ArticleScientific reports2020
CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
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Who cites it
89 citing papers in PubMed, 1 synthesis or guideline pooled it, 231 citations in OpenAlex.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- 3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.Magnetic resonance in medicine · 2026Article
- Fully 3D Unrolled Magnetic Resonance Fingerprinting Reconstruction via Staged Pretraining and Implicit Gridding.Magnetic resonance in medicine · 2026Article
- End-to-End Trained Energy-Based Model for Image Recovery.Journal of imaging · 2026Article
- EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection.IEEE transactions on medical imaging · 2026Article
- Aleatoric uncertainty in accelerated prostate MRI reconstruction: echo-train dropout versus Gaussian noise Monte Carlo sampling.Magma (New York, N.Y.) · 2026Article
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.Magnetic resonance in medicine · 2026Article
- Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.Magnetic resonance in medicine · 2026Article
- Real-time 4D MRI reconstruction using DVR-NeMF, a framework for dynamic volumetric reconstruction.Cell reports methods · 2025Article
- Semi-quantitative Analysis of Right Ventricular Myocardial First-pass Perfusion Using Cardiac Magnetic Resonance Imaging in Systemic Sclerosis and Pulmonary Arterial Hypertension.Academic radiology · 2025Article
- Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients.World journal of cardiology · 2025Article
- High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.Physics in medicine and biology · 2025Article
- Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.European radiology · 2025Article
- MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2025Article
- Computationally Efficient Implicit Training Strategy for Unrolled Networks (IMUNNE): A Preliminary Analysis Using Accelerated Real-Time Cardiac Cine MRI.IEEE transactions on bio-medical engineering · 2025Article
- Cardiac digital twins at scale from MRI: Open tools and representative models from ~ 55000 UK Biobank participants.PloS one · 2025Article
- Measuring biventricular function and left atrial volume in a single five-dimensional whole-heart cardiovascular magnetic resonance scan at 0.55T.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- Clinical feasibility of two cardiac deep learning cine magnetic resonance imaging sequences: Single-breath-hold and free-breathing motion-corrected approaches.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
29 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited to anisotropic resolution and long reconstruction times. Recently deep learning has shown promising results for computationally efficient reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging. CINENet is based on (3 + 1)D complex-valued spatio-temporal convolutions and multi-coil data processing. We trained and evaluated the proposed CINENet on in-house acquired 3D CINE data of 20 healthy subjects and 15 patients with suspected cardiovascular disease. The proposed CINENet network outperforms iterative reconstructions in visual image quality and contrast (+ 67% improvement). We found good agreement in LV function (bias ± 95% confidence) in terms of end-systolic volume (0 ± 3.3 ml), end-diastolic volume (- 0.4 ± 2.0 ml) and ejection fraction (0.1 ± 3.2%) compared to clinical gold-standard 2D CINE, enabling single breath-hold isotropic 3D CINE in less than 10 s scan and ~ 5 s reconstruction time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.