• Progress in Medical Physics
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• v.24 no.2
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• pp.127-132
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• 2013

The position of the internal organs can change continually and periodically inside the body due to the respiration. To reduce the respiration induced uncertainty of dose localization, one can use a respiratory gated radiotherapy where a radiation beam is exposed during the specific time of period. The main disadvantage of this method is that it usually requests a long treatment time, the massive effort during the treatment and the limitation of the patient selection. In this sense, the combination of the real-time position management (RPM) system and the volumetric intensity modulated radiotherapy (RapidArc) is promising since it provides a short treatment time compared with the conventional respiratory gated treatments. In this study, we evaluated the accuracy of the respiratory gated RapidArc treatment. Total sic patient cases were used for this study and each case was planned by RapidArc technique using varian ECLIPSE v8.6 planning machine. For the Quality Assurance (QA), a MatriXX detector and I'mRT software were used. The results show that more than 97% of area gives the gamma value less than one with 3% dose and 3 mm distance to agreement condition, which indicates the measured dose is well matched with the treatment plan's dose distribution for the gated RapidArc treatment cases.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.