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most citedSelf-Supervised Coordinate Projection Network for Sparse-View Computed Tomography

50 citations · 91 across the 8 of their papers we have counts for

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6 papers · 1 filter

eess.IV2023

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

Ruimin Feng, Qing Wu, Jie Feng +4

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an i…

eess.IV2023

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

Lixuan Chen, Xuanyu Tian, Jiangjie Wu +4

Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis. However, it is known that MRI faces challenges such as long acquisition time and vulnerability…

eess.IV2023★ 37 cited

Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction

Jie Feng, Ruimin Feng, Qing Wu +3

Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. H…

eess.IV2022

A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation

Ruimin Feng, Qing Wu, Yuyao Zhang +1

Parallel imaging is a widely-used technique to accelerate magnetic resonance imaging (MRI). However, current methods still perform poorly in reconstructing artifact-free MRI images…

eess.IV2022★ 50 cited

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

Qing Wu, Ruimin Feng, Hongjiang Wei +2

In the present work, we propose a Self-supervised COordinate Projection nEtwork (SCOPE) to reconstruct the artifacts-free CT image from a single SV sinogram by solving the inverse…

eess.IV2021

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

Qing Wu, Yuwei Li, Lan Xu +7

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 an…