ArticleNature communications2023
A deep unrolled neural network for real-time MRI-guided brain intervention.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- A multi-modal foundation model for brain disease diagnosis and medical imaging.Patterns (New York, N.Y.) · 2026Article
- Nanoparticle Detection in Biology and Medicine: A Review.Biosensors · 2025Review
- Real-Time Stereotactic MRI-Guided Sclerotherapy with Bleomycin-Polidocanol Foam: Illuminating Inaccessible Venous Malformations.Journal of clinical medicine · 2025Article
- A bibliometric analysis of Cushing's disease and magnetic resonance imaging: history, research hotspots, and emerging trends.Quantitative imaging in medicine and surgery · 2025Article
- Hybrid pneumatic-hydraulic actuation for MRI-guided robotic stereotactic neurointervention.Science advances · 2025Article
- Fully automated on-scanner aortic four-dimensional flow magnetic resonance imaging processing and hemodynamic analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Neural Influences on Tumor Progression Within the Central Nervous System.CNS neuroscience & therapeutics · 2024Review
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- A deep unrolled neural network for real-time MRI-guided brain intervention.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm
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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.