|M.Sc Student||Kissos Imry|
|Subject||Statistical Reconstruction Algorithms for Continuous|
Wave Electron Spin Resonance Imaging
|Department||Department of Electrical Engineering||Supervisors||Professor Emeritus Arie Feuer|
|Professor Aharon Blank|
|Full Thesis text|
Electron spin resonance (ESR) is a branch of spectroscopy in which molecules or atoms with unpaired electrons, known as free radicals, can be observed. Unpaired electrons place in a presence of magnetic field, absorb electromagnetic radiation, this absorption is detected by a micro-wave detector. ESR can be used to obtain static structural details as well as dynamic molecular parameters of interest.
Electron spin resonance imaging (ESRI) is capable of displaying endogenous (internal), or exogenous (introduced) free radical spatial distribution in a variety of biological systems or materials science-related samples. It can produce either 3D spatial images (providing information about the spatially dependent radical concentration) or 4D spectral-spatial images, where the extra dimension describes the absorption spectrum of the sample (that can also be spatially dependent). The mapping of oxygen in biological samples, often referred to as Oxymetry is a prime example of ESRI application.
Many times ESRI suffers from low signal to noise ratio (SNR), which results in a long acquisition time and poor image quality. Broader use of ESRI is hampered by this slow acquisition, that can be an obstacle for many biological applications, in which conditions may change relatively quickly over time.
The objective of this work is to reduce the ESR data acquisition time without degrading the reconstruction quality. This can be reached either by shorter acquisition time per projection resulting in lower SNR per projection or by selectively sampling less raw data.
In this work, we adapt the reconstruction statistical framework to the ESR physical acquisition mode. We show an improved signal to noise ratio and contrast recovery using both simulations and laboratory data.