Search Results for author: Ruimin Feng

Found 6 papers, 3 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

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

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

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

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.


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