Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application
In liver and pancreatobiliary MRI, mitigating respiratory motion-related artifacts has always been a major challenge in image acquisition. Motion reduction by breathing control schemes or scan time acceleration by k-space undersampling are two accessible approaches in clinical imaging. Parallel i...
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| Format: | Article |
| Language: | English |
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The Korean Society of Radiology
2025-05-01
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| Series: | Journal of the Korean Society of Radiology |
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| Online Access: | https://doi.org/10.3348/jksr.2025.0004 |
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| author | Bohyun Kim So Hyun Park Moon Hyung Choi |
| author_facet | Bohyun Kim So Hyun Park Moon Hyung Choi |
| author_sort | Bohyun Kim |
| collection | DOAJ |
| description | In liver and pancreatobiliary MRI, mitigating respiratory motion-related artifacts has always
been a major challenge in image acquisition. Motion reduction by breathing control
schemes or scan time acceleration by k-space undersampling are two accessible approaches
in clinical imaging. Parallel imaging is an indispensable everyday technique with well-known
characteristics, but with drawbacks that limit acceleration factors to ≤4. Compressed sensing
exploits the data sparsity of MR images, and pseudorandomly undersamples k-space
data to iteratively reconstruct images using sophisticated complex computations within
highly accelerated scanning time. Albeit, this is with long reconstruction time and complexity
in parameter optimization. Deep learning reconstruction uses pretrained and validated
convolutional neural networks to reconstruct undersampled data, with the main tasks being
image acceleration, denoising, and superresolution. While promising, deep learning reconstruction
requires further testing and practical experience with model stability, generalizability,
and output image fidelity. |
| format | Article |
| id | doaj-art-e6010c8805d64c949b61784c2dc2b9e6 |
| institution | OA Journals |
| issn | 2951-0805 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | The Korean Society of Radiology |
| record_format | Article |
| series | Journal of the Korean Society of Radiology |
| spelling | doaj-art-e6010c8805d64c949b61784c2dc2b9e62025-08-20T02:02:44ZengThe Korean Society of RadiologyJournal of the Korean Society of Radiology2951-08052025-05-01863307320https://doi.org/10.3348/jksr.2025.0004Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and ApplicationBohyun KimSo Hyun ParkMoon Hyung ChoiIn liver and pancreatobiliary MRI, mitigating respiratory motion-related artifacts has always been a major challenge in image acquisition. Motion reduction by breathing control schemes or scan time acceleration by k-space undersampling are two accessible approaches in clinical imaging. Parallel imaging is an indispensable everyday technique with well-known characteristics, but with drawbacks that limit acceleration factors to ≤4. Compressed sensing exploits the data sparsity of MR images, and pseudorandomly undersamples k-space data to iteratively reconstruct images using sophisticated complex computations within highly accelerated scanning time. Albeit, this is with long reconstruction time and complexity in parameter optimization. Deep learning reconstruction uses pretrained and validated convolutional neural networks to reconstruct undersampled data, with the main tasks being image acceleration, denoising, and superresolution. While promising, deep learning reconstruction requires further testing and practical experience with model stability, generalizability, and output image fidelity.https://doi.org/10.3348/jksr.2025.0004liver mriimage accelerationundersamplingparallel imagingcompressed sensingdeep learning reconstruction |
| spellingShingle | Bohyun Kim So Hyun Park Moon Hyung Choi Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application Journal of the Korean Society of Radiology liver mri image acceleration undersampling parallel imaging compressed sensing deep learning reconstruction |
| title | Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application |
| title_full | Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application |
| title_fullStr | Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application |
| title_full_unstemmed | Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application |
| title_short | Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application |
| title_sort | fast mri techniques of the liver and pancreaticobiliary tract overview and application |
| topic | liver mri image acceleration undersampling parallel imaging compressed sensing deep learning reconstruction |
| url | https://doi.org/10.3348/jksr.2025.0004 |
| work_keys_str_mv | AT bohyunkim fastmritechniquesoftheliverandpancreaticobiliarytractoverviewandapplication AT sohyunpark fastmritechniquesoftheliverandpancreaticobiliarytractoverviewandapplication AT moonhyungchoi fastmritechniquesoftheliverandpancreaticobiliarytractoverviewandapplication |