Study on Feasibility for Artificial Intelligence (AI) Noise Reduction Algorithm with Various Parameters in Pediatric Abdominal Radio-Magnetic Computed Tomography (CT)

Kim, Seung Hun; Seo, Kanghyen; Kang, Seong-Hyeon; Bae, S H; Kwak, Hyeng Ju; Hong, Ji Woo; Hwang, Yoonji; Kang, Sa-Ouk; Choi, Hye Ran; Kim, Ga Yun; Lee, Youngjin

doi:10.4283/jmag.2017.22.4.570

Cited by 7 publications

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“…Sixteen articles met the selection criteria and were included in this review. Table 1 shows these study characteristics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. All but one article were published in the last two years, representing that the AI for dose optimization in pediatric radiology has only just become popular [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…All but one article were published in the last two years, representing that the AI for dose optimization in pediatric radiology has only just become popular [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Nearly all (14) studies were determined high quality [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], and the lower quality ones were “pure” phantom studies [ 2 , 3 ]. All studies used the DL technique [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] and were conducted by 12 groups of researchers from USA ( n = 8) [ 1 , 8 , 9 , 10 , 11 , 12 ...…”

Section: Resultsmentioning

confidence: 99%

“…Nearly all (14) studies were determined high quality [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], and the lower quality ones were “pure” phantom studies [ 2 , 3 ]. All studies used the DL technique [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] and were conducted by 12 groups of researchers from USA ( n = 8) [ 1 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], People’s Republic of China ( n = 6) [ 8 , 9 , 10 , 11 , 12 , 16 ], Republic of Korea ( n = 5) [ 2 , 3 , 5 , 7 , 15 ], Germany ( n = 2) [ 4 , 14 ], and Japan ( n ...…”

Section: Resultsmentioning

confidence: 99%

“…Radiology is an indispensable part of modern healthcare. However, most of the medical imaging modalities, such as computed tomography (CT), positron emission tomography (PET), and general radiography, use ionizing radiation for image production [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Although the radiation dose involved in these imaging modalities is low (<100 mSv), and their real risk is unclear, some epidemiologic and biologic studies have demonstrated that these radiological examinations can cause cancers [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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“…To solve this problem, there is a technique to increase the number of photons by injecting high radioactivity, but the patient receives a lot of radiation exposure. Therefore, many researchers have proposed noise-reduction algorithms without increasing the radiation exposure [ 22 , 23 , 24 ]. Wiener and median filters are widely used as conventional filters to reduce noise distribution in degraded images.…”

Section: Introductionmentioning

confidence: 99%

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

2021

Self Cite

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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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Feasibility Study for Application of Total-Variation-Based Noise-Removal Algorithm with 450-kVp High-Energy Industrial Computed-Tomography Imaging System for Non-destructive Testing

Lee

2018

Exp Tech

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Study on Feasibility for Artificial Intelligence (AI) Noise Reduction Algorithm with Various Parameters in Pediatric Abdominal Radio-Magnetic Computed Tomography (CT)

Cited by 7 publications

References 0 publications

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

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

Feasibility Study for Application of Total-Variation-Based Noise-Removal Algorithm with 450-kVp High-Energy Industrial Computed-Tomography Imaging System for Non-destructive Testing

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