Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network Article Swipe
Siyeop Yoon
,
Shiro Nakamori
,
Amine Amyar
,
Salah Assana
,
Julia Cirillo
,
Manuel A. Morales
,
Kelvin Chow
,
Xiaoming Bi
,
Patrick Pierce
,
Beth Goddu
,
Jennifer Rodriguez
,
Long Ngo
,
Warren J. Manning
,
Reza Nezafat
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1148/radiol.222878
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1148/radiol.222878
Background Cardiac cine can benefit from deep learning-based image reconstruction to reduce scan time and/or increase spatial and temporal resolution. Purpose To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS). Materials and Methods The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network, trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 to September 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cine images collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS. The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected. Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-rank tests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis. Results The deep learning model was trained on 1616 patients (mean age ± SD, 56 years ± 16; 920 men) and evaluated in 181 individuals, 126 patients (mean age, 57 years ± 16; 77 men) and 55 healthy subjects (mean age, 27 years ± 10; 15 men). In breath-hold ECG-gated segmented cine and free-breathing real-time cine, the deep learning model and GRAPPA showed similar diagnostic quality scores (2.9 vs 2.9, P = .41, deep learning vs GRAPPA) and artifact score (4.4 vs 4.3, P = .55, deep learning vs GRAPPA). Deep learning acquired more sections per breath-hold than GRAPPA (3.1 vs one section, P < .001). In free-breathing real-time cine, the deep learning showed a similar diagnostic quality score (2.9 vs 2.9, P = .21, deep learning vs GRAPPA) and lower artifact score (3.9 vs 4.3, P < .001, deep learning vs GRAPPA). For both sequences, the deep learning model showed excellent agreement for LV parameters, with near-zero mean differences and narrow limits of agreement compared with GRAPPA. Conclusion Deep learning-accelerated cardiac cine showed similarly accurate quantification of cardiac function, volume, and strain to a standardized parallel imaging method. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1148/radiol.222878
- OA Status
- green
- Cited By
- 30
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378746360
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378746360Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1148/radiol.222878Digital Object Identifier
- Title
-
Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-30Full publication date if available
- Authors
-
Siyeop Yoon, Shiro Nakamori, Amine Amyar, Salah Assana, Julia Cirillo, Manuel A. Morales, Kelvin Chow, Xiaoming Bi, Patrick Pierce, Beth Goddu, Jennifer Rodriguez, Long Ngo, Warren J. Manning, Reza NezafatList of authors in order
- Landing page
-
https://doi.org/10.1148/radiol.222878Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10315558/pdf/radiol.222878.pdfDirect OA link when available
- Concepts
-
Medicine, Deep learning, Wilcoxon signed-rank test, Artificial intelligence, Artifact (error), Image quality, Magnetic resonance imaging, Ventricle, Nuclear medicine, Radiology, Cardiology, Internal medicine, Computer science, Image (mathematics), Mann–Whitney U testTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 14, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.retrospectively | 62 |
| abstract_inverted_index.super-resolution | 52 |
| abstract_inverted_index.GRAPPA-accelerated | 118 |
| abstract_inverted_index.electrocardiography | 84 |
| abstract_inverted_index.learning-accelerated | 329 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile.value | 0.9804321 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |