Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence
ABSTRACT: Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to...
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Elsevier
2025-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664724011542 |
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| author | Omer Burak Demirel Fahime Ghanbari Christopher W. Hoeger Connie W. Tsao Adele Carty Long H. Ngo Patrick Pierce Scott Johnson Kathryn Arcand Jordan Street Jennifer Rodriguez Tess E. Wallace Kelvin Chow Warren J. Manning Reza Nezafat |
| author_facet | Omer Burak Demirel Fahime Ghanbari Christopher W. Hoeger Connie W. Tsao Adele Carty Long H. Ngo Patrick Pierce Scott Johnson Kathryn Arcand Jordan Street Jennifer Rodriguez Tess E. Wallace Kelvin Chow Warren J. Manning Reza Nezafat |
| author_sort | Omer Burak Demirel |
| collection | DOAJ |
| description | ABSTRACT: Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction. Methods: A generative AI-based image enhancement was used to improve the sharpness of 2D LGE images acquired with low spatial resolution in the phase-encode direction. The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. The model was implemented inline, allowing the reconstruction of images on the scanner console. We prospectively enrolled 100 patients (55 ± 14 years, 72 males) referred for clinical CMR at 3T. We collected three sets of LGE images in each subject, with in-plane spatial resolutions of 1.5 × 1.5-3-6 mm2. The generative AI model enhanced in-plane resolution to 1.5 × 1.5 mm2 from the low-resolution counterparts. Images were compared using a blur metric, quantifying the perceived image sharpness (0 = sharpest, 1 = blurriest). LGE image sharpness (using a 5-point scale) was assessed by three independent readers. Results: The scan times for the three imaging sets were 15 ± 3, 9 ± 2, and 6 ± 1 s, with inline generative AI-based images reconstructed time of ∼37 ms. The generative AI-based model improved visual image sharpness, resulting in lower blur metric compared to low-resolution counterparts (AI-enhanced from 1.5 × 3 mm2 resolution: 0.3 ± 0.03 vs 0.35 ± 0.03, P < 0.01). Meanwhile, AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images showed similar blur metric (0.30 ± 0.03 vs 0.31 ± 0.03, P = 1.0) Additionally, there was an overall 18% improvement in image sharpness between AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images in the subjective blurriness score (P < 0.01). Conclusion: The generative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis. |
| format | Article |
| id | doaj-art-76931e61a3dd467891557df3eaefeea3 |
| institution | DOAJ |
| issn | 1097-6647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-76931e61a3dd467891557df3eaefeea32025-08-20T03:10:20ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472025-01-0127110112710.1016/j.jocmr.2024.101127Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligenceOmer Burak Demirel0Fahime Ghanbari1Christopher W. Hoeger2Connie W. Tsao3Adele Carty4Long H. Ngo5Patrick Pierce6Scott Johnson7Kathryn Arcand8Jordan Street9Jennifer Rodriguez10Tess E. Wallace11Kelvin Chow12Warren J. Manning13Reza Nezafat14Department of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USA; Siemens Medical Solutions USA, Inc., Boston, Massachusetts, USACardiovascular MR R&D, Siemens Healthcare Ltd., Calgary, Alberta, CanadaDepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division) and Harvard Medical School, Boston, Massachusetts, USA; Corresponding author.ABSTRACT: Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction. Methods: A generative AI-based image enhancement was used to improve the sharpness of 2D LGE images acquired with low spatial resolution in the phase-encode direction. The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. The model was implemented inline, allowing the reconstruction of images on the scanner console. We prospectively enrolled 100 patients (55 ± 14 years, 72 males) referred for clinical CMR at 3T. We collected three sets of LGE images in each subject, with in-plane spatial resolutions of 1.5 × 1.5-3-6 mm2. The generative AI model enhanced in-plane resolution to 1.5 × 1.5 mm2 from the low-resolution counterparts. Images were compared using a blur metric, quantifying the perceived image sharpness (0 = sharpest, 1 = blurriest). LGE image sharpness (using a 5-point scale) was assessed by three independent readers. Results: The scan times for the three imaging sets were 15 ± 3, 9 ± 2, and 6 ± 1 s, with inline generative AI-based images reconstructed time of ∼37 ms. The generative AI-based model improved visual image sharpness, resulting in lower blur metric compared to low-resolution counterparts (AI-enhanced from 1.5 × 3 mm2 resolution: 0.3 ± 0.03 vs 0.35 ± 0.03, P < 0.01). Meanwhile, AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images showed similar blur metric (0.30 ± 0.03 vs 0.31 ± 0.03, P = 1.0) Additionally, there was an overall 18% improvement in image sharpness between AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images in the subjective blurriness score (P < 0.01). Conclusion: The generative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis.http://www.sciencedirect.com/science/article/pii/S1097664724011542Late gadolinium enhancementHighly acceleratedDeep learning |
| spellingShingle | Omer Burak Demirel Fahime Ghanbari Christopher W. Hoeger Connie W. Tsao Adele Carty Long H. Ngo Patrick Pierce Scott Johnson Kathryn Arcand Jordan Street Jennifer Rodriguez Tess E. Wallace Kelvin Chow Warren J. Manning Reza Nezafat Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence Journal of Cardiovascular Magnetic Resonance Late gadolinium enhancement Highly accelerated Deep learning |
| title | Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence |
| title_full | Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence |
| title_fullStr | Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence |
| title_full_unstemmed | Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence |
| title_short | Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence |
| title_sort | late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence |
| topic | Late gadolinium enhancement Highly accelerated Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1097664724011542 |
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