Our lab has multiple abstracts accepted for presentation at the Fully3D 2023 and SNMMI 2023.

One abstract on dynamic PET image reconstruction for Fully3D 2023:

• Siqi Li, Wang GB. Deep kernel representation learning for high-temporal resolution dynamic PET image reconstruction, accepted for oral presentation, Fully3D 2023 Conference, July 16-21, 2023

Three abstracts on total-body PET kinetic modeling for SNMMI 2023:

• Quyen Tran, Lyo V, Matsukuma KE, Corwin MT, Spencer BA, Wang Y, Romeo SR, Medici V, Cherry SR, Badawi RD, Sarkar S,  Wang GB. Total-body PET quantification of metabolism in non-alcoholic steatohepatitis using a three-tissue compartment model, SNMMI Annual Meeting 2023, accepted for an oral presentation, June 27, 2023
• Yiran Wang, Abdelhafez YG, Spencer BA, Verma R, Parikh M, Stollenwerk N, Nardo L, Jones T, Badawi RD, Cherry SR, Wang GB. Impact of dual-blood input function on kinetic modeling of lung tumors using total-body PET, SNMMI Annual Meeting 2023, accepted for an oral presentation, June 27, 2023
• Yiran Wang, Spencer BA, Abdelhafez YG, Parikh M, Verma R, Nardo L, Jones T, Badawi RD, Cherry SR, Wang GB. Total-body compartmental visualization using kinetic modeling and the EXPLORER PET/CT system, accepted for a poster presentation at SNMMI Annual Meeting 2023.

Xiaoyu Duan, a graduate student of the Biomedical Engineering Graduate Group (BMEGG), joins the lab to work on total-body PET kinetic modeling. She will be jointly supervised by Dr. Guobao Wang and Dr. Ramsey Badawi. Welcome, Xiaoyu!

Our lab received a new NIH R01 grant entitled “Single-tracer Multiparametric PET Imaging” from NIBIB. The goal of this project is to develop a parametric PET method to enable the widely accessible radiotracer 18F-FDG for quantitative blood flow imaging. Thanks to all the collaborators!

In the 2022 UC Davis Radiology Research Symposium, Yiran received the “Best Abstract by a Graduate Student” for his work “Multi-organ metabolic changes in COVID-19 recovery measured with total-body dynamic 18F-FDG PET”.

Quyen also received the “Best Abstract by a Postdoctoral Fellow” for his work “Interstitial Space Properties of 18F-FDG in Nonalcoholic Fatty Liver Disease”. Congratulations to both!

Congratulations to Siqi Li, Yansong Zhu and Yiran Wang for their conference abstracts accepted by IEEE MIC 2022 and Total-body PET 2022:

Siqi Li, GB Wang. Learning of deep kernels with pairwise attention for PET Image reconstruction. 2022 IEEE Nuclear Sciences Symposium and Medical Imaging Conference (NSS&MIC), Milan, Italy. 5-12 November 2022. Accepted for oral presentation.

Yansong Zhu, S Li, Z Xie, EK Leung, R Bayerlein, N Omidvari, SR Cherry, J Qi, RD Badawi, BA Spencer, GB Wang. PET-enabled dual-energy CT: open-source implementation and real data validation. 2022 IEEE Nuclear Sciences Symposium and Medical Imaging Conference (NSS&MIC), Milan, Italy. 5-12 November 2022. Accepted for oral presentation.

Yiran Wang, S Li, B Spencer, R Verma, M Parikh, L Nardo, RD Badawi, SR Cherry, GB Wang. Total-body PET parametric imaging using Deep Patlak: a deep-learning kinetic modeling method inspired by the Patlak plot.  Total-body PET 2022, Edinburgh, Scotland. 24-26 September 2022. Accepted for oral presentation.

