Relationships of Antidepressant Medication With Its Various Factors Including Nitrogen Dioxides Seasonality: Machine Learning Analysis Using National Health Insurance Data

Objective@#This study employs machine learning and population-based data to examine major factors of antidepressant medication including nitrogen dioxides (NO2) seasonality. @*Methods@#Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants with the age of 15–79 years, residence in the same districts of Seoul and no history of antidepressant medication during 2002–2012. The dependent variable was antidepressant-free months during 2013–2015 and the 103 independent variables for 2012 or 2015 were considered, e.g., particulate matter less than 2.5 micrometer in diameter (PM2.5), PM10, NO2, ozone (O3), sulphur dioxide (SO2) and carbon monoxide (CO) in each of 12 months in 2015. @*Results@#It was found that the Cox hazard ratios of NO2 were statistically significant and registered values larger than 10 for every three months: March, June–July, October, and December. Based on random forest variable importance and Cox hazard ratios in brackets, indeed, the top 20 factors of antidepressant medication included age (0.0041 [1.69–2.25]), migraine and sleep disorder (0.0029 [1.82]), liver disease (0.0017 [1.33–1.34]), exercise (0.0014), thyroid disease (0.0013), cardiovascular disease (0.0013 [1.20]), asthma (0.0008 [1.19–1.20]), September NO2 (0.0008 [0.01]), alcohol consumption (0.0008 [1.31–1.32]), gender - woman (0.0007 [1.80–1.81]), July NO2 (0.0007 [14.93]), July PM10 (0.0007), the proportion of the married (0.0005), January PM2.5 (0.0004), September PM2.5 (0.0004), chronic obstructive pulmonary disease (0.0004), economic satisfaction (0.0004), January PM10 (0.0003), residents in welfare facilities per 1,000 (0.0003 [0.97]), and October NO2 (0.0003). @*Conclusion@#Antidepressant medication has strong associations with neighborhood conditions including NO2 seasonality and welfare support.

Artificial intelligence in obstetrics

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data

Background@#To analyze the factors associated with women's vasomotor symptoms (VMS) using machine learning. @*Methods@#Data on 3,298 women, aged 40–80 years, who attended their general health check-up from January 2010 to December 2012 were obtained from Korea University Anam Hospital in Seoul, Korea. Five machine learning methods were applied and compared for the prediction of VMS, measured by the Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying the major factors associated with VMS. @*Results@#In terms of the mean squared error, the random forest (0.9326) was much better than linear regression (12.4856) and artificial neural networks with one, two, and three hidden layers (1.5576, 1.5184, and 1.5833, respectively). Based on the variable importance from the random forest, the most important factors associated with VMS were age, menopause age, thyroid-stimulating hormone, and monocyte, triglyceride, gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19-9, C-reactive protein, and low-density lipoprotein cholesterol levels. Indeed, the following variables were ranked within the top 20 in terms of variable importance: cancer antigen 125, total cholesterol, insulin, free thyroxine, forced vital capacity, alanine aminotransferase, forced expired volume in 1 second, height, homeostatic model assessment for insulin resistance, and carcinoembryonic antigen. @*Conclusion@#Machine learning provides an invaluable decision support system for the prediction of VMS. For managing VMS, comprehensive consideration is needed regarding thyroid function, lipid profile, liver function, inflammation markers, insulin resistance, monocyte count, cancer antigens, and lung function.

Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data

Background@#To analyze the factors associated with women's vasomotor symptoms (VMS) using machine learning. @*Methods@#Data on 3,298 women, aged 40–80 years, who attended their general health check-up from January 2010 to December 2012 were obtained from Korea University Anam Hospital in Seoul, Korea. Five machine learning methods were applied and compared for the prediction of VMS, measured by the Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying the major factors associated with VMS. @*Results@#In terms of the mean squared error, the random forest (0.9326) was much better than linear regression (12.4856) and artificial neural networks with one, two, and three hidden layers (1.5576, 1.5184, and 1.5833, respectively). Based on the variable importance from the random forest, the most important factors associated with VMS were age, menopause age, thyroid-stimulating hormone, and monocyte, triglyceride, gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19-9, C-reactive protein, and low-density lipoprotein cholesterol levels. Indeed, the following variables were ranked within the top 20 in terms of variable importance: cancer antigen 125, total cholesterol, insulin, free thyroxine, forced vital capacity, alanine aminotransferase, forced expired volume in 1 second, height, homeostatic model assessment for insulin resistance, and carcinoembryonic antigen. @*Conclusion@#Machine learning provides an invaluable decision support system for the prediction of VMS. For managing VMS, comprehensive consideration is needed regarding thyroid function, lipid profile, liver function, inflammation markers, insulin resistance, monocyte count, cancer antigens, and lung function.

