ReviewPolish journal of radiology2024
Deep learning and AI in reducing magnetic resonance imaging scanning time: advantages and pitfalls in clinical practice.
Review in Polish journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
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Who cites it
18 citing papers in PubMed.
- Abdominal MRI-Derived Liver, Visceral Fat, and Pancreatic Phenotypes Improve Prediction of Incident Type 2 Diabetes.Diabetes/metabolism research and reviews · 2026Article
- Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.European radiology experimental · 2026Article
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
- Editorial: Innovative AI approaches in quantitative MRI: from image enhancement to biomarker estimation.Frontiers in radiology · 2026Article
- AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what's coming.International journal of computer assisted radiology and surgery · 2026Review
- Development of a multimodal vision transformer model for predicting traumatic versus degenerative rotator cuff tears on magnetic resonance imaging: A single-centre retrospective study.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- The Glymphatic-Venous Axis in Brain Clearance Failure: Aquaporin-4 Dysfunction, Biomarker Imaging, and Precision Therapeutic Frontiers.International journal of molecular sciences · 2025Review
- Article
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
- Response to the letter: "deep learning-driven knee MRI acceleration: evidence, enhancements, and the path to universal adoption".La Radiologia medica · 2025Article
- Deep learning-driven abbreviated knee MRI protocols: diagnostic accuracy in clinical practice.La Radiologia medica · 2025Article
- A quality improvement project to reduce magnetic resonance imaging sedation in children.Pediatric radiology · 2025Article
- Deep learning reconstruction for improving image quality of pediatric abdomen MRI using a 3D T1 fast spoiled gradient echo acquisition.Pediatric radiology · 2025Article
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?La Radiologia medica · 2025Review
- Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.Tomography (Ann Arbor, Mich.) · 2025Article
- Enhancing lymphoma staging: Unveiling the potential and challenges of whole-body magnetic resonance imaging.World journal of radiology · 2025Article
- Feasibility of artificial intelligence-assisted fast magnetic resonance imaging technology in the ankle joint injury: a comparison of the proton density-weighted image.Frontiers in radiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging (MRI) is a powerful imaging modality, but one of its drawbacks is its relatively long scanning time to acquire high-resolution images. Reducing the scanning time has become a critical area of focus in MRI, aiming to enhance patient comfort, reduce motion artifacts, and increase MRI throughput. In the past 5 years, artificial intelligence (AI)-based algorithms, particularly deep learning models, have been developed to reconstruct high-resolution images from significantly fewer data points. These new techniques significantly enhance MRI efficiency, improve patient comfort and lower patient motion artifacts. Improving MRI throughput with lower scanning duration increases accessibility, potentially reducing the need for additional MRI machines and associated costs. Several fields can benefit from shortened protocols, especially for routine exams. In oncologic imaging, faster MRI scans can facilitate more regular monitoring of cancer patients. In patients suffering from neurological disorders, rapid brain imaging can aid in the quick assessment of conditions like stroke, multiple sclerosis, and epilepsy, improving patient outcomes. In chronic inflammatory disease, faster imaging may help in reducing the interval between imaging to better check therapy outcomes. Additionally, reducing scanning time could effectively help MRI to play a role in emergency medicine and acute conditions such as trauma or acute ischaemic stroke. The purpose of this paper is to describe and discuss the advantages and disadvantages of introducing deep learning reconstruction techniques to reduce MRI scanning times in clinical practice.
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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.