Mark Tseytlin

Biography

Works as  Assistant Professor in Biochemistry

Research Interest

My collaborators and I are developing new spectroscopic and imaging methods for in vivo Electron Paramagnetic Resonance (EPR). EPR is in many ways analogous to better known Nuclear Magnetic Resonance (NMR) and its imaging modality MRI. While NMR and MRI methods detect signals produced by nuclear spins in protons and other chemical elements, EPR measures electron spins in all kinds of free radicals. A variety of stable radicals has been synthesized that reports the physiological microenvironment in normal tissues and tumors. This includes measurements of pO2, pH, redox status, temperature, etc. EPR spin probes are introduced in vivo in low concentrations to reduce interference with the measured physiological function. For this reason, improvement of EPR sensitivity is of crucial importance for EPR spectroscopy, and especially, imaging. We have been developing new spectroscopic and imaging methods to improve accuracy of measured physiological parameters and spatial resolution. Some of these methods are now being incorporated into commercial EPR spectrometers/imagers. Development of new spectroscopic or imaging methods for in vivo applications requires a multi-disciplinary approach. Students with different backgrounds from mathematics and engineering to biology will be able to find a project to work on. For the 2015-2016 year, the focus of my lab will be on building an EPR imager that will be used in subsequent years for pre-clinical studies on mice models. Immediate help is needed to work on the following projects: Electromagnetic simulations using Sim4life, http://www.zurichmedtech.com/sim4life/, software to develop bi-modal resonators for EPR Imaging (EPRI). The student is expected to master the software and become an expert in MRI and EPRI resonator designs, and GPU programming. Selected bi-modal models will be 3D-printed and tested on phantoms and in vivo. Development of 4D spectral-spatial imaging algorithm that is based on inversion of a large system of linear equations using Tikhonov regularization.