Association of Gastroesophageal Reflux Disease with Preterm Birth: Machine Learning Analysis

Background@#This study used machine learning and population data for testing the associations of preterm birth with gastroesophageal reflux disease (GERD) and periodontitis. @*Methods@#Retrospective cohort data came from Korea National Health Insurance Service claims data for all women who aged 25–40 years and gave births for the first time as singleton pregnancy during 2015–2017 (405,586 women). The dependent variable was preterm birth during 2015–2017 and the independent variables were GERD (coded as no vs. yes) for each of the years 2002–2014, periodontitis (coded as no vs. yes) for each of the years 2002–2014, age (year) in 2014, socioeconomic status in 2014 measured by an insurance fee, and region (city) (coded as no vs. yes) in 2014. Random forest variable importance was adopted for finding main predictors of preterm birth and testing its associations with GERD and periodontitis. @*Results@#Based on random forest variable importance, main predictors of preterm birth during 2015–2017 were socioeconomic status in 2014, age in 2014, GERD for the years 2012, 2014, 2010, 2013, 2007 and 2009, region (city) in 2014 and GERD for the year 2006. The importance rankings of periodontitis were relatively low. @*Conclusion@#Preterm birth has a stronger association with GERD than with periodontitis. For the prevention of preterm birth, preventive measures for GERD would be essential together with the improvement of socioeconomic status for pregnant women. Especially, it would be vital to promote active counseling for general GERD symptoms (neglected by pregnant women).

Determinants of Spontaneous Preterm Labor and Birth Including Gastroesophageal Reflux Disease and Periodontitis

Background@#Periodontitis is reported to be associated with preterm birth (spontaneous preterm labor and birth). Gastroesophageal reflux disease (GERD) is common during pregnancy and is expected to be related to periodontitis. However, little research has been done on the association among preterm birth, GERD and periodontitis. This study uses popular machine learning methods for analyzing preterm birth, GERD and periodontitis. @*Methods@#Data came from Anam Hospital in Seoul, Korea, with 731 obstetric patients during January 5, 1995 - August 28, 2018. Six machine learning methods were applied and compared for the prediction of preterm birth. Variable importance, the effect of a variable on model performance, was used for identifying major determinants of preterm birth. @*Results@#In terms of accuracy, the random forest (0.8681) was similar with logistic regression (0.8736). Based on variable importance from the random forest, major determinants of preterm birth are delivery and pregestational body mass indexes (BMI) (0.1426 and 0.1215), age (0.1211), parity (0.0868), predelivery systolic and diastolic blood pressure (0.0809 and 0.0763), twin (0.0476), education (0.0332) as well as infant sex (0.0331), prior preterm birth (0.0290), progesterone medication history (0.0279), upper gastrointestinal tract symptom (0.0274), GERD (0.0242), Helicobacter pylori (0.0151), region (0.0139), calcium-channel-blocker medication history (0.0135) and gestational diabetes mellitus (0.0130). Periodontitis ranked 22nd (0.0084). @*Conclusion@#GERD is more important than periodontitis for predicting and preventing preterm birth. For preventing preterm birth, preventive measures for hypertension, GERD and diabetes mellitus would be needed alongside the promotion of effective BMI management and appropriate progesterone and calcium-channel-blocker medications.

