Chaoping Zhang

Chaoping Zhang
Erasmus MC | Erasmus MC · Department of Radiology & Nuclear Medicine

Master of Science

About

15
Publications
658
Reads
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91
Citations
Citations since 2016
14 Research Items
91 Citations
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20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
Introduction
Chaoping Zhang currently works as a PostDoc at the NKI on MRI for radiotherapy. Before this, he worked as a PostDoc at the AUMC - locatie AMC (the university hospital affiliated with the Universiteit van Amsterdam). Further before, he worked as a PhD student in the Biomedical Imaging Group Rotterdam at Erasmus Medical Center (EMC), Erasmus University Rotterdam. Interests: MR image reconstruction, quantitative MRI, image processing, and deep learning.

Publications

Publications (15)
Article
Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning...
Preprint
In spite of its extensive adaptation in almost every medical diagnostic and examinatorial application, Magnetic Resonance Imaging (MRI) is still a slow imaging modality which limits its use for dynamic imaging. In recent years, Parallel Imaging (PI) and Compressed Sensing (CS) have been utilised to accelerate the MRI acquisition. In clinical settin...
Article
Full-text available
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NY...
Article
Full-text available
Purpose To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). Methods APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal e...
Preprint
Full-text available
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NY...
Chapter
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application across different contrasts, anatomies, or image sizes. To address this limitation, we propose an unsupervised, auto-calibrated k-space completion method, based on a unique...
Preprint
Full-text available
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application across different contrasts, anatomies, or image sizes. To address this limitation, we propose an unsupervised, auto-calibrated k-space completion method, based on a unique...
Conference Paper
Full-text available
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application across different contrasts, anatomies, or image sizes. To address this limitation, we propose an unsupervised, auto-calibrated k-space completion method, based on a unique...
Article
Purpose: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. Methods: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction...
Conference Paper
The long scan time of the brain MRI limits its applicability in high resolution 3D isotropic imaging. By using the recent Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast (APIR4EMC) method, we propose a high resolution (1 mm) 3D isotropic multi-contrast (T1, T1-Fatsat, T2, PD, FLAIR) brain imaging method with scan time aro...
Conference Paper
Motion during scanning deteriorates MR image quality, especially in 3D fast spin echo (FSE) acquisitions which typically require long acquisition time, even with parallel imaging. Instead of prospective motion compensation which is often difficult to perform, we propose a retrospective translational motion compensation method using autocalibration...
Conference Paper
Multi-contrast images of the same region are routinely acquired in clinical MRI. This abstract proposes a fast reconstruction method for multi-contrast imaging: Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast (APIR4EMC). Unlike conventional parallel imaging (GRAPPA) which reconstructs the image for individual contrast, AP...
Conference Paper
We propose a subsampled interleaved parallel acquisition pattern for Autocalibrated Parallel Imaging Reconstruction for GRASE (APIR4GRASE) which considers different echoes during each refocusing of the GRASE as if they originated from different coil channels. APIR4GRASE eliminates ghosting artifacts caused by the phase and amplitude modulations in...
Thesis
Stereo matching could create a dense disparity map from related views and be of great importance for applications of 3DTV, machine vision navigation and virtual reality etc. Stereo matching based on belief propagation(BP) globally optimizes the energy function and produces an accurate depth map with low computational complexity by propagating the b...

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