ArticleMagnetic resonance imaging2022
Simulated diagnostic performance of low-field MRI: Harnessing open-access datasets to evaluate novel devices.
Article in Magnetic resonance imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed, 19 citations in OpenAlex.
- Ultra-low-field MRI for bedside imaging of severe multiple sclerosis.Journal of neurology · 2026Article
- First-Order Spatial Encoding Simulations for Improved Accuracy in the Presence of Strong BMagnetic resonance in medicine · 2026Article
- In-silico comparison of a diffusion model with conventionally trained deep networks for translating 64mT to 3T brain FLAIR.Scientific reports · 2025Article
- Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis.Insights into imaging · 2024Review
- Applications, limitations and advancements of ultra-low-field magnetic resonance imaging: A scoping review.Surgical neurology international · 2024Article
- Low-field MRI: A report on the 2022 ISMRM workshop.Magnetic resonance in medicine · 2023Review
- An integrated target field framework for point-of-care halbach array low-field MRI system design.Magma (New York, N.Y.) · 2023Article
- Benchmarking the performance of a low-cost magnetic resonance control system at multiple sites in the open MaRCoS community.NMR in biomedicine · 2023Article
- Low-field MRI: Clinical promise and challenges.Journal of magnetic resonance imaging : JMRI · 2023Review
- Generating High-Resolution CT Slices from Two Image Series Using Deep-Learning-Based Resolution Enhancement Methods.Diagnostics (Basel, Switzerland) · 2022Article
- Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions.NeuroImage. Clinical · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The purpose of this study is to demonstrate a method for virtually evaluating novel imaging devices using machine learning and open-access datasets, here applied to a new, low-field strength portable 64mT MRI device. Paired 3 T and 64mT brain images were used to develop and validate a transformation converting standard clinical images to low-field quality images. Separately, 3 T images were aggregated from open-source databases spanning four neuropathologies: low-grade glioma (LGG, N = 76), high-grade glioma (HGG, N = 259), stroke (N = 28), and multiple sclerosis (MS, N = 20). The transformation method was then applied to the open-source data to generate simulated low-field images for each pathology. Convolutional neural networks (DenseNet-121) were trained to detect pathology in axial slices from either 3 T or simulated 64 mT images, and their relative performance was compared to characterize the potential diagnostic capabilities of low-field imaging. Algorithm performance was measured using area under the receiver operating characteristic curve. Across all cohorts, pathology detection was similar between 3 T and simulated 64mT images (LGG: 0.97 vs. 0.98; HGG: 0.96 vs. 0.95; stroke: 0.94 vs. 0.94; MS: 0.90 vs 0.87). Pathology detection was further characterized as a function of lesion size, intensity, and contrast. Simulated images showed decreasing sensitivity for lesions smaller than 4 cm
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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.