Status of radiation protection at different hospitals in Nepal.

Nepal has a long history of medical radiology since1923 but unfortunately, we still do not have any Radiation Protection Infrastructure to control the use of ionizing radiations in the various fields. The objective of this study was an assessment of the radiation protection in medical uses of ionizing radiation. Twenty-eight hospitals with diagnostic radiology facility were chosen for this study according to patient loads, equipment and working staffs. Radiation surveys were also done at five different radiotherapy centers. Questionnaire for radiation workers were used; radiation dose levels were measured and an inventory of availability of radiation equipment made. A corollary objective of the study was to create awareness in among workers on possible radiation health hazard and risk. It was also deemed important to know the level of understanding of the radiation workers in order to initiate steps towards the establishment of Nepalese laws, regulation and code of radiological practice in this field. Altogether, 203 Radiation workers entertained the questionnaire, out of which 41 are from the Radiotherapy and 162 are from diagnostic radiology. The radiation workers who have participated in the questionnaire represent more than 50% of the radiation workers working in this field in Nepal. Almost all X-ray, CT and Mammogram installations were built according to protection criteria and hence found safe. Radiation dose level at the reference points for all the five Radiotherapy centers are within safe limit. Around 65% of the radiation workers have never been monitored for radiation. There is no quality control program in any of the surveyed hospitals except radiotherapy facilities.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).