2020/04: Undergraduate Jesse to present deep-learning ultralow-dose CT quantification

By | April 7, 2020

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!