Artificial Intelligence Approaches to Social Determinants of Cognitive Impairment and Its Associated Conditions

Background@#and Purpose: This study uses an artificial-intelligence model (recurrent neural network) for evaluating the following hypothesis: social determinants of disease association in a middle-aged or old population are different across gender and age groups. Here, the disease association indicates an association among cerebrovascular disease, hearing loss and cognitive impairment. @*Methods@#Data came from the Korean Longitudinal Study of Ageing (2014–2016), with 6,060 participants aged 53 years or more, that is, 2,556 men, 3,504 women, 3,640 aged 70 years or less (70−), 2,420 aged 71 years or more (71+). The disease association was divided into 8 categories: 1 category for having no disease, 3 categories for having 1, 3 categories for having 2, and 1 category for having 3. Variable importance, the effect of a variable on model performance, was used for finding important social determinants of the disease association in a particular gender/age group, and evaluating the hypothesis above. @*Results@#Based on variable importance from the recurrent neural network, important social determinants of the disease association were different across gender and age groups:1) leisure activity for men; 2) parents alive, income and economic activity for women; 3) children alive, education and family activity for 70−; and 4) brothers/sisters cohabiting, religious activity and leisure activity for 70+. @*Conclusions@#The findings of this study support the hypothesis, suggesting the development of new guidelines reflecting different social determinants of the disease association across gender and age groups.

Determinants of Spontaneous Preterm Labor and Birth Including Gastroesophageal Reflux Disease and Periodontitis

Background@#Periodontitis is reported to be associated with preterm birth (spontaneous preterm labor and birth). Gastroesophageal reflux disease (GERD) is common during pregnancy and is expected to be related to periodontitis. However, little research has been done on the association among preterm birth, GERD and periodontitis. This study uses popular machine learning methods for analyzing preterm birth, GERD and periodontitis. @*Methods@#Data came from Anam Hospital in Seoul, Korea, with 731 obstetric patients during January 5, 1995 - August 28, 2018. Six machine learning methods were applied and compared for the prediction of preterm birth. Variable importance, the effect of a variable on model performance, was used for identifying major determinants of preterm birth. @*Results@#In terms of accuracy, the random forest (0.8681) was similar with logistic regression (0.8736). Based on variable importance from the random forest, major determinants of preterm birth are delivery and pregestational body mass indexes (BMI) (0.1426 and 0.1215), age (0.1211), parity (0.0868), predelivery systolic and diastolic blood pressure (0.0809 and 0.0763), twin (0.0476), education (0.0332) as well as infant sex (0.0331), prior preterm birth (0.0290), progesterone medication history (0.0279), upper gastrointestinal tract symptom (0.0274), GERD (0.0242), Helicobacter pylori (0.0151), region (0.0139), calcium-channel-blocker medication history (0.0135) and gestational diabetes mellitus (0.0130). Periodontitis ranked 22nd (0.0084). @*Conclusion@#GERD is more important than periodontitis for predicting and preventing preterm birth. For preventing preterm birth, preventive measures for hypertension, GERD and diabetes mellitus would be needed alongside the promotion of effective BMI management and appropriate progesterone and calcium-channel-blocker medications.

Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants

BACKGROUND: Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.

Prediction of Gestational Age at Birth using an Artificial Neural Networks in Singleton Preterm Birth

PURPOSE: The objective of the present study was to predict the gestational age at preterm birth using artificial neural networks for singleton pregnancy. METHODS: Artificial neural networks (ANNs) were used as a tool for the prediction of gestational age at birth. ANNs trained using obstetrical data of 125 cases, including 56 preterm and 69 non-preterm deliveries. Using a 36-variable obstetrical input set, gestational weeks at delivery were predicted by 89 cases of training sets, 18 cases of validating sets, and 18 cases of testing sets (total: 125 cases). After training, we validated the model by another 12 cases containing data of preterm deliveries. RESULTS: To define the accuracy of the developed model, we confirmed the correlation coefficient (R) and mean square error of the model. For validating sets, the correlation coefficient was 0.839, but R of testing sets was 0.892, and R of total 125 cases was 0.959. The neural networks were well trained, and the model predictions were relatively good. Furthermore, the model was validated with another dataset of 12 cases, and the correlation coefficient was 0.709. The error days were 11.58±13.73. CONCLUSION: In the present study, we trained the ANNs and developed the predictive model for gestational age at delivery. Although the prediction for gestational age at birth in singleton preterm birth was feasible, further studies with larger data, including detailed risk variables of preterm birth and other obstetrical outcomes, are needed.

