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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Korean Society of Radiology
2025-05-01
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| Series: | Journal of the Korean Society of Radiology |
| Subjects: | |
| Online Access: | https://doi.org/10.3348/jksr.2025.0004 |
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| Summary: | 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. |
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| ISSN: | 2951-0805 |