Improvement of CdZnTe based gamma imager CdZnTe using machine learning

Published : 8 February 2020

Gamma imaging is a technique widely used in medical imaging (molecular imaging, nuclear medicine) and security (transportation, industry). CdZnTe semiconducting detectors usage is currently emerging for SPECT (Single Photon Emission Computed Tomography, using gamma-cameras) and portable gamma imaging. Indeed, they enable performance improvements in speed, sensitivity and image quality.

These detectors operate at room temperature and are sensitive to five physical parameters of the interaction: deposited energy E, interaction time T and the 3-dimensional position XYZ. These parameters are estimated by real-time analysis of anode electronics signals.

However, the link between electrical signals and physical parameters is not fully known, as material physical properties are not uniform inside detector. The goal of this Ph.D. internship is to overcome these limits by using machine learning techniques to model actual detector response. Recent multi-layered deep learning technique enable to build and train complex and flexible system models, and to overcome our lack of knowledge about detector physics.

The identification of internal physical parameters of the detector would allow to optimize estimation of interaction location, time and energy.

This will lead to a better image quality and then capability to detect small and weak objects, enabling better diagnoses and lower false alarm rate.

The student may have a background in applied mathematics (machine learning) and/or instrumentation physics. He/she need to have taste for multi-disciplinary research, mixing experimental physics and data science.

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