## 3 Results and Discussion  
From the searches of the EMBASE, MEDLINE EBSCO, and Cochrane databases, we identified 82 studies; a total of 57 papers (count after duplicates removed) were initially included. When we searched for papers, we set the search fields as the abstract, title, and keywords, and in the MEDLINE-EBSCO engine, we searched the abstract and title separately using \"OR\". These papers were then screened based on the inclusion and exclusion criteria. After this screening, the following papers were excluded: conference papers (\(n=9\)); papers with a title and abstract that are inappropriate, including papers on segmentation, registration, computed tomography, positron emission tomography, dictionary learning processing, or a different algorithm (\(n=6\)); a review paper (\(n=1\)); a paper not including an image (\(n=1\)); papers that did not express the MR imaging technique (\(n=4\)); and papers that did not include a noise or quantitative evaluation (\(n=6\)). Finally, of the 57 unique papers 25 were then investigated using their full text and had their common data sections organized [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. The specific flowchart of the method used is presented in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Search Engine** & **Search Strategies** \\ \hline EMBASE & Denoising (candidate term) or Noise reduction (Emtree) or Noise removal (AND) \\ EMBASE & Non-local means or NLM or Non local means (AND) \\ Nuclear magnetic resonance imaging (Emtree) or MRI or Magnetic resonance imaging or MR image \\ \hline MEDLINE EBSCO & Non-local means or NLM or Non local means (AND) \\ Nuclear magnetic resonance imaging or MRI or Magnetic resonance imaging (MeSH term) or MR image \\ \hline \end{tabular}
\begin{tabular}{c c} \hline
**Search Engine** & **Search Strategies** \\ \hline  & Denoising (candidate term) or Noise reduction (Emtree) or Noise removal (AND) \\ EMBASE & Non-local means or NLM or Non local means (AND) \\ Nuclear magnetic resonance imaging (Emtree) or MRI or Magnetic resonance imaging or MR image \\ \hline  & Denoising or Noise Reduction or Noise removal (AND) \\ MEDLINE EBSCO & Non-local means or NLM or Non local means (AND) \\ Nuclear magnetic resonance imaging or MRI or Magnetic resonance imaging (MeSH term) or MR image \\ \hline \end{tabular}
\begin{tabular}{c c} \hline
**Cochrane** & **Non-local means or NLM or Non local means (AND) \\ Nuclear magnetic resonance imaging or MRI or Magnetic resonance imaging (MeSH descriptor) or MR image \\ \hline \end{tabular}
\end{table}
Table 1: Search strategies using the EMBASE, MEDILINE EBSCO, and Cochrane engines.  
Figure 3 shows the total number of papers that quantitatively analyze the use and effectiveness of the NLM noise reduction algorithm in MR images by publication year. The graph shows the overall Gaussian distribution over time after a polynomial fitting function (r-square (COD) = 0.131 and standard deviation = 1.283) was used. In addition, the number of publications of related papers tended to increase as the year increased; the highest number of publications per year was four, occurring three times, in 2010, 2015, and 2016 (orange lines in Figure 3).  
Figure 3: Graph of the number of publications per year, including the polynomial fitting curve.  
Figure 2: Flowchart of the method used for selection of publications. Through searches of three search databases, 52 papers were identified based on our criteria after removing duplicate articles.