The Society of Nuclear Medicine and Molecular Imaging (SNMMI) selected our work on total-body dynamic PET of metastatic cancer that has been recently presented in the 2020 Annual Meeting for a press release:

https://www.snmmi.org/NewsPublications/NewsDetail.aspx?ItemNumber=34153

This work reports the first patient results from our study of total-body 18F-FDG dynamic PET of metastatic cancer. The study is ongoing in the EXPLORER Molecular Imaging Center with support from the UC Davis Cancer Center.

Our NIH R01 grant proposal on Liver Parametric PET received a score in the 1st percentile. Thanks to all the collaborators for the teamwork in the past three years!

Jesse Ahlquist has recently had a conference submission accepted by SNMMI 2020 Annual Meeting for oral presentation.  His work investigates the potential of state-of-the-art deep learning noise reduction for improving ultralow-dose CT imaging. A specific significance of his work is for total-body PET/CT imaging using the EXPLORER scanner in the context of liver quantification (see a figure from his work).

Jesse was an undergraduate student and graduated in December 2019. We wish him all the best for his next adventure!

We have four conference submissions accepted by the 2020 Annual Meeting of SNMMI (Society of Nuclear Medicine and Molecular Imaging), all for oral presentation. Three of these submissions are on total-body PET kinetic modeling:

• Zuo Y, Cherry SR, Badawi RD, Wang GB. Multiphase Patlak Plot Enabled by High Temporal Resolution Total-body Dynamic PET. SNMMI 2020 Annual Meeting, June 2020. accepted for oral presentation.
• Wang Y, Cherry SR, Badawi RD, Wang GB. Effect of dual-input function and dispersion on lung FDG-PET kinetic quantification using the EXPLORER total-body PET/CT scanner. SNMMI 2020 Annual Meeting, June 2020. accepted for oral presentation.
• Wang GB, Parikh M, Nardo L, Zuo Y, Abdelhafez YG, Qi J, Jones T, Price PM, Cherry SR, Pan CX, Badawi RD. Total-Body Dynamic PET of Metastatic Cancer: First Patient Results. SNMMI 2020 Annual Meeting, June 2020. accepted for oral presentation.

Another one is on deep-learning low-dose CT:

• Ahlquist J, Abdelhafez YG, Nardo L, Badawi RD, Qi J, Wang GB. Ultralow-Dose CT Imaging with Deep Learning Noise Reduction on the EXPLORER Total-Body PET/CT Scanner. SNMMI 2020 Annual Meeting, June 2020. accepted for oral presentation.

Thanks to everyone for your hard and smart work!

On December 18th, we completed a total-body one-hour dynamic F18-FDG PET scan of a cancer patient using the EXPLORER scanner. The subject is the first one recruited for our clinical trial on parametric PET for anti-cancer targeted therapy and immunotherapy. The scan provided visualization of total-body spatiotemporal distribution of F18-FDG in a patient with metastatic cancer.

Dr. Siqi Li is joining the lab to work on PET/CT image reconstruction and analysis, starting on November 1st. Dr. Li obtained his PhD degree in Software Engineering from Northeastern University, Shenyang, China in July 2019. He has good background in mathematics and prior experience in PET/CT image processing and machine learning. We are looking forward to working together!

We organized a Mini-Workshop on EXPLORER Kinetic Modeling on September 17th when Dr. Roger Gunn was visiting the EXPLORER Center. The mini-workshop consists of a tutorial on PET kinetic modeling and several scientific talks on the recent progresses in total-body kinetic modeling and parametric imaging. Big thanks to all the speakers!

Chelsea Hong, an undergraduate in Biomedical Engineering joins the lab to explore her interests in medical imaging research. She will be working closely with Dr. Yang Zuo on tracer kinetic modeling of dynamic PET data.

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.

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.