Woodruff School of Mechanical Engineering
Nuclear & Radiological Engineering and Medical Physics Programs
Measurements of LET, Nuclear Halo, Energy and Angular Spectra of a Proton Spot Scanning Beam with a Hybrid Semiconductor Detector
Dr. Serdar Charyyev
Thursday, October 29, 2020 at 11:00:00 AM
Dr. C.K. Wang
There are numerous models of LET, nuclear halos, energy and angular spectra of proton spot scanning beams, but little data for these quantities has been measured due to inherent difficulty in the measurements. Therefore, we introduce an approach to measure these quantities with hybrid semiconductor pixel detector, AdvaPix TPX3 TPX. In this method, a proton pencil beam is incident on a square pixelized silicon detector. Data can be taken in list mode where, for each proton, position, energy, time of arrival and track shape are measured. Other spectroscopic and directional information i.e. LET, elevation angle, type are extracted from detailed pattern analysis of the particle tracks. All results from measurements are compared to simulated data using TOPAS. Developed technique may allow increased confidence in beam modeling for treatment planning systems.
Dr. Serdar Charyyev is an early career researcher at Emory University with interest in charged particle therapy, Monte Carlo simulations, radiobiology, linear energy transfer LET, with specific emphasis on computational and experimental development of minibeams for spatial fractionation. His work is based on optimization of hexagonal minibeam arrangement and dose calculations with aim to increase therapeutic gain. Moreover, he is involved in design of the experiment setup and testing of the novel detector to measure the perturbations of the dose and LET in the presence of metal implant in proton therapy patients. As a resident physicist in radiation oncology, he is also involved in a broad spectrum of clinical research primarily based on characterization of metal implants in radiation therapy patients and improvement of image quality in proton portal imaging using deep learning.