2019/04: Jesse presented at UC Davis Undergraduate Research Symposium

Jesse Ahlquist, an undergraduate student researcher in our group, presented his work on deep-learning low-dose liver CT imaging at the 30th UC Davis Annual Undergraduate Research, Scholarship & Creative Activities Conference. His study uses convolutional neural networks as a tool to investigate low-dose CT for fat quantification. Jesse’s effort on liver CT is synergistic with our ongoing development of liver parametric PET to enable a multiparametric liver PET/CT technique for imaging of fatty liver disease.

2019/04: New graduate student

Yiran Wang, a graduate student of the Biomedical Engineering Graduate Group, joins the lab to work on total-body parametric imaging with EXPLORER. He will be jointly supervised by Dr. Guobao Wang and Dr. Simon Cherry.

2019/03: SNMMI acceptance on kinetic modeling works

We have two conference submissions on PET kinetic modeling methodologies accepted by the 2019 Annual Meeting of SNMMI, both for oral presentation.  Elizabeth Li, a graduate student of Biomedical Engineering who is jointly supervised by Drs. Simon Cherry and Guobao Wang, will present her work on total-body PET kinetic modeling:

Li E, Cherry SR, TarantalAF , Shi H, Chen S, Hu P, Ding Y, Hu D, Zhou P, Xu T, Wang C, Jones T, Badawi RD, Wang GB. Identification and comparison of image-derived input functions using total-body PET, Society of Nuclear Medicine and Molecular Imaging (SNMMI) 2019 Annual Meeting, Anaheim, California, June 22-25, 2019.

Guobao will present a recent work on high-temporal resolution dynamic PET kinetic modeling:

Wang GB, Spencer B, Sarkar S, Shi H, Chen S, Hu P, Ding Y, Hu D, Zhou P, Xu T, Wang C, Jones T, Cherry SR, Badawi RD. Quantification of Glucose Transport Using High Temporal Resolution Dynamic PET Imaging. Society of Nuclear Medicine and Molecular Imaging (SNMMI) 2019 Annual Meeting, Anaheim, California, June 22-25, 2019.


2019/02: NIH Trailblazer R21 award

Guobao receives a NIH/NIBIB Trailblazer Award (R21). We will explore and develop the feasibility and potential of a novel PET-enabled dual-energy spectral CT method. Success of this research will add spectral CT imaging as a new dimension of information to clinical PET/CT without increasing imaging cost, time, and radiation dose.