ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2021
Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 34 citations in OpenAlex.
- 3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.Magnetic resonance in medicine · 2026Article
- FlowVN Trained on a Single Dataset Enables Rapid Reconstruction of Highly Accelerated 4D Flow MRI Across Multiple Sites.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
- 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
- Artificial intelligence in four-dimensional imaging for motion management in radiation therapy.Artificial intelligence review · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- 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
- High-angular resolution diffusion imaging generation using 3d u-net.Neuroradiology · 2024Article
- Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI.Magnetic resonance in medicine · 2024Article
- Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system.Physics in medicine and biology · 2023Article
- Distortion-corrected image reconstruction with deep learning on an MRI-Linac.Magnetic resonance in medicine · 2023Article
- Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency.Magnetic resonance in medicine · 2023Article
- Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2023Article
- Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and RMagnetic resonance in medicine · 2023Article
- GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging.NMR in biomedicine · 2023Article
- The future of MRI in radiation therapy: Challenges and opportunities for the MR community.Magnetic resonance in medicine · 2022Review
- Integrated MRI-guided radiotherapy - opportunities and challenges.Nature reviews. Clinical oncology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 4 countries.
Funding
Abstract
background and purpose4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times.
methodsTwo 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study.
resultsFor 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm
conclusionOur results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.
Indexed as
Identifiers
What OpenQuestion holds
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.