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Purpose There is substantial need for optimizing radiation protection in nuclear medicine imaging studies. However, the diagnostic reference levels (DRLs) have not yet been established for nuclear medicine imaging studies in Korea. Materials and Methods The data of administered activity in 32 nuclear medicine imaging studies were collected from the Korean Society of Nuclear Medicine (KSNM) dose survey database from 2013 and 2014. Through the expert discussions and statistical analyses, the 75th quartile value (Q3) was suggested as the preliminary DRL values. Preliminary DRLs were subjected to approval process by the KSNM Board of Directors and KSNM Council, followed by clinical applications and performance rating by domestic institutes. Results DRLs were determined through 32 nuclear medicine imaging studies. The Q3 value was considered as appropriate selection as it was generally consistent with the most commonly administered activity. In the present study, the final version of initial DRL values for nuclear medicine imaging in Korean adults is described including various protocols of the brain and myocardial perfusion imaging. Conclusion The first DRLs for nuclear medicine imaging in Korean adults were confirmed. The DRLs will enable optimized radiation protection in the field of nuclear medicine imaging in Korea.

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By means of a novel time-dependent cumulated variation penalty function, a new class of real-time prediction methods is developed to improve the prediction accuracy of time series exhibiting irregular periodic patterns: in particular, the breathing motion data of the patients during robotic radiation therapy. It is illustrated that for both simulated and empirical data involving changes in mean, trend, and amplitude, the proposed methods outperform existing forecasting methods based on support vector machines and artificial neural network in terms of prediction accuracy. Moreover, the proposed methods are designed so that real-time updates can be done efficiently with O(1) computational complexity upon the arrival of a new signal without scanning the old data repeatedly.

In this paper we propose a family of tests for exponentiality against the IDMRL alternative. Here we assume that the turning point or the proportion before the turning point is unknown. We derive the asymptotic null distributions of the test statistics and obtain their asymptotic critical values based on Durbin's approximation method. A simulation study is conducted to evaluate the proposed tests.

F-18 fluorodeoxyglucose (FDG)-avid metastatic lesions are associated with a poor response to radioiodine ablation therapy (RIT) in papillary thyroid cancer (PTC). This study evaluated the significance of preablative FDG positron emission tomography (PET) for the assessment of risk factors and frequency of malignant FDG-avid lymph nodes in patients with PTC undergoing RIT. The study included 339 consecutive patients (mean age 46.3 ± 12.5 y; 260 females) with PTC referred for the first RIT and who underwent routine preablative FDG PET between April 2011 and February 2013. FDG-avid lymph nodes (FALNs) were identified using retrospective image reviews. The frequency of malignant FALN (mFALN), its contribution to persistent or recurrent PTC, and its risk factors were analyzed. Among the patients, 112 had FALNs (33.0%): 11 mFALNs (3.2%) and 101 benign FALNs (bFALNs, 29.8%). mFALN contributed to 55% of persistent or recurrent PTC after RIT, which was observed in 20 of 339 patients (5.9%) during the post-RIT follow-up. Among preoperative risk factors, suspicious extrathyroidal extension and lateral neck lymph node metastasis on imaging studies were associated with mFALN. Among postoperative risk factors, T3/T4 and N1b stages, higher stimulated thyroglobulin, and higher numbers of metastatic lymph nodes and dissected lymph nodes, were associated with mFALN. mFALNs were observed in a small number of patients with PTC undergoing RIT, but it contributed 55% of total recurrent or persistent disease. Increased frequency of mFALNs is associated with more advanced PTC. Preablative FDG PET has value in evaluation of patients with RIT-resistant lesions and may help determine further treatment strategies.

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Weighting the Hanwoo (Korean cattle) is very important for Korean beef producers when selling the Hanwoo at the right time. Recently, research is being conducted on the automatic prediction of the weight of Hanwoo only through images with the achievement of research using deep learning and image recognition. In this paper, we propose a method for the automatic weight prediction of Hanwoo using the Bayesian ridge algorithm on RGB-D images. The proposed system consists of three parts: segmentation, extraction of features, and estimation of the weight of Korean cattle from a given RGB-D image. The first step is to segment the Hanwoo area from a given RGB-D image using depth information and color information, respectively, and then combine them to perform optimal segmentation. Additionally, we correct the posture using ellipse fitting on segmented body image. The second step is to extract features for weight prediction from the segmented Hanwoo image. We extracted three features: size, shape, and gradients. The third step is to find the optimal machine learning model by comparing eight types of well-known machine learning models. In this step, we compared each model with the aim of finding an efficient model that is lightweight and can be used in an embedded system in the real field. To evaluate the performance of the proposed weight prediction system, we collected 353 RGB-D images from livestock farms in Wonju, Gangwon-do in Korea. In the experimental results, random forest showed the best performance, and the Bayesian ridge model is the second best in MSE or the coefficient of determination. However, we suggest that the Bayesian ridge model is the most optimal model in the aspect of time complexity and space complexity. Finally, it is expected that the proposed system will be casually used to determine the shipping time of Hanwoo in wild farms for a portable commercial device.