implementations • 27 Apr 2024 • Chenhe Du, Xiyue Lin, Qing Wu, Xuanyu Tian, Ying Su, Zhe Luo, Hongjiang Wei, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang
However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT.
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 • 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.
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.
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.