AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI Article Swipe
Wen Shi
,
Haoan Xu
,
Cong Sun
,
Jiwei Sun
,
Yamin Li
,
Xinyi Xu
,
Tianshu Zheng
,
Yi Zhang
,
Guangbin Wang
,
Dan Wu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2022.3208277
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2022.3208277
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.
Related Topics
Concepts
Artificial intelligence
Computer vision
Computer science
Magnetic resonance imaging
Pipeline (software)
Iterative reconstruction
Image fusion
Motion (physics)
Rotation (mathematics)
Volume (thermodynamics)
Displacement (psychology)
Pattern recognition (psychology)
Physics
Image (mathematics)
Medicine
Radiology
Psychology
Psychotherapist
Quantum mechanics
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmi.2022.3208277
- OA Status
- green
- Cited By
- 22
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280530093
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4280530093Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tmi.2022.3208277Digital Object Identifier
- Title
-
AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRIWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-21Full publication date if available
- Authors
-
Wen Shi, Haoan Xu, Cong Sun, Jiwei Sun, Yamin Li, Xinyi Xu, Tianshu Zheng, Yi Zhang, Guangbin Wang, Dan WuList of authors in order
- Landing page
-
https://doi.org/10.1109/tmi.2022.3208277Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2205.05851Direct OA link when available
- Concepts
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Artificial intelligence, Computer vision, Computer science, Magnetic resonance imaging, Pipeline (software), Iterative reconstruction, Image fusion, Motion (physics), Rotation (mathematics), Volume (thermodynamics), Displacement (psychology), Pattern recognition (psychology), Physics, Image (mathematics), Medicine, Radiology, Psychology, Psychotherapist, Quantum mechanics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 10, 2023: 5, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.regardless | 122 |
| abstract_inverted_index.relatively | 55 |
| abstract_inverted_index.sequential | 81 |
| abstract_inverted_index.volumetric | 110 |
| abstract_inverted_index.Iteratively | 67 |
| abstract_inverted_index.Multi-slice | 0 |
| abstract_inverted_index.multi-slice | 74 |
| abstract_inverted_index.outperforms | 127 |
| abstract_inverted_index.reconstruct | 28 |
| abstract_inverted_index.Fusion-based | 64 |
| abstract_inverted_index.contaminated | 10 |
| abstract_inverted_index.conventional | 42, 181 |
| abstract_inverted_index.incorporated | 153 |
| abstract_inverted_index.insufficient | 52 |
| abstract_inverted_index.orientations | 125 |
| abstract_inverted_index.quantitative | 38 |
| abstract_inverted_index.registration | 108, 159 |
| abstract_inverted_index.displacement. | 150 |
| abstract_inverted_index.reconstructed | 96 |
| abstract_inverted_index.learning-based | 130 |
| abstract_inverted_index.reconstruction | 111, 191 |
| abstract_inverted_index.high-resolution | 29 |
| abstract_inverted_index.slice-to-volume | 107, 158 |
| abstract_inverted_index.motion-corrupted | 135 |
| abstract_inverted_index.multi-resolution | 157 |
| abstract_inverted_index.state-of-the-art | 129 |
| abstract_inverted_index.super-resolution | 190 |
| abstract_inverted_index.registration-based | 43 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.98007075 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |