Fast non-local means noise reduction algorithm with acceleration function for improvement of image quality in gamma camera system: A phantom study

The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020; of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide; however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique; these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal.

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Performance evaluation of noise reduction algorithm with median filter using improved thresholding method in pixelated semiconductor gamma camera system: A numerical simulation study

Nuclear Engineering and Technology ◽

10.1016/j.net.2018.10.005 ◽

2019 ◽

Vol 51 (2) ◽

pp. 439-443 ◽

Cited By ~ 2

Author(s):

Youngjin Lee

Keyword(s):

Numerical Simulation ◽

Performance Evaluation ◽

Simulation Study ◽

Gamma Camera ◽

Median Filter ◽

Reduction Algorithm ◽

Camera System ◽

System A ◽

Noise Reduction Algorithm ◽

Semiconductor Gamma

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Experimental study of the fast non-local means noise reduction algorithm using the Hoffman 3D brain phantom in nuclear medicine SPECT image

Optik ◽

10.1016/j.ijleo.2020.165440 ◽

2020 ◽

Vol 224 ◽

pp. 165440

Author(s):

Min Young Jang ◽

Chan Rok Park ◽

Seong-Hyeon Kang ◽

Youngjin Lee

Keyword(s):

Experimental Study ◽

Nuclear Medicine ◽

Noise Reduction ◽

Reduction Algorithm ◽

Local Means ◽

Spect Image ◽

Non Local ◽

Noise Reduction Algorithm ◽

Brain Phantom

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Evaluation of the image quality and dose reduction in digital radiography with an advanced spatial noise reduction algorithm in pediatric patients

European Radiology ◽

10.1007/s00330-021-07942-6 ◽

2021 ◽

Author(s):

Saelin Oh ◽

Ji Hye Kim ◽

So-Young Yoo ◽

Tae Yeon Jeon ◽

Yu Jin Kim

Keyword(s):

Image Quality ◽

Noise Reduction ◽

Dose Reduction ◽

Digital Radiography ◽

Pediatric Patients ◽

Reduction Algorithm ◽

Spatial Noise ◽

Noise Reduction Algorithm

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Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study

Diagnostics ◽

10.3390/diagnostics11020319 ◽

2021 ◽

Vol 11 (2) ◽

pp. 319

Author(s):

Chan-Rok Park ◽

Seong-Hyeon Kang ◽

Young-Jin Lee

Keyword(s):

Image Quality ◽

Noise Reduction ◽

Total Variation ◽

Parallel Imaging ◽

Pulse Sequence ◽

Reduction Algorithm ◽

Median Filters ◽

Reduction Techniques ◽

Imaging Results ◽

Noise Reduction Algorithm

Recently, the total variation (TV) algorithm has been used for noise reduction distribution in degraded nuclear medicine images. To acquire positron emission tomography (PET) to correct the attenuation region in the PET/magnetic resonance (MR) system, the MR Dixon pulse sequence, which is based on controlled aliasing in parallel imaging, results from higher acceleration (CAIPI; MR-ACDixon-CAIPI) and generalized autocalibrating partially parallel acquisition (GRAPPA; MR-ACDixon-GRAPPA) algorithms are used. Therefore, this study aimed to evaluate the image performance of the TV noise reduction algorithm for PET/MR images using the Jaszczak phantom by injecting 18F radioisotopes with PET/MR, which is called mMR (Siemens, Germany), compared with conventional noise-reduction techniques such as Wiener and median filters. The contrast-to-noise (CNR) and coefficient of variation (COV) were used for quantitative analysis. Based on the results, PET images with the TV algorithm were improved by approximately 7.6% for CNR and decreased by approximately 20.0% for COV compared with conventional noise-reduction techniques. In particular, the image quality for the MR-ACDixon-CAIPI PET image was better than that of the MR-ACDixon-GRAPPA PET image. In conclusion, the TV noise-reduction algorithm is efficient for improving the PET image quality in PET/MR systems.

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