activity
20232026
most citedRegularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data

1 citations · 3 across the 14 of their papers we have counts for

collaborators

14 papers

physics.med-ph2026

A Wireless Volumetric Metamaterial Resonator for Breast MRI

Yuhan Liu, Xia Zhu, Christina A. LeBedis +2

High-performance breast magnetic resonance imaging (MRI) remains limited by the availability, geometry, and cost of dedicated receive coils. In contrast, spine coils are widely ins…

physics.med-ph2026

A Wireless Reconfigurable Metasurface for Enhanced Parallel Magnetic Resonance Imaging

Yuhan Liu, Xia Zhu, Ke Wu +4

Modern magnetic resonance imaging (MRI) relies on application-specific multi-channel receive coils to achieve high performance, but these coils are typically costly, rigid, and dif…

physics.med-ph2026

Circularly polarized metamaterial cage for homogeneous signal-to-noise ratio enhancement in magnetic resonance imaging

Yuhan Liu, Xia Zhu, Ke Wu +2

The signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images.…

eess.IV2024

Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction

Guoyao Shen, Mengyu Li, Stephan Anderson +2

Recent advancements in deep learning have enabled the development of generalizable models that achieve state-of-the-art performance across various imaging tasks. Vision Transformer…

physics.app-ph2024

A robust near-field body area network based on coaxially-shielded textile metamaterial

Xia Zhu, Ke Wu, Xiaohang Xie +2

A body area network (BAN) involving wearable sensors populated around the human body can continuously monitor physiological signals, finding applications in personal healthcare and…

physics.app-ph2024

Phased Gradient Ultra Open Metamaterials for Broadband Acoustic Silencing

Zhiwei Yang, Ao Chen, Xiaohang Xie +2

Noise pollution is a persistent environmental concern with severe implications for human health and resources. Acoustic metamaterials offer the potential for ultrathin silencing de…

physics.med-ph2023

Wireless, Customizable Coaxially-shielded Coils for Magnetic Resonance Imaging

Ke Wu, Xia Zhu, Stephan W. Anderson +1

Anatomy-specific RF receive coil arrays routinely adopted in magnetic resonance imaging (MRI) for signal acquisition, are commonly burdened by their bulky, fixed, and rigid configu…

physics.med-ph2023

Wearable Coaxially-shielded Metamaterial for Magnetic Resonance Imaging

Xia Zhu, Ke Wu, Stephan W. Anderson +1

Recent advancements in metamaterials have yielded the possibility of a wireless solution to improve signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI). Unlike traditio…

physics.med-ph2023

Computational-design Enabled Wearable and Tunable Metamaterials via Freeform Auxetics for Magnetic Resonance Imaging

Ke Wu, Xia Zhu, Thomas G. Bifano +2

Metamaterials hold significant promise for enhancing the imaging capabilities of MRI machines as an additive technology, due to their unique ability to enhance local magnetic field…

eess.IV2023★ 1 cited

Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise

Guoyao Shen, Mengyu Li, Chad W. Farris +2

Deep learning-based MRI reconstruction models have achieved superior performance these days. Most recently, diffusion models have shown remarkable performance in image generation,…

physics.med-ph2023★ 1 cited

Conformal Metamaterials with Active Tunability and Self-adaptivity for Magnetic Resonance Imaging

Ke Wu, Xia Zhu, Xiaoguang Zhao +2

Ongoing effort has been devoted to applying metamaterials to boost the imaging performance of magnetic resonance imaging owing to their unique capacity for electromagnetic field co…

cs.CV2023★ 1 cited

Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data

Guoyao Shen, Yancheng Zhu, Mengyu Li +6

Recent advances in MRI reconstruction have demonstrated remarkable success through deep learning-based models. However, most existing methods rely heavily on large-scale, task-spec…

eess.IV2023

Attention Hybrid Variational Net for Accelerated MRI Reconstruction

Guoyao Shen, Boran Hao, Mengyu Li +4

The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that…

physics.med-ph2023

Bayesian Reconstruction of Magnetic Resonance Images using Gaussian Processes

Yihong Xu, Chad W. Farris, Stephan W. Anderson +2

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations suc…