Fei Yang
University of Miami
16 Papers
22 Citations
Fei Yang is an academic researcher from University of Miami. The author has contributed to research in topics: Dosimetry & Imaging phantom. The author has an hindex of 5, co-authored 14 publications. Previous affiliations of Fei Yang include University of Washington.
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Papers
Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth.
TL;DR: A general simulation framework is presented for assessing the robustness and accuracy of radiomic textural features under various MR acquisition/reconstruction scenarios and how these features may be preserved by MR imaging.
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Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain.
TL;DR: Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial, calling for caution in employing MRI-derived radiomic features.
Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer
Rodrigo Delgadillo,B. Spieler,John C. Ford,Deukwoo Kwon,Fei Yang,Matthew T. Studenski,Kyle R. Padgett,Matthew C. Abramowitz,Alan Dal Pra,Radka Stoyanova,Alan Pollack,Nesrin Dogan +11 more
TL;DR: In this paper, the quality of CBCT-based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction were investigated.
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Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features
G. Simpson,John C. Ford,Ricardo Llorente,Lorraine Portelance,Fei Yang,Eric A. Mellon,Nesrin Dogan +6 more
TL;DR: Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.
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Rounded leaf end modeling in Pinnacle VMAT treatment planning for fixed jaw linacs
TL;DR: Fine‐tune adjustments to MLC rounded leaf ends may improve patient‐specific QA pass rates and provide more accurate predictions of dose deposition to avoidance structures.
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