Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation Article Swipe
Itzik Malkiel
,
Sangtae Ahn
,
Valentina Taviani
,
Anne Menini
,
Lior Wolf
,
Christopher J. Hardy
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/iccp51581.2021.9466257
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/iccp51581.2021.9466257
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing (AGB) technique that automates the process of combining the adversarial and pixel-wise terms and streamlines hyperparameter tuning. In addition, we introduce a Densely Connected Iterative Network, which is an undersampled MRI reconstruction network that utilizes dense connections. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques. To demonstrate the general nature of our method, it is further evaluated on a battery of image-to-image translation experiments, demonstrating an ability to recover from sub-optimal weighting in multi-term adversarial training.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/iccp51581.2021.9466257
- OA Status
- green
- References
- 39
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3151138472
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3151138472Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/iccp51581.2021.9466257Digital Object Identifier
- Title
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Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image TranslationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-23Full publication date if available
- Authors
-
Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. HardyList of authors in order
- Landing page
-
https://doi.org/10.1109/iccp51581.2021.9466257Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2104.01889Direct OA link when available
- Concepts
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Computer science, Image translation, Iterative reconstruction, Artificial intelligence, Translation (biology), Hyperparameter, Computer vision, Image (mathematics), Artificial neural network, Image quality, Weighting, Algorithm, Chemistry, Biochemistry, Medicine, Gene, Messenger RNA, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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39Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| best_oa_location.pdf_url | https://arxiv.org/pdf/2104.01889 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2104.01889 |
| primary_location.id | doi:10.1109/iccp51581.2021.9466257 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2021 IEEE International Conference on Computational Photography (ICCP) |
| primary_location.landing_page_url | https://doi.org/10.1109/iccp51581.2021.9466257 |
| publication_date | 2021-05-23 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2809834193, https://openalex.org/W2133665775, https://openalex.org/W2339754110, https://openalex.org/W6765779288, https://openalex.org/W189587150, https://openalex.org/W2340897893, https://openalex.org/W2778924750, https://openalex.org/W2806475032, https://openalex.org/W2963446712, https://openalex.org/W2963073614, https://openalex.org/W2963800363, https://openalex.org/W2101675075, https://openalex.org/W6741832134, https://openalex.org/W2111388536, https://openalex.org/W2594014149, https://openalex.org/W2604388535, https://openalex.org/W6763645550, https://openalex.org/W2611467245, https://openalex.org/W4249760698, https://openalex.org/W2883105305, https://openalex.org/W4233764193, https://openalex.org/W2883939028, https://openalex.org/W2593414223, https://openalex.org/W3034514764, https://openalex.org/W2911290743, https://openalex.org/W2963981733, https://openalex.org/W2962879692, https://openalex.org/W2620324432, https://openalex.org/W2963873275, https://openalex.org/W2167233877, https://openalex.org/W2168887049, https://openalex.org/W2775288145, https://openalex.org/W2125389028, https://openalex.org/W2739748921, https://openalex.org/W1921523184, https://openalex.org/W2949443434, https://openalex.org/W2605195953, https://openalex.org/W2619122209, https://openalex.org/W3129544500 |
| referenced_works_count | 39 |
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| abstract_inverted_index.battery | 136 |
| abstract_inverted_index.enhance | 48 |
| abstract_inverted_index.further | 132 |
| abstract_inverted_index.general | 125 |
| abstract_inverted_index.k-space | 19 |
| abstract_inverted_index.method, | 129 |
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| abstract_inverted_index.sharper | 117 |
| abstract_inverted_index.tuning. | 83 |
| abstract_inverted_index.Adaptive | 64 |
| abstract_inverted_index.Gradient | 65 |
| abstract_inverted_index.However, | 26 |
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| abstract_inverted_index.hyperparameter | 82 |
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| abstract_inverted_index.reconstruction | 3, 98, 114 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.03236402 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |