7 citations · 10 across the 17 of their papers we have counts for
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DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
Basar Demir, Yikang Liu, Xiao Chen +5
Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are…
Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping
Yikang Liu, Lin Zhao, Eric Z. Chen +3
Dynamic coronary roadmapping is a technology that overlays the vessel maps (the "roadmap") extracted from an offline image sequence of X-ray angiography onto a live stream of X-ray…
Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI
Shihan Qiu, Shaoyan Pan, Yikang Liu +7
Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring. We aim to improve image sharpness and motion delineation for cine…
Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI
Shihan Qiu, Shaoyan Pan, Yikang Liu +7
The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processi…
Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer
Zihao Chen, Xiao Chen, Yikang Liu +3
Cardiac Magnetic Resonance imaging (CMR) is the gold standard for assessing cardiac function. Segmenting the left ventricle (LV), right ventricle (RV), and LV myocardium (MYO) in C…
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal
Yikang Liu, Eric Z. Chen, Xiao Chen +2
The k-space data generated from magnetic resonance imaging (MRI) is only a finite sampling of underlying signals. Therefore, MRI images often suffer from low spatial resolution and…
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction
Daniel H. Pak, Xiao Chen, Eric Z. Chen +3
Dynamic Magnetic Resonance Imaging (dMRI) is widely used to assess various cardiac conditions such as cardiac motion and blood flow. To accelerate MR acquisition, techniques such a…
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP
Eric Z. Chen, Chi Zhang, Xiao Chen +3
Compared with 2D MRI, 3D MRI provides superior volumetric spatial resolution and signal-to-noise ratio. However, it is more challenging to reconstruct 3D MRI images. Current method…
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
Lin Zhao, Xiao Chen, Eric Z. Chen +4
Multi-contrast Magnetic Resonance Imaging (MRI) generates multiple medical images with rich and complementary information for routine clinical use; however, it suffers from a long…
Invertible Sharpening Network for MRI Reconstruction Enhancement
Siyuan Dong, Eric Z. Chen, Lin Zhao +4
High-quality MRI reconstruction plays a critical role in clinical applications. Deep learning-based methods have achieved promising results on MRI reconstruction. However, most sta…