XEIL Directory

Amir H. Goldan, Ph.D., develops photon-counting x-ray imaging and PET medical imaging detectors and systems. The research of Dr. Goldan, who received his doctorate in electrical and computer engineering from the University of Waterloo, Canada, has been funded by the National Institute of Health (NIH), the National Science Foundation, and the Defense Advanced Research Projects Agency. Recently, the NIH awarded Dr. Goldan a $6.25M U01 grant to develop Prism-PET, a high-performance, compact, portable, upright brain PET scanner featuring motion compensation and CT-less attenuation correction. For clinical translation, Dr. Goldan and the XEIL Lab will use [18F]MK6240 radiotracer, which has subnanomolar affinity for tau neurofibrillary tangles. They will leverage Prism-PET imaging's ultra-high resolution topographical capabilities — such as uptake in small regions like the entorhinal cortex and hippocampus — to perform early-stage Braak staging in asymptomatic individuals with mild cognitive impairment.

The research of Yixin Li, Ph.D., focuses on advancing Positron Emission Tomography (PET) detector and system technology. During his Ph.D., he developed deep learning and statistical methods to decode multiplexed signals from Prism-PET systems, earning him the 22nd Annual IEEE NPSS Real Time Conference Best Student Paper Award. He also developed a deep learning-based inter-crystal Compton scatter recovery method, significantly enhancing PET imaging accuracy.

Eric Petersen is completing his Ph.D. in medical physics at Stony Brook University. His work is driven by applying analytical and statistical methods to modeling and optimizing high-resolution PET imaging systems. Specifically, he has demonstrated the utility of Bayesian and Maximum Likelihood methods in identifying inter-crystal scatter events and applying depth-dependent position corrections. Additionally, he has explored outlier identification techniques based on the Wasserstein distance metric to identify scattered events and apply crystals-specific depth calibrations.

Soroush Shabani is currently a Ph.D. candidate in the Cornell University Physics Department. His research focuses on developing a non-linear forward model and differentiable inverse rendering for PET image reconstruction.

Wanbin Tan is a Ph.D. candidate in biomedical engineering (medical physics) at Stony Brook University. His research focuses on motion tracking and correction, kinetic modeling, and clinical studies in PET imaging.

Zipai Wang, Ph.D., received his doctorate in biomedical engineering from Stony Brook University. His work focuses on image reconstruction, motion correction, modeling, and Monte Carlo simulation of whole-body & brain-dedicated PET imaging systems. He is also developing multi-modal brain imaging methods for early-stage diagnosis of neurodegenerative disorders by integrating AR-based stimulations and eye-tracking with the ultra-high-resolution Prism-PET scanners at XEIL. His work in image reconstruction of the experimental Prism-PET scanner was recognized with the 2022 IEEE MIC Best Student Paper Award.

Zipai Wang, Ph.D., received his doctorate in biomedical engineering from Stony Brook University. His work focuses on image reconstruction, motion correction, modeling, and Monte Carlo simulation of whole-body & brain-dedicated PET imaging systems. He is also developing multi-modal brain imaging methods for early-stage diagnosis of neurodegenerative disorders by integrating AR-based stimulations and eye-tracking with the ultra-high-resolution Prism-PET scanners at XEIL. His work in image reconstruction of the experimental Prism-PET scanner was recognized with the 2022 IEEE MIC Best Student Paper Award.

Neda Zaker, Ph.D., received her doctorate in medical radiation engineering from Shiraz University in Shiraz, Iran. She works on whole-body parametric PET imaging and uses machine-learning techniques for quantitative image analysis.

The research of Xinjie Zeng, Ph.D., focuses on the modeling, development, and quantitative characterization of PET detectors and scanners based on the Prism-PET technology. She also applies machine learning methods to enhance PET imaging systems' timing and depth-of-interaction resolution. Her work on the Prism-PET I brain scanner prototype won the Second Young Investigator Award at the 2022 SNMMI Conference and the Best Mini-Oral/Poster Award at the 2021 IEEE NSS MIC Conference.