Investigation of two heavy element scintillators by Monte-Carlo methods

The aim of this study was to estimate the influence of K-characteristic radiation on the performance of x-ray scintillating screens containing two heavy elements by Monte Carlo methods. K-characteristic radiation is produced within materials of at least one heavy (high atomic number) element. This radiation may result either in spatial resolution degradation or in emission efficiency decrease. The scintillators studied were the following: LYSO (Lu1.8Y0.2SiO5 and LuYSiO5), CsI and YTaO4. All the aforementioned scintillators have two heavy elements, thus the K-characteristic radiation of the high-Z element can produce additional K-characteristic photons on the low-Z element, resulting in further degradation. Scintillator performance was described in terms of the: (a) Probability of generation and reabsorption of a K-characteristic photon (PKR) and (b) Spatial distribution of K-characteristic radiation within the scintillator material. A custom validated Monte Carlo model was used, in order to simulate the transport of K-characteristic radiation within the above scintillator materials. Results showed that, depending on screen thickness (20-100 mg/cm2) and incident photon energy (20-80 keV) the scintillator's emission efficiency may be significantly reduced. © 2009 IOP Publishing Ltd and SISSA

Computer assisted characterization of cervical intervertebral disc degeneration in MRI

A texture-based pattern recognition system is proposed for the automatic characterization of cervical intervertebral disc degeneration from saggital magnetic resonance images of the spine. A case sample of 50 manually segmented ROIs, corresponding to 25 normal and 25 degenerated discs, was analyzed and textural features were generated from each disc-ROI. Student's t-test verified the existence of statistically significant differences between textural feature values generated from normal and degenerated discs. This finding is indicative of disc image texture differentiation due to the degeneration of the disc. The generated features were employed in the design of a pattern recognition system based on the Least Squares Minimum Distance classifier. The system achieved a classification accuracy of 94{%} and it may be of value to physicians for the assessment of cervical intervertebral disc degeneration in MRI