Our lab has multiple abstract submissions on PET tracer kinetic modeling accepted for oral presentation or poster presentation at SNMMI Annual Meeting 2022:

• Yiran Wang, L Nardo, BA. Spencer, Y Abdelhafez, AJ Chaudhari, RD Badawi, SR Cherry, GB Wang, Multi-organ metabolic changes in COVID-19 recovery measured with total-body dynamic 18F-FDG PET, accepted for oral presentation, SNMMI Annual Meeting 2022.
• Quyen Tran, KE Matsukuma, BA Spencer, Y Wang, E Li, MT Corwin, SR Cherry, RD Badawi, S Sarkar, GB Wang, High-temporal resolution kinetic modeling on total-body PET differentiates the hepatic interstitial space in nonalcoholic fatty liver disease and healthy subjects, accepted for oral presentation, SNMMI Annual Meeting 2022.
• Yansong Zhu, Y Wang, Q Tran, RD Badawi1,2, SR Cherry, J Qi, S Abbaszadeh, GB Wang, Kernel SIME: simultaneous estimation of blood input function using a kernel method and its evaluation with total-body PET, accepted for oral presentation, SNMMI Annual Meeting 2022.
• Siqi Li, Y Wang, BA Spencer, H Hunt, T Seibert, L Nardo, SR Cherry, RD Badawi, GB Wang, Dual-energy CT bone-fraction correction for total-body PET kinetic quantification of bone marrow, accepted for poster presentation, SNMMI Annual Meeting 2022.

Student Liz Li of the Cherry Lab also has an abstract accepted for oral presentation:

• Elizabeth Li, BA Spencer, Y Abdelhafez, JE López, GB Wang, SR Cherry, Total-body perfusion imaging using [11C]-butanol, accepted for oral presentation, SNMMI Annual Meeting 2022.

Dr. Siqi Li presented his recent work on a deep kernel method for PET image reconstruction at the SPIE Medical Imaging 2022 held in San Diego. This is his first time presenting in person at an international conference in the pandemic, though he already presented three other talks virtually at IEEE-MIC 2021, SPIE Medical Imaging 2021, and Fully 3D 2021. Well done, Siqi!

Our Liver Parametric PET work for imaging fatty liver disease (in close collaboration with Souvik Sarkar MD and other colleagues at UC Davis Medical Center) was recently highlighted by UC Davis Health and also featured as Top Story on SNMMI SmartBrief newsletters.

UC Davis Health Newsroom: New PET imaging-based tool detects liver inflammation from fatty liver disease

SNMMI SmartBrief: PET-based tool detects liver inflammation in NAFLD

Yiran Wang, a Ph.D. student of Dr. Guobao Wang and Dr. Simon Cherry, won the IEEE NPSS Christopher J Thompson Student Awards “2nd Best Oral Paper” at the 2021 IEEE Medical Imaging Conference. His paper

Y Wang, E Berg, Y Zuo, E Li, BA Spencer, RD Badawi, SR Cherry, GB Wang. Voxel-wise kinetic model selection using single-subject deep learning for total-body PET parametric imaging. 2021 Virtual IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS&MIC), October 16-23, 2021.

is one of the two Best Oral Papers that were selected from 141 student applicants.

Yiran has been working on total-body PET kinetic modeling and parametric imaging and exploring deep learning approaches in this area. In this work, Yiran developed a single-subject deep learning approach to address the voxel-wise model selection problem for total-body PET parametric imaging on EXPLORER. His approach uses a small fraction of voxels from a subject to build a temporal neural network model and then applies the learned model to fast predict the appropriate kinetic model type of a vast amount of voxels of the same subject. As compared to a standard solution, the deep-learning approach leads to improved total-body PET parametric images with reduced artifacts without a significant increase in computational time.

This work is supported in part by NIH grants R01 CA206187, R01DK124803.

Congratulations, Yiran!

Yiran, Siqi, and Yansong have three conference submissions accepted by the 2021 IEEE Nuclear Science Symposium & Medical Imaging Conference (NSS&MIC):

• Y Wang, E Berg, Y Zuo, E Li, BA Spencer, RD Badawi, SR Cherry, G Wang. Voxel-Wise Kinetic Model Selection Using Single-Subject Deep Learning for Total-Body PET Parametric Imaging, IEEE NSS&MIC, Oct 2021, accepted for oral presentation.
• S Li, K Gong, J Qi, G Wang. Neural KEM for PET Image Reconstruction, IEEE NSS&MIC, Oct 2021, accepted for oral presentation.
• Y Zhu, G Wang. Statistical CT sinogram generation from time-of-flight PET data using kernel methods in the projection space, IEEE NSS&MIC, Oct 2021, accepted for mini-oral presentation.

Elizabeth (of the Cherry Lab) also has a paper accepted:

• EJ Li, E Berg, Y Wang, BA Spencer, RD Badawi, AF Tarantal, G Wang, SR Cherry, CNN-based time delay estimation in dynamic total-body PET kinetic modeling, IEEE NSS&MIC, Oct 2021, accepted for poster presentation.