Mengxi Zhang
University of California, Davis
15 Papers
32 Citations
Mengxi Zhang is an academic researcher from University of California, Davis. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 5, co-authored 15 publications.
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Papers
A Prototype High-Resolution Small-Animal PET Scanner Dedicated to Mouse Brain Imaging
Yang Yongfeng,Julien Bec,Jian Zhou,Mengxi Zhang,Martin S. Judenhofer,Xiaowei Bai,Kun Di,Yibao Wu,Mercedes Rodriguez,Purushottam Dokhale,Kanai S. Shah,Richard Farrell,Jinyi Qi,Simon R. Cherry +13 more
TL;DR: A prototype PET scanner based on depth-encoding detectors using dual-ended readout of small scintillator elements to produce high and uniform spatial resolution suitable for imaging the mouse brain is developed, and a spatial resolution approaching the physical limits of a small-bore PET scanner is achieved.
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Efficient system modeling for a small animal PET scanner with tapered DOI detectors.
TL;DR: An efficient system model for the tapered PET scanner using matrix factorization and a virtual scanner geometry to maintain image quality while substantially reduce the image reconstruction time and system matrix storage cost is presented.
Prototype Small-Animal PET-CT Imaging System for Image-guided Radiation Therapy
Ekaterina Mikhaylova,Jamison Brooks,Jamison Brooks,Darren Zuro,Darren Zuro,Farouk Nouizi,Maciej Kujawski,Srideshikan Sargur Madabushi,Jinyi Qi,Mengxi Zhang,Junie Chea,Erasmus Poku,Nicole Bowles,Jeffrey Y.C. Wong,John E. Shively,Paul J. Yazaki,Gultekin Gulsen,Simon R. Cherry,Susanta K. Hui +18 more
TL;DR: The development of a prototype positron emission tomography scanner integrated into a commercial cone beam computed tomography (CBCT) based small animal irradiation system for molecular- image-guided, targeted external beam radiation therapy and demonstrates the feasibility of molecular-image-guided treatment plans using the prototype theranostic system.
Low-dose CT reconstruction method based on prior information of normal-dose image.
Zixiang Chen,Qiyang Zhang,Chao Zhou,Mengxi Zhang,Yang Yongfeng,Xin Liu,Hairong Zheng,Dong Liang,Hu Zhanli +8 more
TL;DR: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction that has superior performance over other state-of-art methods.
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Regularization parameter selection for penalized-likelihood list-mode image reconstruction in PET
TL;DR: A method to choose regularization parameters using a cross-validation log-likelihood (CVLL) function that does not require any knowledge of the true image and is directly applicable to list-mode PET data is presented.