Cost Effectiveness of Colorectal Cancer Screening Interventions with Their Effects on Health Disparity Being Considered / Journal of the Korean Cancer Association, 대한암학회지

PURPOSE: The purpose of this study was to evaluate the cost effectiveness of colorectal cancer screening interventions with their effects on health disparity being considered. MATERIALS AND METHODS: Markov cohort simulation was conducted with the cycle/duration of 1/40 year(s). Data came from the results of randomized trials and others. Participants were hypothetical cohorts aged 50 years as of year 2013 in 16 Korean provinces. The interventions until the age of 80 were annual organized fecal occult blood test (FOBT) (standard screening), annual FOBT with basic reminders for provinces with higher mortalities than the national average (targeted reminder) and annual FOBT with basic/enhanced reminders for all provinces (universal reminder 1 and 2). The comparison was non-screening, the outcome was quality-adjusted life years, and only medical costs for screening and treatment were considered from a societal perspective. The Atkinson incremental cost effectiveness ratio (Atkinson ICER), the incremental cost effectiveness ratio adjusted by the Atkinson Inequality Index, was used to evaluate the cost effectiveness of the four interventions with their impacts on regional health disparity being considered. RESULTS: Health disparity was smallest (or greatest) in non-screening (or the standard screening). The targeted reminder had smaller health disparity, and smaller Atkinson ICER with respect to standard screening, than did the universal reminder 1 and 2. CONCLUSION: The targeted reminder might be more cost effective than the universal reminders with their effects on health disparity being considered. This study helps to develop promotional effort for colorectal cancer screening with both the greatest cost effectiveness and the smallest health disparity.

Setting a Health Policy Research Agenda for Controlling Cancer Burden in Korea / Journal of the Korean Cancer Association, 대한암학회지

PURPOSE: The aim of study was to provide suggestions for prioritizing research in effort to reduce cancer burden in Korea based on a comprehensive analysis of cancer burden and Delphi consensus among cancer experts. MATERIALS AND METHODS: Twenty research plans covering 10 topics were selected based on an assessment of the literature, and e-mail surveys were analyzed using a two-round modified Delphi method. Thirty-four out of 79 experts were selected from four organizations to participate in round one, and 21 experts among them had completed round two. Each item had two questions; one regarding the agreement of the topic as a priority item to reduce cancer burden, and the other about the importance of the item on a nine-point scale. A consensus was defined to be an average lower coefficient of variation with less than 30% in importance. RESULTS: Seven plans that satisfied the three criteria were selected as priority research plans for reducing cancer burden. These plans are "research into advanced clinical guidelines for thyroid cancer given the current issue with over-diagnosis," "research into smoking management plans through price and non-price cigarette policy initiatives," "research into ways to measure the quality of cancer care," "research on policy development to expand hospice care," "research into the spread and management of Helicobacter pylori," "research on palliative care in a clinical setting," and "research into alternative mammography methods to increase the accuracy of breast cancer screenings." CONCLUSION: The seven plans identified in this study should be prioritized to reduce the burden of cancer in Korea. We suggest that policy makers and administrators study and invest significant effort in these plans.

Economic Burden of Cancer in Korea during 2000-2010 / Journal of the Korean Cancer Association, 대한암학회지

PURPOSE: This study estimated the economic burden of cancer in Korea during 2000-2010 by cancer site, gender, age group, and cost component. MATERIALS AND METHODS: Data came from national health insurance claims data and information from Statistics Korea. Based on the cost of illness method, this study calculated direct, morbidity and mortality cost of cancer in the nation during 2000-2010 by cancer site, gender, and age group. RESULTS: With an average annual growth rate of 8.9%, the economic burden of cancer in Korea increased from 11,424 to 20,858 million US$ (current US dollars) during 2000-2010. Colorectal, thyroid, and breast cancers became more significant during the period, i.e., the 5th/837, the 11th/257, and the 7th/529 in 2000 to the 3rd/2,210, the 5th/1,724, and the 6th/1,659 in 2010, respectively (rank/amount in million US$ for the total population). In addition, liver and stomach cancers were prominent during the period in terms of the same measures, i.e., the 1st/2,065 and the 2nd/2,036 in 2000 to the 1st/3,114 and the 2nd/3,046 in 2010, respectively. Finally, the share of mortality cost in the total burden dropped from 71% to 51% in Korea during 2000-2010, led by colorectal, thyroid, breast, and prostate cancers during the period. These results show that the economic burden of cancer in Korea is characterized by an increasing importance of chronic components. CONCLUSION: Incorporation of distinctive epidemiological, sociocultural contexts into Korea's cancer control program, with greater emphasis on primary prevention such as sodium-controlled diet and hepatitis B vaccination, may be needed.

