Elizabeth Li
University of California, Davis
Deep learningKinetic energyArtificial intelligenceBiological systemDynamic imagingSensitivity (control systems)Positron emission tomographyScale (ratio)VentricleMotion correctionNuclear medicineParametric imagingPet imagingTotal bodyBlood volumeNeuroimagingBinding potentialMedicinePartial volume
3Publications
1H-index
28Citations
Publications 5
Newest
#1Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
#2Lorenzo Nardo (UC Davis: University of California, Davis)H-Index: 30
Last. Ramsey D. Badawi (UC Davis: University of California, Davis)H-Index: 29
view all 10 authors...
Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification as compared to static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiological models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on long axial field-of-view scanners. In this paper,...
Source
#1Edwin Leung (UC Davis: University of California, Davis)H-Index: 3
#2Eric Berg (UC Davis: University of California, Davis)H-Index: 15
Last. Songsong TangH-Index: 2
view all 15 authors...
Absolute quantification of regional tissue concentration of radioactivity in positron emission tomography (PET) is a critical parameter-of-interest across various clinical and research applications and is affected by a complex interplay of factors including scanner calibration, data corrections, and image reconstruction. The emergence of long axial field-of-view (FOV) PET systems widens the dynamic range accessible to PET and creates new opportunities in reducing scan time and radiation dose, de...
Source
#1Yiran Wang (UC Davis: University of California, Davis)H-Index: 1
#2Elizabeth Li (UC Davis: University of California, Davis)H-Index: 1
Last. Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
view all 4 authors...
The uEXPLORER total-body PET/CT system provides a very high level of detection sensitivity and simultaneous coverage of the entire body for dynamic imaging for quantification of tracer kinetics. This article describes the fundamentals and potential benefits of total-body kinetic modeling and parametric imaging focusing on the noninvasive derivation of blood input function, multiparametric imaging, and high-temporal resolution kinetic modeling. Along with its attractive properties, total-body kin...
Source
#1Tao Feng (BP)H-Index: 7
#2Hongdi Li (BP)H-Index: 23
Last. Ramsey D. Badawi (UC Davis: University of California, Davis)H-Index: 29
view all 0 authors...
For a dedicated brain scan, the carotid artery is the best location for acquiring an image-based input function. With improvements in PET spatial resolution, accurate quantitation may be achieved with PET data alone. With the ability to cover both the carotid artery and the thorax at high spatial resolution, the uEXPLORER datasets provide a unique opportunity to develop and validate input functions in multiple regions such as the carotid artery. The regions containing the carotid arteries were f...
Source
#1Elizabeth Li (UC Davis: University of California, Davis)H-Index: 1
#2Simon R. Cherry (UC Davis: University of California, Davis)H-Index: 92
Last. Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
view all 14 authors...
520 Objectives: Full compartmental modeling is vital in dynamic positron emission tomography (PET) for a quantitative understanding of tracer kinetics but requires a blood input function. A noninvasive image-derived input function (IDIF) can replace arterial sampling, but reliable IDIF extraction (i.e. large blood pool) can be challenging with conventional PET due to the limited axial field-of-view (FOV) (e.g. dynamic brain studies). The EXPLORER [1] is a 2-meter long total-body human PET scanne...
Close Researchers
Laura J. Esserman
H-index : 90
University of California, San Francisco
Guobao Wang
H-index : 19
University of California, Davis
Simon R. Cherry
H-index : 92
University of California, Davis
Hongcheng Shi
H-index : 18
Fudan University
Yiran Wang
H-index : 1
University of California, Davis
Chao Wang
H-index : 1
Debin Hu
H-index : 1
Ping Zhou
H-index : 1
Yu Ding
H-index : 4
Bo La Yun
H-index : 1
Seoul National University Bundang Hospital
Tianyi Xu
H-index : 3
Shuguang Chen
H-index : 6
Fudan University
Negar Omidvari
H-index : 6
University of California, Davis
Wen Li
H-index : 7
University of California, San Francisco
Ella F. Jones
H-index : 18
University of California, San Francisco
Pengcheng Hu
H-index : 11
Fudan University
Jessica Gibbs
H-index : 14
University of California, San Francisco
Terry L. Jones
H-index : 29
California Polytechnic State University
David C. Newitt
H-index : 56
University of California, San Francisco
Vignesh A. Arasu
H-index : 9
Kaiser Permanente
Ramsey D. Badawi
H-index : 29
University of California, Davis
John Kornak
H-index : 40
University of Toronto
Alice F. Tarantal
H-index : 63
California National Primate Research Center
Savannah C. Partridge
H-index : 34
University of Washington
Lisa J. Wilmes
H-index : 18
University of California, San Francisco
Nola M. Hylton
H-index : 62
University of California, San Francisco
Jeffrey P. Schmall
BP
Hongdi Li
H-index : 3
BP
Negar Omidvari
H-index : 1
University of California, Davis
Yizhang Zhao
H-index : 1
Yang Lv
H-index : 4
Yasser Abdelhafez
H-index : 5
University of California, Davis
Tao Feng
H-index : 7
BP
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