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Zhifei Wen

Researcher at University of Texas MD Anderson Cancer Center

Publications -  48
Citations -  2433

Zhifei Wen is an academic researcher from University of Texas MD Anderson Cancer Center. The author has contributed to research in topics: Magnetic field & Imaging phantom. The author has an hindex of 17, co-authored 48 publications receiving 2193 citations. Previous affiliations of Zhifei Wen include Stanford University.

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Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) : application with fast spin-echo imaging

TL;DR: Close agreement between theoretical and experimental results obtained from an oil–water phantom was observed, demonstrating that the iterative least‐squares decomposition method is an efficient estimator.
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Multicoil Dixon chemical species separation with an iterative least-squares estimation method.

TL;DR: This work describes a new approach to multipoint Dixon fat–water separation that is amenable to pulse sequences that require short echo time increments, such as steady‐state free precession (SSFP) and fast spin‐echo (FSE) imaging, and extends to multicoil reconstruction with minimal additional complexity.
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Cramér-Rao bounds for three-point decomposition of water and fat.

TL;DR: The Crámer–Rao bound (CRB) was used to study the variance of the estimates of the magnitude, phase, and field map by computing the maximum effective number of signals averaged (NSA) for any choice of echo time shifts.
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The future of image-guided radiotherapy will be MR guided.

TL;DR: This review covers where MR-guided RT might be heading in the near future, including the prevalence, history, advantages and disadvantages of these units, several varieties of which have been designed and installed in centres across the globe.
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Exploratory Study of 4D versus 3D Robust Optimization in Intensity Modulated Proton Therapy for Lung Cancer.

TL;DR: 4D robust optimization produced significantly more robust and interplay-effect-resistant plans for targets with comparable dose distributions for normal tissues, and the benefits from 4D robust optimized plans were most obvious for the 2 typical stage III lung cancer patients.