Patterns of Alcohol Consumption and Suicidal Behavior: Findings From the Fourth and Fifth Korea National Health and Nutritional Examination Survey (2007-2011) / 예방의학회지

OBJECTIVES: The purpose of this study was to investigate the association between suicidal behavior and patterns of alcohol consumption in Korean adults. METHODS: This study was based on data provided by the Korea National Health and Nutritional Examination Survey from 2007 to 2011. A total of 42 347 subjects were included in the study, of whom 19 292 were male and 23 055 were female. Logistic regression analysis was performed to assess the association between patterns of alcohol consumption and suicidal behavior. RESULTS: Among the study subjects, 1426 males (11.3%) and 3599 females (21.2%) had experienced suicidal ideation, and 106 males (0.8%) and 190 females (1.1%) had attempted suicide during the previous 12 months. Alcohol Use Disorders Identification Test (AUDIT) scores were found to be associated with suicidal ideation in males and associated with both suicidal ideation and suicide attempts in females. Alcoholic blackouts were associated with suicidal ideation and suicide attempts in males, and were also associated with suicidal ideation in females. CONCLUSIONS: In this study, we found that certain patterns of alcohol consumption were associated with suicidal behaviors. In particular, only alcoholic blackouts and categorized AUDIT scores were found to be associated with suicidal behavior in males. We therefore suggest that further research is needed to examine this relationship prospectively and in other settings.

Relationship Between Current Sleep Duration and Past Suicidal Ideation or Attempt Among Korean Adolescents / 예방의학회지

OBJECTIVES: To comprehensively examine the relationship between current sleep duration and past suicidal idea or attempt among Korean adolescents. METHODS: Data came from the 2009 Korea Youth Risk Behavior Web-based Survey with 75 066 participants (with the participation rate of 97.6%) in 800 middle and high schools. Binary logistic regression was conducted by gender and depressed mood to identify significant factors for suicidal ideation/attempt. The dependent variable was the log odds of suicidal ideation/attempt, while the independent/control variables were sleep duration and other demographic, socio-economic and health-related factors. RESULTS: A negative association between sleep duration and suicidal ideation or attempt was weaker for those with depressed mood than for those without such experience in Korea for Year 2009. The odds ratio of suicidal ideation/attempt regarding less than 4 hours of sleep compared to 6 to 7 hours of sleep, was smaller in a group with depressed mood than in a group without such experience, for example, 1.64 (95% confidence interval [CI], 1.29 to 2.08) vs. 2.06 (95% CI, 1.34 to 3.17) for men's suicidal ideation, 2.50 (95% CI, 1.69 to 3.69) vs. 3.89 (95% CI, 1.74 to 8.66) for men's suicidal attempt. A negative association between age (or self-rated health) and suicidal ideation/attempt was also weaker for those with depressed mood than for those without such experience in the nation for the year. CONCLUSIONS: There was a negative association between sleep duration and suicidal ideation/attempt in Korea for Year 2009 and this association was weaker for those with depressed mood than for those without such experience. Based on the findings of this study, adolescents' better mental health and longer, more comfortable sleep might help to prevent their suicidal ideation and attempt in Korea.

