2022/04: SNMMI 2022 abstract acceptance

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.

2022/02: Siqi Li presented at SPIE Medical Imaging

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!

2021/10: Yiran Wang Won IEEE-MIC “2nd Best Oral Paper” Award

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!

2021/07: IEEE MIC submission acceptance

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.

2021/01: Dr. Yansong Zhu Joining the Lab

Dr. Yansong Zhu is joining our lab starting from February 1st. He received his Ph.D. in Electrical and Computer Engineering from Johns Hopkins University in 2020. During his PhD research in the lab of Quantitative Radiomolecular Imaging and Therapy, Yansong developed improved physical modeling and image reconstruction algorithms for fluorescence molecular tomography and partial volume correction algorithms for brain PET/MR imaging. We are looking forward to working together on exciting projects.

2020/12: Dr. Quyen Tran Joining the Lab

Dr. Quyen Tran is joining our lab, starting February 1st, 2021. Dr. Tran obtained his Ph.D. in Mathematics from the Vietnam Academy of Science and Technology in 2013. He was an Alexander von Humboldt Fellow at the University of Hamburg, Germany, and a Postdoctoral Fellow at the University of Goettingen, Germany, and received Habilitation in 2019. He has a strong background in differential equations and inverse problems. We are looking forward to working with him together!

2020/11: Liver Imaging Symposium Successfully Held

The virtual 2020 UC Davis Liver Imaging Symposium – Innovations in Liver Imaging: From Byte to Bedside, co-chaired by Dr. Souvik Sarkar and Dr. Guobao Wang, was successfully held on November 17th, 2020. The symposium is jointly sponsored by the Department of Internal Medicine, Department of Radiology, and the UCD Comprehensive Cancer Center. It consisted of ten excellent scientific talks and a panel discussion. The symposium attracted a good attendance (peaked at ~90 participants attending the scientific sessions and ~65 attendees in the last panel discussion session). We thank all the organizers, speakers, and attendees!