Search Results for author: Haimiao Zhang

Found 9 papers, 3 papers with code

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction

1 code implementation11 Sep 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Jiawei Chen, Kai Ma, Deyu Meng, Yefeng Zheng

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration.

Metal Artifact Reduction

InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images

1 code implementation23 Dec 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Meng, Yefeng Zheng

To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded.

Computed Tomography (CT) Metal Artifact Reduction

A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection

1 code implementation7 Mar 2023 Haocheng Ju, Haimiao Zhang, Lin Li, Xiao Li, Bin Dong

Joint channel estimation and signal detection (JCESD) in wireless communication systems is a crucial and challenging task, especially since it inherently poses a nonlinear inverse problem.

Rolling Shutter Correction

JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data

no code implementations3 Dec 2018 Haimiao Zhang, Bin Dong, Baodong Liu

CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging.

Image Reconstruction

A Review on Deep Learning in Medical Image Reconstruction

no code implementations23 Jun 2019 Haimiao Zhang, Bin Dong

More recently, as more data and computation resources are made available, deep learning based models (or deep models) pushed data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs.

Image Reconstruction Image Restoration +1

Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation

no code implementations9 Mar 2021 Ce Wang, Haimiao Zhang, Qian Li, Kun Shang, Yuanyuan Lyu, Bin Dong, S. Kevin Zhou

More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.

Computed Tomography (CT)

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

no code implementations21 Nov 2021 Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, Yuan Hui, S. Kevin Zhou

While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views.

Computed Tomography (CT)

Active CT Reconstruction with a Learned Sampling Policy

no code implementations3 Nov 2022 Ce Wang, Kun Shang, Haimiao Zhang, Shang Zhao, Dong Liang, S. Kevin Zhou

Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.

Computed Tomography (CT) Decision Making

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