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Rejestracja i klasyfikacja zdarzeń niepożądanych w radioterapii w Europie. Cz. 1

Kowalik A., Skrobała A.

Inżynier i Fizyk Medyczny

2015

Vol. 4, nr 5

293--300

Ocena ryzyka, kontrola jakości i bezpieczeństwa w radioterapii w dobie nowoczesnych technologii i coraz bardziej skomputeryzowanych systemów odgrywa coraz większą rolę w procesie radioterapii. Szybki rozwój technologii oraz rosnąca liczba pacjentów przyczyniają się do powstawania sytuacji, w których istnieje większe ryzyko wystąpienia zdarzeń niepożądanych (ang. adverse events) zwłaszcza w przypadku niewystarczającej liczby wykwalifikowanego personelu i specjalistycznych szkoleń. Dane dotyczące zdarzeń niepożądanych i ich raportowania dotyczą tylko kilku krajów. W krajach tych kultura bezpieczeństwa jest na tyle rozwinięta, że utworzono systemy do klasyfikacji i rejestracji zdarzeń niepożądanych w radioterapii. Funkcjonujące systemy do rejestracji i klasyfikacji zdarzeń niepożądanych w radioterapii mają jeden wspólny cel, a mianowicie utworzenie bazy danych, na podstawie której możliwymbędzie uniknięcie niechcianych zdarzeń w przyszłości poprzez szerzenie kultury bezpieczeństwa. W chwili obecnej podstawowymi systemami do rejestracji i klasyfikacji zdarzeń niepożądanych w Europie są ROSIS i SAFRON.

Nowadays the risk assessment, quality control and safety in radiotherapy on the era of modern technologies and increasingly computerized systems plays an increasingly important role in radiotherapy. The rapid technology development and the increasing number of patients is conducive to a situation in which the greater risk of adverse events occurs. Especially in the situation of insufficient number of qualified staff and lack of specialized trainings. The data about adverse events and reporting them concern only a few countries, where the safety culture is so developed that the systems for classifying and reporting adverse events in radiation therapy are already established. The systems for registration and classification adverse events in radiotherapy have one common goal to create a database on the basis of which it is possible to avoid similar events in the future by spreading a safety culture. Currently, the major systems for registration and classification of adverse events in Europe are ROSIS and SAFRON.

Zróżnicowanie kosodrzewiny w Tatrach, w świetle badań teledetekcyjnych

Zwijacz-Kozica M.

Teledetekcja Środowiska

2010

T. 44

1--61

Dwarf mountain pine (Pinus mugo Turra) is the main component in the subalpine belt in the Tatra National Park, where the study was conducted. From the ecological point of view dwarf pine plays an important role in the sensitive mountainous area. Until now there were no studies focused on structure of dwarf pine community and there were also no attempts to work out methodology for detailed qualitative and quantitative description of dwarf pine. In this study for the first time it was aimed to prepare methodology of dwarf pine characterization and monitoring using hyperspectral data. Analysis involved processing of airborne and satellite images data and field measurements. Presented study evaluated linear predictive models between vegetation indices derived from radiometrically corrected air- borne imaging spectrometer ROSIS, spectral field and laboratory measurements and field measurements of dwarf pine biophysical variables (LAI, fAPAR). Narrow band vegetation indices were computed on the basis of all possible two-band combinations of set of vegetation indices (VI, NDVI, PVI, SAVI2, TSAVI). VI based on ROSIS wavebands 510 nm and 630 nm was linearly related to leaf area index (R2=0,48). VI and NDVI based on FieldSpec HH wavebands 886 nm and 518 nm performed better and were linearly related to LAI (R2=0,72). TSAVI based on ROSIS wavebands 658 nm and 570 nm was linearly related to the fraction of absorbed photosynthetically active radiation (R2=0,72). SAVI2 based on FieldSpec HH wavebands 747 nm and 703 nm was linearly related to fAPAR (R2=0,81). Analysed indices of vegetation condition were correlated (R2>0,90) with spectral vegetation indices based on FieldSpec Pro laboratory data. The study shows that for hyperspectral image data covering spectral region of visible light and near infrared, linear regression models can be applied to quantify LAI and fAPAR with satisfying accuracy. Models involving spectral information from sensors that have wider spectral range have better potential to linearly quantify biophysical vegetation parameters involving spectral vegetation indices. Vegetation indices that have the best relation to LAI and fAPAR were based on wavebands related to spectral features. It can be assumed that hyperspectral data contain information relevant to the estimation of vegetation biophysical parameters. In this study it was investigated if dwarf pine community differs spectrally within study site. To assess presence and extent of the spectral differentiation the set of field and laboratory spectral measurements were used. Reflectance curves were compared visually and using the statistical test. It was demonstrated that the majority of the studied dwarf pine plots have a characteristic signature. Parts of the electromagnetic spectrum which offer greatest information content for discriminating between and identifying dwarf pine spectral types were indicated. It was also examined if any of abiotic components of environment (altitude above sea level, aspect, slope, soil type, geology, global radiation and temperature) has an influence on the spatial distribution of LAI and fAPAR values. WMP (index of tie strength) and MP (tie strength) were used to assess an extent of the influence. It was found that neither of investigated abiotic factors affects LAI and fAPAR values.