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MRI 신호획득과 영상재구성에서의 인공지능 적용

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research communi...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748458/
https://www.ncbi.nlm.nih.gov/pubmed/36545429
http://dx.doi.org/10.3348/jksr.2022.0156
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collection PubMed
description Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.
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spelling pubmed-97484582022-12-20 MRI 신호획득과 영상재구성에서의 인공지능 적용 J Korean Soc Radiol Multi-Task Learning, Prediction Model, and Fast Imaging Algorithm Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction. The Korean Society of Radiology 2022-11 2022-11-30 /pmc/articles/PMC9748458/ /pubmed/36545429 http://dx.doi.org/10.3348/jksr.2022.0156 Text en Copyrights © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Multi-Task Learning, Prediction Model, and Fast Imaging Algorithm
MRI 신호획득과 영상재구성에서의 인공지능 적용
title MRI 신호획득과 영상재구성에서의 인공지능 적용
title_full MRI 신호획득과 영상재구성에서의 인공지능 적용
title_fullStr MRI 신호획득과 영상재구성에서의 인공지능 적용
title_full_unstemmed MRI 신호획득과 영상재구성에서의 인공지능 적용
title_short MRI 신호획득과 영상재구성에서의 인공지능 적용
title_sort mri 신호획득과 영상재구성에서의 인공지능 적용
topic Multi-Task Learning, Prediction Model, and Fast Imaging Algorithm
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748458/
https://www.ncbi.nlm.nih.gov/pubmed/36545429
http://dx.doi.org/10.3348/jksr.2022.0156
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