The intrinsic phosphor properties are of significant importance for the performance of phosphor screens used in medical imaging systems. In previous analytical-theoretical and Monte Carlo studies on granular phosphor materials, values of optical properties, and light interaction cross sections were found by fitting to experimental data. These values were then employed for the assessment of phosphor screen imaging performance. However, it was found that, depending on the experimental technique and fitting methodology, the optical parameters of a specific phosphor material varied within a wide range of values, i.e., variations of light scattering with respect to light absorption coefficients were often observed for the same phosphor material. In this study, x-ray and light transport within granular phosphor materials was studied by developing a computational model using Monte Carlo methods. The model was based on the intrinsic physical characteristics of the phosphor. Input values required to feed the model can be easily obtained from tabulated data. The complex refractive index was introduced and microscopic probabilities for light interactions were produced, using Mie scattering theory. Model validation was carried out by comparing model results on x-ray and light parameters (x-ray absorption, statistical fluctuations in the x-ray to light conversion process, number of emitted light photons, output light spatial distribution) with previous published experimental data on Gd2O2S: Tb phosphor material (Kodak Min-R screen). Results showed the dependence of the modulation transfer function (MTF) on phosphor grain size and material packing density. It was predicted that granular Gd2O2S: Tb screens of high packing density and small grain size may exhibit considerably better resolution and light emission properties than the conventional Gd2O2S: Tb screens, under similar conditions (x-ray incident energy, screen thickness).

The objective of the present project was the determination of the dose received by patients during cardiac procedures, such as coronary angiography, percutaneous transluminal coronary angioplasty (PTCA) and stent implantation. Thermoluminescent dosemeters (TLDs), suitably calibrated, were used for the measurement of the dose received at four anatomical locations on the patient's skin. A dose-area product (DAP) meter was also used. The contribution of cinefluorography to the total DAP was higher than that of fluoroscopy. A DAP to effective dose conversion factor equal to 0.183 mSv Gy-1 cm-2 was estimated with the help of a Rando phantom. Thus, the effective dose received by the patients could be assessed. Mean values of effective dose equal to 5.6 mSv, 6.9 mSv, 9.3 mSv, 9.0 mSv and 13.0 mSv were estimated for coronary angiography, PTCA, coronary angiography and ad hoc PTCA, PTCA followed by stent implantation and coronary angiography and ad hoc PTCA followed by stent implantation, respectively.

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Measures have to be taken to reduce patient's skin dose, which, in extreme cases, may be close to deterministic effects threshold. The highest dose rates, recorded during the procedure, were found for primary operator's hands and chest when no shielding was used.

The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the Digital Database for Screening Mammography. Mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Laws' texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve ( A(z)) of 0.989. Results suggest that MCs' ST texture analysis can contribute to computer-aided diagnosis of breast cancer.