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Demirel OB, Zhang C, Yaman B, Gulle M, Shenoy C, Leiner T, Kellman P, Akcakaya M. High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083374 DOI: 10.1109/embc40787.2023.10340709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Real-time cine cardiac MRI provides an ECG-free free-breathing alternative to clinical gold-standard ECG-gated breath-hold segmented cine MRI for evaluation of heart function. Real-time cine MRI data acquisition during free breathing snapshot imaging enables imaging of patient cohorts that cannot be imaged with segmented or breath-hold acquisitions, but requires rapid imaging to achieve sufficient spatial-temporal resolutions. However, at high acceleration rates, conventional reconstruction techniques suffer from residual aliasing and temporal blurring, including advanced methods such as compressed sensing with radial trajectories. Recently, deep learning (DL) reconstruction has emerged as a powerful tool in MRI. However, its utility for free-breathing real-time cine MRI has been limited, as database-learning of spatio-temporal correlations with varying breathing and cardiac motion patterns across subjects has been challenging. Zero-shot self-supervised physics-guided deep learning (PG-DL) reconstruction has been proposed to overcome such challenges of database training by enabling subject-specific training. In this work, we adapt zero-shot PG-DL for real-time cine MRI with a spatio-temporal regularization. We compare our method to TGRAPPA, locally low-rank (LLR) regularized reconstruction and database-trained PG-DL reconstruction, both for retrospectively and prospectively accelerated datasets. Results on highly accelerated real-time Cartesian cine MRI show that the proposed method outperforms other reconstruction methods, both visibly in terms of noise and aliasing, and quantitatively.
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Demirel ÖB, Zhang C, Yaman B, Gulle M, Shenoy C, Leiner T, Kellman P, Akçakaya M. High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.13.528388. [PMID: 36824797 PMCID: PMC9948950 DOI: 10.1101/2023.02.13.528388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Real-time cine cardiac MRI provides an ECG-free free-breathing alternative to clinical gold-standard ECG-gated breath-hold segmented cine MRI for evaluation of heart function. Real-time cine MRI data acquisition during free breathing snapshot imaging enables imaging of patient cohorts that cannot be imaged with segmented or breath-hold acquisitions, but requires rapid imaging to achieve sufficient spatial-temporal resolutions. However, at high acceleration rates, conventional reconstruction techniques suffer from residual aliasing and temporal blurring, including advanced methods such as compressed sensing with radial trajectories. Recently, deep learning (DL) reconstruction has emerged as a powerful tool in MRI. However, its utility for free-breathing real-time cine MRI has been limited, as database-learning of spatio-temporal correlations with varying breathing and cardiac motion patterns across subjects has been challenging. Zero-shot self-supervised physics-guided deep learning (PG-DL) reconstruction has been proposed to overcome such challenges of database training by enabling subject-specific training. In this work, we adapt zero-shot PG-DL for real-time cine MRI with a spatio-temporal regularization. We compare our method to TGRAPPA, locally low-rank (LLR) regularized reconstruction and database-trained PG-DL reconstruction, both for retrospectively and prospectively accelerated datasets. Results on highly accelerated real-time Cartesian cine MRI show that the proposed method outperforms other reconstruction methods, both visibly in terms of noise and aliasing, and quantitatively.
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Affiliation(s)
- Ömer Burak Demirel
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Chi Zhang
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Burhaneddin Yaman
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Merve Gulle
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Chetan Shenoy
- Department of Medicine (Cardiology), University of Minnesota, Minneapolis, MN, USA
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Peter Kellman
- National Heart-Lung and Blood Institute, Bethesda, MD, USA
| | - Mehmet Akçakaya
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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The Role of Cardiac Computed Tomography in Heart Failure. Curr Heart Fail Rep 2022; 19:213-222. [PMID: 35588344 DOI: 10.1007/s11897-022-00553-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/25/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE OF REVIEW Cardiac computed tomography (CT) is becoming a more widely applied tool in the diagnosis and management of a variety of cardiovascular conditions, including heart failure. The aim of this narrative review is to examine the role of cardiac CT in patients with heart failure. RECENT FINDINGS Coronary computed tomographic angiography has robust diagnostic accuracy for ruling out coronary artery disease. These data are reflected in updated guidelines from major cardiology organizations. New roles for cardiac CT in myocardial imaging, perfusion scanning, and periprocedural planning, execution, and monitoring are being implemented. Cardiac CT is useful in ruling out coronary artery disease its diagnostic accuracy, accessibility, and safety. It is also intricately linked to invasive cardiac procedures that patients with heart failure routinely undergo.
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