ArticleMagnetic resonance in medicine2024
Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI.
Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- 3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.Magnetic resonance in medicine · 2026Article
- Review
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Volumetric thermometry in moving tissues using stack-of-radial MRI and an image-navigated multi-baseline proton resonance frequency shift method.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
- Summary from the 2025 International Society for Magnetic Resonance in Medicine workshop on body MRI: Unsolved problems and unmet needs.Magnetic resonance in medicine · 2026Review
- A dynamic reconstruction and motion estimation framework for cardiorespiratory motion-resolved real-time volumetric MR imaging (DREME-MR).Physics in medicine and biology · 2025Article
- Real-Time Dose-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Review
- Enabling in vivo comparisons of different four-dimensional magnetic resonance imaging sequences for radiotherapy guidance using visual biofeedback.Physics and imaging in radiation oncology · 2025Article
- High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.Physics in medicine and biology · 2025Article
- Artificial intelligence in four-dimensional imaging for motion management in radiation therapy.Artificial intelligence review · 2025Article
- Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.Magnetic resonance imaging · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac.Magnetic resonance in medicine · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- A systematic review of 4D magnetic resonance imaging techniques for abdominal radiotherapy treatment planning.Physics and imaging in radiation oncology · 2024Review
- Paired conditional generative adversarial network for highly accelerated liver 4D MRI.Physics in medicine and biology · 2024Article
- Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system.Physics in medicine and biology · 2023Article
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7 authors.
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Abstract
purposeTo develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI.
methodsMovienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers.
resultsThe reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts.
conclusionMovienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.
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