Disability Weights for Cancers in Korea

This study renewed the estimation of disability weights for cancers in Korea, reflecting the nation's economic and medical-technological development during the past 10 yr. Thirty-two medical doctors evaluated disability weights for 24 major cancers based on the visual analogue scale (VAS) method. To check the intra-rater reliability, a correlation was calculated between 2011 and 2012 medians. To assess the inter-rater reliability, a correlation was estimated between oncologist and non-oncologist medians. To assess the inter-method reliability, a correlation was calculated between medians on VAS and Person-Trade-Off approaches. Moreover, findings in this study were compared to those in 2003 research. Spearman correlation was used and the 1% significance level was applied. Disability weights were relatively high for pancreas cancer (0.90), gallbladder cancer (0.81), mouth and oropharynx cancer (0.80), and esophagus cancer (0.80). Conversely, they were relatively low for breast cancer (0.37), prostate cancer (0.33) and thyroid cancer (0.10). All the inter-rater reliabilities were higher than 0.7. Indeed, the intra-rater and inter-method reliabilities were 0.752 and 0.927, respectively. Above all, disability weights for major cancers went down in Korea during 2003-2012, reflecting the progress of medical technology and the growth of cancer survival.

Time Resolution Improvement of MRI Temperature Monitoring Using Keyhole Method

PURPOSE: This study proposes the keyhole method in order to improve the time resolution of the proton resonance frequency(PRF) MR temperature monitoring technique. The values of Root Mean Square (RMS) error of measured temperature value and Signal-to-Noise Ratio(SNR) obtained from the keyhole and full phase encoded temperature images were compared. MATERIALS AND METHODS: The PRF method combined with GRE sequence was used to get MR temperature images using a clinical 1.5T MR scanner. It was conducted on the tissue-mimic 2% agarose gel phantom and swine's hock tissue. A MR compatible coaxial slot antenna driven by microwave power generator at 2.45GHz was used to heat the object in the magnetic bore for 5 minutes followed by a sequential acquisition of MR raw data during 10 minutes of cooling period. The acquired raw data were transferred to PC after then the keyhole images were reconstructed by taking the central part of K-space data with 128, 64, 32 and 16 phase encoding lines while the remaining peripheral parts were taken from the 1st reference raw data. The RMS errors were compared with the 256 full encoded self-reference temperature image while the SNR values were compared with the zero filling images. RESULTS: As phase encoding number at the center part on the keyhole temperature images decreased to 128, 64, 32 and 16, the RMS errors of the measured temperature increased to 0.538, 0.712, 0.768 and 0.845degrees C, meanwhile SNR values were maintained as the phase encoding number of keyhole part is reduced. CONCLUSION: This study shows that the keyhole technique is successfully applied to temperature monitoring procedure to increases the temporal resolution by standardizing the matrix size, thus maintained the SNR values. In future, it is expected to implement the MR real time thermal imaging using keyhole method which is able to reduce the scan time with minimal thermal variations.

Preliminary Study on the MR Temperature Mapping using Center Array-Sequencing Phase Unwrapping Algorithm

PURPOSE: To investigate the feasibility and accuracy of Proton Resonance Frequency (PRF) shift based magnetic resonance (MR) temperature mapping utilizing the selfdeveloped center array-sequencing phase unwrapping (PU) method for non-invasive temperature monitoring. MATERIALS AND METHODS: The computer simulation was done on the PU algorithm for performance evaluation before further application to MR thermometry. The MR experiments were conducted in two approaches namely PU experiment, and temperature mapping experiment based on the PU technique with all the image postprocessing implemented in MATLAB. A 1.5T MR scanner employing a knee coil with T2* GRE (Gradient Recalled Echo) pulse sequence were used throughout the experiments. Various subjects such as water phantom, orange, and agarose gel phantom were used for the assessment of the self-developed PU algorithm. The MR temperature mapping experiment was initially attempted on the agarose gel phantom only with the application of a custom-made thermoregulating water pump as the heating source. Heat was generated to the phantom via hot water circulation whilst temperature variation was observed with T-type thermocouple. The PU program was implemented on the reconstructed wrapped phase images prior to map the temperature distribution of subjects. As the temperature change is directly proportional to the phase difference map, the absolute temperature could be estimated from the summation of the computed temperature difference with the measured ambient temperature of subjects. RESULTS: The PU technique successfully recovered and removed the phase wrapping artifacts on MR phase images with various subjects by producing a smooth and continuous phase map thus producing a more reliable temperature map. CONCLUSION: This work presented a rapid, and robust self-developed center arraysequencing PU algorithm feasible for the application of MR temperature mapping according to the PRF phase shift property.